466. Process-Focused Teaching

Given the widespread availability of generative AI tools, traditional assessments of learning conducted in an unproctored environment have become unreliable indicators of student learning. In this episode, Jason Gulya joins us to discuss the potential benefits of shifting our focus to the process of learning.

Jason is Professor of English and Media Communications at Berkeley College, where he also chairs the AI and Academic Integrity Committee. He is a co-author of Artificial Intelligence, Real Literacy: a practical guide to using AI for evidence-based literacy practices in education, and is currently writing a book on process-focused teaching in the age of AI, for Oklahoma University Press.

Show Notes

  • Matthews, Paul; Gulya, Jason (2025). Artificial Intelligence, Real Literacy: A Practical Guide To Using AI For 10 Evidence-Based Literacy Practices in Education. Independently published.
  • Jeanne Beatrix Law
  • Perkins, Furze, Roe and MacVaugh (2024). The AI Assesment Scale
  • PAIRR

Transcript

John: Given the widespread availability of generative AI tools, traditional assessments of learning conducted in an unproctored environment have become unreliable indicators of student learning. In this episode, we explore the potential benefits of shifting our focus to the process of learning.

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John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

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John: Our guest today is Jason Gulya. Jason is Professor of English and Media Communications at Berkeley College, where he also chairs the AI and Academic Integrity Committee. He is a co-author of Artificial Intelligence, Real Literacy: a practical guide to using AI for evidence-based literacy practices in education, and is currently writing a book on process-focused teaching in the age of AI, for Oklahoma University Press. Welcome, Jason.

Jason: Thank you so much. It’s such a pleasure to be here and talk to you both.

Rebecca: Today’s teas are:… Jason. Are you drinking some tea with us today?

Jason: I’m not. I usually drink coffee over tea. I have my own preferences when I do drink tea, but right now I don’t have any. But if I were drinking tea, it would either be English breakfast or Earl Grey.

Rebecca: All right, classics. You’re not a complete tea denier. [LAUGHTER] How about you, John?

John: And I am drinking Prince of Wales tea today.

Rebecca: I’m drinking English breakfast today.

Jason: In some ways, I prefer the taste of tea. I don’t know why I don’t drink it more. I probably should.

Rebecca: I wasn’t trying to make you feel guilty, Jason.

John: Yes, she was. .[LAUGHTER] We’ve invited you here today to discuss process-focused teaching, which is the subject of your new book. Can you tell us a little bit about the origin story of this book project?

Jason: Yeah, it really, in some ways, started way before generative AI. I think for a lot of us, generative AI was a push to really think about process over product. I’m more than happy to talk about what that is. But really, a long time ago, I felt like just looking at a final product, which typically… I’m a writing professor, so it’s typically a paper in front of me… I felt like it wasn’t really giving me enough insight into how a student was thinking, how they were thinking about their own thinking, how they were learning, what they were learning, and so a long time ago, I felt very disillusioned just looking at a final product. And I did a lot of the process-focused teaching that in writing we do, multiple drafts and things like that, but I started to get a sense, and this really hit a head probably three or four years ago, right around the time ChatGPT hit everyone. I started to think about how to move past that disillusionment and another way of teaching, and trying to get more of an access into what my students’ processes were like, because beyond a rough draft and a final draft, I didn’t really know. And so when ChatGPT came out for many of us, it put a lot of pressure on us. And I started to redesign. That’s where the book really starts, trying to think about what that push meant to me, and trying to figure out giving students agency over the process. One of the things that I think a lot about is that there are so many different kinds of processes, and now especially that process-focused teaching has kind of been picked up by so many people who’ve tried to redesign their assessments for an age of AI or whatever you want to call it. But as I dive into it more, there’s a lot to say about processes that I create versus ones that my students create versus ones that we co-create, and so when I started to do the research for this book and talking about generative AI and what it meant for college education, I just did more and more of a deep dive, and as is always the case, it just became more and more nuanced, more and more complicated as I went along, and that became sort of invigorating because it forced me to really start to flesh out what process actually is, when I think that many times we lose touch with just how much of a black box that term is, even though everyone uses it and it’s become very much a term for the moment.

Rebecca: I heard you say that generative AI was a little bit of a catalyst to kind of move you further in this direction, in this moment, but a lot of folks have been doing some of this work over time. So you mentioned a little bit of digging deep into this literature. Can you talk a little bit about some context prior to generative AI [LAUGHTER] that you might have been looking at?

Jason: This is something that I think is really important because if you look at the broader conversation about generative AI in education, it almost makes it seem like this is a new thing. But there’s this really long history going back 60, 70, years that has looked at teaching process, and so a lot of this comes out of writing studies. Like there are individuals who really, in the 1960s, early 1970s, already started to think about how to teach process, what that looks like. There’s all these different pieces of information, and there’s also a lot of research, even starting late-70s, early-80s, into something like think-aloud protocols. Now, this is not only in English; it’s big in sociology and other fields as well. But getting someone to talk about something as they do it, it’s kind of difficult to do. It takes a little bit of practice, but think-aloud protocols in the 1980s, people really looked at when a student is writing something, and they were forced to talk about what they were writing, what they were thinking as they did it, what do you learn? And we’re actually starting to see Jeanne Beatrix Law at Kennesaw State University also has done some research in the AI version of this. I think one of the best things we can do in terms of invigorating this conversation about AI and process-focused teaching is to go back to that. Look at how process was talked about in writing studies and sociology and in other fields, because there is this really, really long research into process in the abstract, that’s one kind of research, and then also like specific strategies. This is think-aloud protocols. This is revision practices, revision literacy, like all this is linked, and there’s this really, really long history of it. And I feel like the conversation about AI in education, especially higher education, tends to jump over that. It’s such a shame because I think it’s really, really rife with a lot of ideas. That no, we can’t transfer all of them, but I think it’s surprising how much you can actually use, because I’m a firm believer that generative AI broke some things, but more often than not, it showed us things that were broken for a while, maybe we shouldn’t have been as product-focused as many of us were in our disciplines, and we can go back, look at that literature, and kind of reinvigorate it and adapt it if we need to.

John: So while there have been many people who’ve been exploring and in many disciplines exploring process-focused teaching, it’s still not the norm, although there’s certainly been a lot of push for people to move in that direction in response to generative AI. When only the final product is evaluated, you can see where students ended up, but it’s hard to detect how much they’ve learned and their process for learning, and help them develop better learning skills. What aspects of learning become more visible when you’re engaged in process-focused teaching?

Jason: Yeah, I think that one of the big benefits is that teaching process is a way to make thinking visible. One of the things that’s happened, especially now, when we’re very product focused, we can look at something that looks like it has the skills in it. It used to be 5 to 10 years ago, I could read a paper from a student, and I could see well, there was a good argument. I’m pretty sure they could argue, or they use evidence and analyze quotes. Well, I’m pretty sure they can analyze quotes. And then one of the things that’s happened with ChatGPT, Claude, Gemini, all these programs that have just been given to students and thrown in institutions, is that it’s getting harder and harder to bridge that gap. So, the idea behind process-focused teaching is it gives you a way to bridge it, to kind of figure out what’s happening with that gap. What are they actually working through? And one of the things that I’ve learned over the last few years is, as I’ve done a deep dive into process-focused teaching, there are so many different ways of doing it, and I think we need to make distinctions. If you’re talking about one form of process-focused teaching, which is giving students an interactive oral assessment, so that students come in, they have a product, something they’ve created, and then in class you can do this online too. It’s a little bit harder, it takes some creativity, but you can do it. So the whole idea is, it puts a little bit of pressure on the students, so they need to explain things, and so that’s where process comes in, because it’s something they couldn’t fully rehearse for, and it forces them to pivot. And so here you’re testing something pretty specific. You’re trying to get a sense of whether the information stuck, whether they can pivot, they can move because they can’t fully plan for a conversation, and so that’s what you’re really taking a look at. Whereas other forms, it can be very different. So if you ask a student to, for example, do a think-aloud protocol, and I’ve done this with my students at Berkeley College, I’ll ask them: for this week what I want you to do, keep writing, keep going forward with your project. But I want you to take five minutes. I want to take five minutes, and as you are writing, I want you to talk about it. And so when I look at that, what I’m really looking for is how they’re processing things as they write. So this also focuses on process, but it’s a very, very different side of it. And then another way to get a process is to really allow students to create their own process. So this we can do, and I often do in a fully asynchronous learning environment. That instead of telling a student: write a five-page paper about blank, instead of doing that, I have them, if I’m able to, if I have the leeway, to design their own project, and we do it together. Some of them will say, “I know what I want to do.” Some will say, “I don’t,” and I work with them. They design a project. They design a process. So you want to create blank? What steps are going to get you there, do you think? Do you think generative AI can help with that? Why or why not? Do you think they’re going to help with the outline? Do you think they’re going to help with reformatting?… whatever it is. And some students will just say, “No, I don’t want to use generative AI. I’m just going to go through my process.” And others will say, “I think it’s going to help here and not here.” And that’s a part of the conversation. So for that, again, it’s process-focused teaching, but the skill set is a little bit different. It’s about design thinking. It’s about can a student create their own process and manage their own process, and sometimes even evaluate their own process. Halfway through, I might say, “How’s it going? Do you need to pivot? What do you need to do?” And those are three examples of process-focused teaching, but sort of splinter in terms of how they’re approaching process. And I think one of the things that’s happened in the conversation about AI in education is that process-focused teaching has become one umbrella term that’s trying to capture all these different things, and it’s just really hard to do. And I don’t think we should have to do that. It’s very, very nuanced. So those different strategies, they focus on different skills, even though they all focus on process.

Rebecca: I come from a field that focuses on process a lot, the design field, so we’ve, over time, required students to do things like process books, or we might do a case study at the end where they talk about how they came to their final product, which is similar to your oral exam, but maybe is in a written format or has audiovisual components to that. It makes me think a little bit more about what you were talking about earlier, in that there’s advantages and disadvantages when an instructor designs the process that’s being looked at, maybe a process that’s happening over time versus discussion of process in a fixed amount of time versus a student designing their process, and it ties, I think, to some scaffolded learning too. Those are some processes that faculty design. Can you talk a little bit about some of the strengths and differences between those different kinds of examples that you just mentioned.

Jason: I think the big question is: What is the intentionality? What are we trying to teach? And I can say things like teaching process makes things visible, makes thinking visible. But the question is: To whom? Who needs to see it? And once we have those questions, what is the intention here, and also who should the process be visible to, then that helps us start to design things. Sometimes I want to see the process out in the open so that I can give feedback on it. This is what we’re talking about when we ask the students to go through the design process. What did you do? Let’s talk about it. So it’s a form of collaboration. Sometimes I want to do that. Sometimes, really, the most important thing is for students to see it, to see that kind of process and see it documented. And so when I do that, the big benefit here is metacognition, making students aware of how they’re thinking through something, and sometimes it’s more for them than it is for me. And this is where we get to reflections and journals. Sometimes I read them. Sometimes I may even evaluate them. But more often than not, ideally that is for them. And I think that those are some of the big questions that allow us to start to design things because I think it’s about balance. I think it’s really all about balance. If we go all in on teaching process for every single assignment, we’re gonna lose it. You can’t do it. On the one hand, it’s not really ideal because it’s just going to balloon everyone’s work. Students have to do more documenting. I have to look at more assignments. It can very quickly become what I call product bloat, where instead of sending in one thing, which is what they might have done 5, 10, years ago, now they’re sending in eight things, and they’re doing that full scaffold. And so you can see how product bloat isn’t good for them, and it’s not good for me. And so I think it’s okay to sometimes say I just want to see the overall process here, or maybe I don’t. There are examples of what I call process-bearing products that you can take a look at. So, for a quick example, a student writes, say, a two pager. That’s a product, but if you ask them to go through and annotate it, now it has something about the process built into it. Annotate the process you went through. Do it by pen. Do it by Google Doc. And so I think it’s okay to try to balance it. This is based off of discipline. This is based off of how many students we have. I think that one of the mistakes we can sometimes make is thinking that something that might work for teaching process in a 20-person classroom might not work in 100-person classroom, and so we make that determination and we start to narrow it down from there. And I think it’s okay to just focus on product for a little bit, and then you get the process in there later. So I think it’s all about like balancing that. A lot of it is institution, what we’re teaching, asynchronous online versus in person can be very very different too in terms of how much leeway we have.

John: And going back to that issue you just raised of scale, I can see this working really nicely in small classes. But say, hypothetically you were teaching a class of between two and four hundred students in introductory economics. Are there any ways in which process-focused teaching could be introduced into that?

Jason: Yeah, I think there can be. You know more about the field than I do in terms of how this would work out pragmatically, and I think the big decision that needs to happen at that level is how assessment is going to work. You can have students walk through the process of something in the course, a project that you maybe have had for a while, and they can walk through the process of it. But then, where it becomes a problem is if everything has to be evaluated, everything has to be graded. That’s a big decision. So I know people who’ve experimented with self-assessment as a way to get around that. Now, depending on where you teach, depending on your discipline, you might have a different approach or maybe non-approach towards that, but self-assessment is one way. Alternative assessment is another way. Students assessing each other, depending on your course, if you feel like that’s going to work. So I think a lot of it is trying to figure out how to be creative with it and what we want grading to be. So one of the things that ends up happening when we talk about the scalability of process teaching, this is where we have the conversation about AI grading or AI assisted grading, and I know people have very different feelings about this. So one way that people have done this is yes, I have a 200 person class. I want to teach process, but I can’t do it, especially if it’s a 200 person online course, I can’t manage that, and so that is where some people have leaned into AI grading as a way to approach scale, and people might have different ideas about AI-generated grading versus AI-assisted grading, but I think it’s something that has helped a lot of people. And then again, it’s just a matter of like, is it worth the trade off? There’s always a trade-off. You’re going to move away from product and towards process. Try to figure out: is that trade-off worth it? Is there another way to do it? Now, one of my hopes is that in the next couple years, we will have a way to start approaching that scale in another way that isn’t AI grading. I’m not aware of too much yet, but my hope is that we’ll continue to be creative as a community and try to figure out how to do that in a way that doesn’t just hand off grading to an AI bot. I think that many of us would balk at that, myself included. But, there is a middle ground potentially here that helps us with the scalability issue.

Rebecca: I think in terms of scalability, those different kinds of process assignments that you were talking about have different kinds of scalability. So a student that’s designing their own process that’s an interactive process with the instructor to design that process-seems a lot more labor intensive and would probably only work in a small context. But even that, if you have 20 students and they’re all doing a different process, that can seem a little overwhelming as well. [LAUGHTER] Can you talk a little bit about how you might implement something like that in a class?

Jason: Yeah, and so this is where I think we need to really decide what we need to have access to. We don’t need to have access to everything. So, for example, if I were doing a think-aloud protocol for my students, so they film themselves for five minutes, and I take a look at it. Now, 10-person class, 20-person class, cool. 50-person class, even a 20-person class, that’s a lot. That’s a lot of work hours. And so I tell them, and this takes a little bit of trust building, that actually I’m not that interested in watching the video. Sometimes I don’t watch the video. I want them to take a look at it. It’s for them. The video is for them, and instead of getting a five-minute video, maybe I get a quick write-up that I can look at very, very quickly. Sometimes I can look at it in 30 seconds, and so I think we make that determination. And I think it’s okay too to have this ad hoc approach, so that if I go and I’m looking at those think-aloud protocols, and I read one, and I say, “Well, I’m not quite sure the learning is happening here. I’m not seeing that level of engagement.” Then I can, in an ad hoc way, go and look at that video, and that allows me to use my time a little bit more. And I think it’s okay to do that. I think it’s okay to have things that are just for them. And the other thing, and again, I know this is so discipline-based, but teaching self-assessment has saved me a ton of time. Having students go through and give their own grade for themselves, I obviously come in and take a look, and if something seems really, really off, then I can step in and intervene. But in general, giving them the ability to self-assess themselves really really does help, and I think it’s okay. I don’t think that’s even cutting corners. I think that’s allowing us to have a conversation about how they’re assessing themselves. If there is a gap between how they assess their work and how I’m assessing their work. If I have a student send in a project and their self-assessment, I might be able to review that in five minutes, maybe even shorter, depending on the project, and I can see: “Oh, I think their self-assessment is pretty good. I think it’s kind of on point here.” Or conversely, that maybe I think it’s really off. Maybe I would give a student a D, but they’re giving them an A, and I want access to that, and that allows me to have a conversation with the student in a way that also does guard my time. So I think, for me, what has really helped is those different strategies, and I think that we can pick and choose them and adapt them for our own discipline. That’s in my experience, anyway.

John: So one of the things you talk about is having students design their processes individually. Could you walk us through an example of that in terms of a particular assignment, let’s say, or to achieve a particular learning objective?

Jason: Yeah, so I’ve done this in my art of film class. I’ve also done this in our writing-intensive courses. So for the class I’m teaching right now, I’m teaching a foundational literature course, and so it’s all about just engaging with story and just how expansive that can be as a category. So for that one, as their midterm project, I want them to create a product, create a project, that shows how they’re working through the course material. I can give students more specific directions if they’re stuck with that. But what I really do is I ask them, let’s design this together and show how it connects. So, for example, I will have a whole range of projects in that literature class. Some students want to do a creative writing assignment. Some students want to do an analytical assignment. So, right now we’re reading Persepolis. So, I might have some students who say write a prequel to Persepolis. They want to do that sort of work, and then others they want to do more of a traditional essay. So I work with them, and I do this as complete and incomplete, or try again. As they’re going through, and they have that project, we have a back and forth. I give the okay. That’s something to review pretty quickly. If they’re connecting it to the course, it’s something that they’re interested in. I usually give them just a complete, and then they start to build from there. And so once they have that done, once they have that project, and again, some will be creative, some will be analytical. Then we have the conversation about what’s going to help you get there. And this is where it is 90% for them, 10% for me, that they’re going to go through and say, “This is what I think I’m going to do. I want to write a prequel to Persepolis.” Okay, cool. So I’m going to go and I’m going to write a rough draft on my own. Then I’m going to use AI if they want to. I’m going to create an outline or whatever it is, whatever the process is. And for that, again, thinking about scalability, it’s all about do you have a process that makes sense? I can usually review this in about 30 seconds. You get a sense very quickly some students who are really struggling with creating a process, and others who like this is fine. This is not bothering me at all. And so, even in that part in the process itself, I call a process folio. I get a sense of how students are doing with creating processes and thinking about processes. The course learning objective that this comes down to, and this is where I anchor everything. For me, managing a writing process is a foundational core skill of the course, what a lot of it is about. To give a sense of how I start to create that, so when I look at a class and I’m trying to think about a process-focused assignment, first thing I do, I look at the course goals. These have been established for a while. What are the course goals that I need to hit? Then I break it down into the core skills. I usually pick like four of them. How can I boil it down even more? And then I link this assignment to that. And so that’s a way for me to make sure I’m actually hitting the course objectives, the learning objectives that I need to hit, even as I’m taking a somewhat unconventional approach, and I think that’s the sort of balance, right? You want to do both. If you’re going to focus on process, you can be creative, you can push things a little bit, but you do want to make sure you’re hitting those course objectives because that student needs to have certain skills when they graduate, go on to the next course. So I think doing that sort of work, starting with the learning objectives and working back from there, has really helped me out a lot. It also prevents me from starting from just step zero or step one. It gives me something to work with and make sure that if a student gets through my class, they’re ready for the next writing class, even if they’re doing something a little bit different. That’s been my process anyway.

Rebecca: Can you talk a little bit about the difference between students experimenting with process in like a 100-level class, where they maybe don’t have [LAUGHTER] a general sense of the processes used in the field or discipline, or maybe one a little further along in their academic journey, where they might be more deeply rooted into a discipline and might have more exposure to different kinds of processes they might use for a project.

Jason: I’m a big fan of the gradual release model, so scaffolded appropriately. And AI can actually help a lot with this, and it can allow us to give more scaffolding to some students and less to others. So, for example, for a lot of my classes, for the first project, I give a lot of scaffolding. It walks them through. This is how you create a project. For me, it’s kind of it may seem self-evident, but for a student who’s just learning the discipline, not at all, right? If someone tells you, oh create a project that’s connected to the course. So, what does that look like? And so, my impulse is to always give a lot of scaffolding for the first project, and then gradually give a little bit less as we go along. The other big way that I do this is with self-assessment. If I ask the students to self-assess themselves, some of them will just be like, all right, I kind of know what you’re talking about. Others have no idea, and so I actually give my students a couple of ways to do it. First way is the AI-free way, so they think about this is what I want to create, these are the skills I’m working on, this I’m going to self-assess. And in any individual class, I probably have about 40 to 50% choose that one, and then the other thing I do is for students who need more scaffolding and need more support, and they just need it in the moment asynchronously. I give them a custom chatbot that walks them through the process. So it asks them, “What are you trying to create?” All right, cool. Build from there. “What are the skills?” And it actually can create a rubric with the chatbot. Now suddenly, this is an activity that is more scaffolded for the students who want that scaffolding, and it’s something they can do in five minutes. Whereas without that scaffolding, it might have taken them an hour, maybe more. And then I can see that, and then when they send me the overall chat, I can quickly scan through it, and I can see the process work itself out as they’re dialoguing with this, and so my rule of thumb is to always give them more and more agency over the process as they go along. So that means a lot of scaffolding early on, and if I have two projects or three projects, I let them create the process a little bit more. And now for foundational courses, I really lean into that. If I have a student in writing and research, like I really try to carefully scaffold it. If it’s say an upper-level elective course, very very different. You might not need a ton of scaffolding early on, so I do personalize that for level and discipline. A writing-intensive course versus an elective course might be a little bit different, and it’s different in terms of course learning objectives that you need to hit. And so I’m kind of informed by that as well as I design. And then, the other thing that’s going to be interesting for us is that my college this semester has taken on AI literacy as a college-wide objective that we need to hit, and so we’re going to start to see that work its way into the conversation too. If a student is taking, say, our writing and research course, we need to have a way to tie that course to that overall college-wide learning objective. So that affects the scaffolding, that affects what you do, that affects if a faculty member wants to create a custom chatbot and link it to that, and so that’s going to be another additional wrinkle, I think. And we’ll see it cutting across different departments because students in every department are going to have to show some evidence that they worked on that competency, that literacy, whatever you want to call it.

John: One concern that many faculty have is that students are using AI to offload their cognitive processing and strategies such as designing chatbots where students are interacting with that to help design a process can be really productive because the research is suggesting that when AI is used intentionally in a manner to support student learning, it can help students learn. But we don’t always see that. One of the things I know some faculty here have been doing is they had been using in asynchronous courses metacognitive reflection discussions and logs and so forth, and they’ve been seeing more and more students turn in reflections that appear to be generated by AI rather than the authentic voice of their students. What can we do to help ensure that students are using AI appropriately. Having students share their interactions is a really good way of doing that, so you can observe it as part of the process. But how do we deal with those cases where students turn in something that doesn’t appear to have been created by them individually?

Jason: I do think getting full transcripts when you can really helps. It gives you a sense of their process, how they’re dialoging with it. My approach has been this: I front load early on in a semester, demystifying it. So custom chatbots are a great way to do this. I also design for my students pressure bots, and I have my students doing this next week. So they did some of the reading. They got the page 70 or whatever it is in the book, and I’m giving them a pressure bot that is designed to put pressure on them. I told them it might frustrate them a little bit. It’s not going to give them answers. It’s going to ask them for clarification. Going to ask them to go back to the book. I want the book in front of them. They can do some of it from memory, but in the end, I want that student to go back to the book and take another look. And so I gave it instructions for doing that. And I’ve done this for a while. One of the things that really changed for me was… it seems so simple now… show them the prompt and so they come back and we have this conversation, I say, oh, this is how AI helped them learn, and I kind of say, well, AI did, but it’s because I did this to it, and you can like show them I had to change it. I had to basically, in these custom instructions, change it from a sycophantic chatbot that’s going to say you’re awesome and great and just keep going, and towards something that’s going to challenge you, and there’s all of this knowledge that needed to happen in the background. And showing them has been really, really helpful for demystifying how this works and making it clear. And some students, I think, already knew this, or it seemed very obvious, and some not so much. If you go and you open ChatGPT and try to do this, it will not work because it doesn’t have this background. It doesn’t have this push for it. So that has been very, very helpful in demystifying it, and then getting transcripts really help. And then also the other thing that’s changed in my approach is after it’s all said and done, a student has gone through the process, they’ve created the thing, asking for a transparency statement that really has them walking through what they did and why they did it. So I have students at the end of a paper. I ask them, “What are the steps you took? Did you do a draft? Did you do an outline? Did you draw something out? …whatever it is. And I have them go to the AI Assessment Scale. So this was created by Leon Furtze and Mike Perkins, and so very simply, for a step in their process, they give it a one if they didn’t use AI. I will have students who say, “I didn’t want to use AI. I didn’t work it into my process. One’s all the way down.” A one if they didn’t touch it. A five if it was full on AI. I rarely get those. Every once in a while, they just say I created an AI outline and went from there. So go through the process, give it a number from one to five, and think about your intellectual ownership. Did you feel like that actually served you as a learner? Were you handing something off? And so, it’s not foolproof by any means, but it tries to get at how, when we use this technology, there’s always a trade-off. What we might gain in efficiency, we might lose in intellectual ownership, or anything like that. And so that has sort of helped. And over the years, the other thing that has helped me is… and this just sounds so obvious, but I think it’s kind of hard to do… really leaning into asking questions. It seems so obvious, but say, “I want to talk to you about your process. What did you do?” I think for a lot of my students, it’s hard because that feels accusatorial, and I’m learning to lean into it a little bit more. I took a look at what you did, can you walk me through the process? Did you happen to use AI? Why or why not? And just trying to really make that conversation normal. It’s not even just that I’m policing it or accusing it, but try to say “I really want to talk about this. I think something happened. Can you work with me to try to figure out if that happened or that didn’t happen?” And I always felt like that was going to automatically feel like I was catching them, and all that. But I’ve actually found exactly the opposite. Depending on how we approach it, it is a way to collaborate with the students. And I try to make it clear, this is us working together because I want to talk about your process and figure it out. It takes some ironing over to do that, but having that transparency statement, having that demystifying conversation early on has helped me scaffold for that conversation because then I talk a lot with my students about how AI could be a learning tool, but only if you use it in very specific and very intentional ways.

Rebecca: I know you were calling showing the prompt that you wrote, demystifying a little bit, but I also think it’s modeling process. Your teaching process, your modeling process, you’re talking about process. So can you talk a little bit more about the role of modeling some of these skills for students?

Jason: I think modeling is one of the most important things we can do. So when I create a scaffold for my students, I walk them through it. Why did I create that scaffold? If I’m telling students for that first project, this is what I want you to do. That is a model process, and I think sometimes I myself I never leaned into just how that works. So if I ask them, all right, I want you to free write first. Cool. Why did I do that? Why did I feel like, oh, you can’t just free write with ChatGPT? I had you free write, then I had you create an outline. Whatever the scaffolding is, and that is a model process that the instructor has created. And I think that a lot of it is showing students that step back and being very honest when we learn something about our process. So one of the example I give to my students is after ChatGPT came out and other programs started to come out. There was a few months when I was generating my PowerPoints with AI. I’d have like notes, I’d have a document, I would generate it and then I would change it. I would revise it, all that sort of stuff. And I end up walking through my students, this is the process I’ve created, and then I junked it because I felt like generating things with the slide made it so that I couldn’t present on it. I had a hard time presenting, pivoting, doing that sort of work. And so talking to them about the processes we create, why we change them, how we go forward. I think it’s probably one of the most valuable things we can do in the classroom, and it helps them to see how important process creation actually is. I think that many of my students, when they come in, they’ve been taught by the education system that what really matters is that final product, and that final product does matter, but I try to use modeling as a way to get them to think about how does scaffolding or any sort of laying out of the process, how does it help us? Why are we doing things? I think that my impulse is always just to have the scaffold there and not really talk about why I designed it that way. And I’m trying to be better about that, just showcasing the intentionality because I think it helps them think about things a little bit differently.

Rebecca: I feel like one thing that I’ve also done in that same space that has been effective is pointing out parts of the process that maybe as a student I thought were a waste of time, or I didn’t fully understand why they were there, or I would skip over it or shortcut it in various ways, and why I don’t do that now. But I found those conversations to be really powerful conversations about what is actually the point of this step.

Jason: That was my experience in high school. I hated, I hated writing in high school because of how it was scaffolded. Because I was told do this, do this, do this, and do this. And even when I was a high school junior and high school senior, someone would come in and say, “All right, the first thing I want you to do is to create a thesis statement.” No, that doesn’t help me. I need to go through the actual writing process to figure out what it is. And then they would say, “Do a thesis statement, do an outline, do this.” And I started to realize at that point that, like, “Oh, this process does not work for me. It’s great that it works for someone else. Great that it works for several of my classmates. Does not work for me.” And this again is something that we can take from process studies. In the early 1990s, there was a whole post- process movement. So this is something that was almost like 35 years ago that people were thinking about, “Oh, we all have different processes. It doesn’t work for everyone.” There isn’t a linear process we can do that gets us from point A to point B to so on and so forth and final product. The processes that we use are all different, and they should be different. And I think that sometimes we lose how fragmented that can be. But again, there’s all this scholarship about how to teach in that context, and sometimes we just need to go back to it and look at how much of that carries over to the current moment. And my experience has been that quite a bit of it does carry over.

John: So, for those who are thinking about adopting process-focused teaching, you’d recommend that they not try to impose a process on all their students.

Jason: So yeah, find ways in which, if you’re teaching process, make it conversational if you can. Now there are certain limits to this if you’re teaching online or teaching a large number of classes, but find ways to make it as conversational as you can. And this actually can be a way to also manage your course load so that you’re not designing a process for everyone. You’re working with them as they create their processes. There are ways to manage that, and again, it’s about scaffolding. For that first project, I think it’s okay to control the process a little bit. You just want them to see it. You want them to see what that process looks like, and then gradually release more agency so they can think about their own process. Think about what works for them, and make those sort of selections. That also, for individual students, might help cut down those seven steps to three steps. If I do a scaffold for students, it might be like seven steps or so. I’ve never had a student who, when I ask them to create their own process, had a 10-step process. It’s always like two or three or four. It’s always on the lower end. I think it helps us in terms of scalability too.

Rebecca: One thing that has been helpful when I’ve thought about this and providing flexibility is, for example, we need to do something to come up with an idea. We need to do some sort of ideation, but there’s lots of ways to do that. [LAUGHTER] But you need like a component of a process that gets some ideas out, and maybe offering like a menu of options sometimes can be really helpful too.

Jason: Gives them that agency, and I think that is so key, and it’s one of the reasons too why I give students as much agency as I can while also hitting those objectives over the project they’re creating. This is one of the first principles of teaching process from the 1970s. If you’re going to teach students to own the process, they have to have some ownership over the product. That’s like principle number one or principle number two, when it was laid out. I think that’s just so key, and also it pushes against the incentive to overuse AI or misuse AI, to own the project you’re creating. No, it’s not going to make everyone be honorable and do all the work. But I think it helps when you are creating a project that you have very little buy-in, you didn’t help create, you didn’t help put that sort of work in, AI becomes an even more attractive option for bypassing the learning. So I think giving students agency, giving them ownership in the material is one of the best ways to push against that and get them to do the learning, which sometimes requires hard work.

John: You’ve suggested some strategies that can be used in any class, including asynchronous courses. But are there some aspects of process-focused assessment that works well with asynchronous courses when you don’t get to see the students individually on a regular basis.

Jason: Two things: one, really think about products that you can take a look at that have something about the process built into them. Those comments, some sort of combination that you can look at a product pretty quickly and see what actually was built into it, how was it designed, that sort of thing. And then the other is lean into asking questions. I think that if I ask for every process along with every assignment, especially in an online course, it blows up so fast it’s overwhelming for everyone. But if I’m looking at say 20 projects and I ask a question about the process with one or two of them, that’s doing my due diligence. And again, it can be conversational. I think there are ways to phrase it so that it becomes a part of a collaborative exercise to figure out what that process was, how it can be improved. And those have been ones that really, really helped me because if you’re teaching a pretty large online asynchronous class, you need to do it in a way that guards your own mental health, if you can. And those have been the two that really, really helped me. So that I’m not asking for a page-long process statement from 20 students for one week. I can’t do that, but I can do ad hoc questions, make them conversational, have a back and forth. That’s much more imaginable to me. And I make it very clear to students, I ask you for clarification on a comment on Canvas, the LMS that we use, that is a part of the assignment. Writing back to me is a part of the assignment, so we can have this back and forth, which they’re not usually used to early on. But if I do it a couple times, they become very used to it pretty quickly.

Rebecca: We talked a little bit earlier about your pressure bot, and then also a bot to help design a process. Can you talk a little bit about ways that we might use AI to do some critique of our work or challenging students to interrogate their work a little bit more before submission? [LAUGHTER]

Jason: The model that has been really, really helpful is the PAIRR model. So it’s P-A-I-R-R. It’s peer and AI review and reflection, I think i’s the acronym. So this is out of the University of California San Diego, and so what they found was the big thing was you use AI assistive feedback, and it’s very very helpful for students, and you sandwich it. Basically, you start with peer assessment. I also sometimes start with self-assessment. How do you think you did? Then you can get feedback from an AI program. You can ask for the transcript, however you want to do it, and then you have them review and reflect on it. And so it’s really about anchoring that feedback. They go and they take their paper and they put it into a feedback generator or an AI grader, which my students are increasingly doing. Do you agree with it? Would you change it? Do you think that grade is accurate? And that allows them to anchor it a little bit more. That’s the key to it. They can get that feedback, but I also want them to engage with it critically, because I think the impulse for my students is to just take all of the feedback from an AI bot and just implement it. And I don’t want them to do that. And I’ll tell them, if I even go through something like Grammarly, it’s on my own work. I’m constantly saying yes, no, yes, no, yes. You use your judgment, and so I think that’s the muscle that needs to be developed. And a lot of it is contextualizing the feedback rather than just letting it stand on its own. Yeah, and you compare it. What did the AI bot tell you? What did your peer tell you? If they’re different, why do you think they’re different? What am I telling you? And so it allows them to kind of see the link, which is exactly what a lot of times we’re being asked to do in the workforce. We go through, and we have a project. I do this all the time. You have a project, you send it out there, you get three sets of feedback, and you have to like judge. And they don’t complement each other a lot of times. They contradict each other. You can’t hit all those sets of feedback. So I think that there’s an argument to be said that this is part of workforce training. How do you implement that feedback? And AI just becomes one set of feedback among others, which I think is exactly where it should be, so that it’s not getting too much gravity and becoming the center of attention, and it contextualizes it a little bit, and also starts to demystify the feedback in a way that’s just one source among many.

John: Suppose someone wants to begin implementing process-based assessment. Can it be something done at a small scale before they completely revise the design of their courses, or is it something that should be done throughout the course as a complete change in structure?

Jason: Start small, please start small. Make it easier for yourself. Do it for an assignment. Do it for a mini assignment. So start small. Build from there. In my experience, if you try to make the entire course process based. It’s a lot to deal with at once, and especially because I do believe that a lot of teaching process is discipline-based or discipline-sensitive, I’ll say. And so if you have that assignment, you have that scaffold, that’s one way of teaching process. Take what you’ve already learned, move it towards something else. Try something a week. Maybe you try to think-out-loud. Maybe it works. Maybe it doesn’t work. You can sort of pivot from there. So I would say you definitely try small, iterate, figure out what you’ve learned from that, and then you can design from there. I don’t think you have to do everything all at once or do all of your classes. I say if you’re just getting started on that journey, start small in one course and then build as you feel like you learn more about it.

Rebecca: Well, we always wrap up by asking: “What’s next?”

Jason: Yeah, so I think the big thing is trying to figure out how grading works in this conversation. This is an ongoing project for me. I have a lot of provisional ideas, and I’ve changed my grading over the last couple of years. But if the process-focused classroom, and I think it is, is about experimentation, it’s about you try something, you try that process, and it fails. How does grading fit into that? Because the students get to me, and they’ve had 12 plus years, usually in a formal education system where everything is made visible, everything is graded, everything is given a score, and sometimes, especially when students go on their phone now, they go into the LMS and they’re greeted by an AI bot that offers to do something for them. I think a lot about the incentive structure, and this is big-picture stuff. Trying to figure out if we’re going to teach process in this way, where generative AI is just going to be embedded in everything. Do we need to think about grading again? What sort of grading systems helps? Is there an incentive structure that students are surrounded by that we need to start to challenge and poke again? So that’s increasingly what I’m thinking about because it’s really hard to get students to experiment and play in a classroom when it’s like, oh, they’re gonna get a 70 instead of 100. So that’s the current part of my project that I’m always stuck on because grade reform is really hard, and we’d have to wait a really really long time for if it ever happens, and so trying to figure out how we can push against the incentive structures of transactional education, even within our very pretty limited spheres of influence. That’s where my mind is right now.

Rebecca: Well, thank you so much for joining us. It’s a great conversation and plenty of things for everyone to think about.

John: And we’re really looking forward to seeing your book.

Jason: Thank you so much.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing Assistance provided by Madison Lee.

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451. All Day TA

 Imagine having a virtual clone of yourself to assist your students whenever they need help and would track where students are struggling. In this episode, Joshua Gans joins us to discuss an attempt at creating an AI platform that accomplishes this.

Joshua is a Professor of Strategic Management and Jeffrey S. Skill Chair of Technical Innovation and Entrepreneurship at the University of Toronto’s Rotman School of Management with a joint appointment in the Department of Economics. His work focuses on the economics of innovation, entrepreneurship, and artificial intelligence. He has authored numerous scholarly articles on the economic impact of generative AI and, with Ajay Agrawal and Avi Goldfarb, of Prediction Machines and Power and Prediction, two books that have helped shape public understanding of the economic implications of AI.  Joshua has been thinking deeply about what generative AI means for higher education: how it can support teaching and learning, how colleges should prepare students for AI-augmented careers, and how AI may reshape labor market opportunities for today’s students. At the University of Toronto, he and Kevin Bryan developed an AI teaching assistant, All Day TA, which has been used to provide students with support outside traditional class hours.

Show Notes

  • Agrawal, A., Gans, J., & Goldfarb, A. (2022). Prediction machines, updated and expanded: The simple economics of artificial intelligence. Harvard Business Press.
  • Agrawal, A., Gans, J., & Goldfarb, A. (2022). Power and prediction: The disruptive economics of artificial intelligence. Harvard Business Press.
  • All Day TA

Transcript

John: Imagine having a virtual clone of yourself to assist your students whenever they need help and would track where students are struggling. In this episode, we explore an attempt at creating an AI platform that accomplishes this.

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John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

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Rebecca: Our guest today is

Joshua: Gans, Professor of Strategic Management and Jeffrey S. Skill Chair of Technical Innovation and Entrepreneurship at the University of Toronto’s Rotman School of Management with a joint appointment in the Department of Economics. His work focuses on the economics of innovation, entrepreneurship, and artificial intelligence. He has authored numerous scholarly articles on the economic impact of generative AI and, with Ajay Agrawal and Avi Goldfarb, of Prediction Machines and Power and Prediction, two books that have helped shape public understanding of the economic implications of AI.

Joshua: has been thinking deeply about what generative AI means for higher education: how it can support teaching and learning, how colleges should prepare students for AI-augmented careers, and how AI may reshape labor market opportunities for today’s students. At the University of Toronto, he and Kevin Bryan developed an AI teaching assistant, All Day TA, which has been used to provide students with support outside traditional class hours. Welcome,

Joshua:.

Joshua:: I’m happy to be here.

John: We’re very glad you are joining us. Today’s teas are:…

Joshua:, are you drinking tea by any chance?

Joshua:: I am not. I’m sorry. I see it in the title. I don’t see it in the instructions for the podcast, and I would have followed them. [LAUGHTER]

John: Most of our recent guests have not.

Joshua:: I guess the listeners out there should pause this now and go get their tea.

Rebecca: Sounds like a good plan. I have English afternoon today, John.

John: and I have an Irish breakfast tea today.

Rebecca: Going all imperial. [LAUGHTER] At the initial release of ChatGPT in late November 2022 a primary concern of faculty was really on the impact of generative AI platforms on academic integrity, but now that we’ve had more experience with generative AI tools, what have faculty and institutions learned about the risks and opportunities of generative AI?

Joshua:: Well, those initial concerns that people were worried about have only got worse [LAUGHTER] in many respects. Nothing in the development of AI has brought any comfort to those concerns, at least insofar as the foundation models are concerned. I think this was always on the cards, which is one of the reasons we started working on this problem back in November 2022 when ChatGPT came out. Put simply, it’s not just a student issue. ChatGPT helps you do stuff, and it helps you do stuff that you don’t like doing. It just so happens that we have set ourselves up for a challenge there with regard to students, because we give them a whole lot of things to do, for most of students, most of the time they don’t like doing it, [LAUGHTER] relative to other things they could do. And I think they very much understand why they should be doing it, but unfortunately, our students, it turns out, are quite human, and so a temptation is right there that they will take it. That said, there are actually things we’ve learned from this experience. I think actually students have been learning faster than faculty about AI. One of the challenges we have is, well, how do we prepare the students for the world that they’re going to be working in, where they’ll have AI tools, etc. when we, as faculty, and everybody else doesn’t know anything about that, [LAUGHTER] and really it’s very difficult to even anticipate. Well, one of the fortunate answers is that, amongst all groups in society, students by this very mechanism have actually spent more time with AI than most people, [LAUGHTER] and so they are more keenly aware of where it works and where it does not work. That may still pose a challenge for assessment, but I think students I’ve noticed have got better at using AI, even in their assessment, which has got to bode well for something.

John: We give a lot of workshops here, and there’s two sort of responses we tend to get from faculty. Faculty tend to be really excited about all the things that AI can do for them when they learn about them, but they’re really troubled about all the things that students can use AI for. What sort of changes should we be making in teaching practices to deal with an environment where student access to AI tools is pretty much ubiquitous?

Joshua:: I think, when it comes down to it, the problem we have is the problem we’ve always had, [LAUGHTER] which is how do we engage students and get them to learn for their own good. Now our tried and true method is to basically hold a set of carrots and sticks, or some mix between the two, and then come up with surrogates for learning, which is essentially not proof that you’ve actually learned or deeply understood, the things that are correlated with it, and they may be more correlated with it, or more appropriate for certain types of students, and certain types of learning patterns, and neurodiversity, we call it, and things like that, but that’s always been the case, and so basically our approach has been: you should learn this or else, [LAUGHTER] and the “or else” is also done with a caveat as well. It basically says “I’m a professor, I can come and I can give you lectures and talk with you. I can spend a little bit of time in office hours if other people have additional questions, but by golly, I hate grading. I hate setting assessment. I hate grading assessment. So I’m going to give you assessment a couple of times a semester, and we’re going to be all in on that, you either do well there or you get whacked with a stick.” Now, that creates a very high-stakes, high-pressure environment, but I don’t believe there is any scientific merit that that’s a good way to learn. The way to learn is the way we actually assume students learn, but none of them do, which is every week you prepare for the lecture, you do the readings, you come in and you pay 100% rapt attention in it, and then you repeat. That would be the nice way to do things, [LAUGHTER] but we haven’t matched the assessment with that at all, we’ve made the assessment to do with cramming for final exams and things like that. Now, to be clear, if you do do what the professors want you to do and pay attention in a methodical way, you do quite well. And you know which group of people actually did that? The professors themselves, when they were studying, they generally tended to do that. That’s how we select professors. So we basically created a mythical system with practical implications that are very predictable, and I don’t like to condone cheating, but if you tell people their livelihood depends on something, which is a critical moment, and they haven’t learned all this stuff, they’re going to do things. [LAUGHTER] I’m an economist, and that’s what we’d expect, and we’ve set up everything about assessment, actually, on the presumption that they will try and do those things as well. So, throw in ChatGPT, [LAUGHTER] you’ll get more of that. The sort of good news is that ChatGPT, and AI in general, is so powerful. We’ve got a whole group of professors saying, well, I guess I’m going to have to spend more time creating exams and proctoring it, and some people are talking about oral exams, and a whole lot of things. I guess I’m going to have to do all that, but the alternative is, or maybe not. Is there a way we can actually use the AI in a way that will actually alleviate the fundamental problem, which is high-stakes assessment, and so that was our motivation for creating All Day TA. We haven’t got to that aspiration, but that’s where we were starting from. Is there a way that we can reduce the incentives of students to go to ChatGPT and instead pay attention to the things that the professor wants them to pay attention to.

Rebecca: What I’m hearing you describe are faculty that sound a lot like students and students that sound a lot like faculty.

Joshua:: They have a lot of commonalities. [LAUGHTER] If you are honest with yourselves, we are all balancing all sorts of things, and in many respects, depending on your perspective, they can look the same way, which is one of the reasons why I don’t jump up and down with the moral force and outrage of some [LAUGHTER] of my colleagues, because we’re not exactly pure in this endeavor. So you can think about it, what if we were so pure and given that as professors we actually wanted to do the right thing by learning, what would we do? We’ll spend all our time on it. Then we might come up with something closer to what they do in Oxford and Cambridge, which is they have two or three students a classroom sessions. They teach all the time. They are able to monitor the students’ progress every week. It’s in an intimate setting, so everybody’s got an incentive to study and learn as they go along, and then who cares about the final exams? [LAUGHTER] They probably don’t mean anything whatsoever, and who cares about AI, because you’re sitting right there, you can talk to people, you can find out if they know things. So we know that if we alleviate the chief cost for faculty, that is their own time, then we can design the right system. So then there comes the question is: “Well, can we use AI to do that same thing?” And our answer is yes. [LAUGHTER] Yes, I think you can, because it is possible to take your own material, the things in the way you like to teach, transcription of your own lectures, add other stuff to it, and I know it’s not a popular phrase, but you sort of create a digital twin of yourself, and it’s a twin that can interact, and 95% of the time will give exactly the same answer to the students that you would if you were spending time there. But there’s a big difference, even in this ideal Oxford Cambridge world, I only have 24 hours a day, seven days a week, even in that ideal world. When we first did a very crude version of our chatbot for our own courses, trained on our own material, back in 2023, we had 200 students in the class, and they asked, over the course of semester, this chatbot, which is ChatGPT 3.5 which is like the very first version, 10,000 questions. There is no professor at Oxford and Cambridge who is answering 10,000 questions, and so you can see that. And now most of those questions came near the midterm and near the final… what a surprise… because it’s actually a very useful tool, given the current system, but at the same time, when we’ve done things since then, you can now use these same materials to generate questions to ask the students, and they use it for self-testing, and it turns out they like that even more, and that’s not even assessment, that’s just practicing for midterms and finals, they like that even more. And we got up in one class in Texas was 80,000 of these, they were using it all the time, and they’re very high quality because they’re trained on your data and with your learning objectives in mind, so it’s only a small step away from that to saying, “Why don’t the students do something a bit more like the Khan Academy does for elementary, middle, and high school, where it does little tests, and you don’t get to learn the next bit or do the hardest stuff until you’ve mastered the other thing.” What if we had these multiple choice short answers, or whatever things we might evolve to, if we put our minds to it every week, and then the students will be sitting there: “Well, I could go to ChatGPT for these questions, but the problem is by the end of the semester, ChatGPT won’t be able to answer them, and so I’ll need to answer them. So it may be just easier for me to just answer them now when they’re easy [LAUGHTER] and spread out my learning of it.” I don’t know if that’s possible. We have another thing we deal with in this, which is IT departments and integrations in universities. We haven’t had that sort of access to really put in the tests, but that’s the goal. The goal is, if we could alleviate the professor constraint, we can have low- stakes assessment at regular intervals, paced at the student’s own pace, that allows us to do things, for instance, like making it not by pass and fail, keep doing it until you know it, which is very low-stakes indeed. So that’s why I think there’s a lot of promise, but we’ve had a wake-up call. But the wake-up call reaction is not to run away from AI. I think it’s to run directly towards it.

John: Now, does All Day TA track student progress and provide reports to the instructor, or is it just something used by the students themselves without that sort of information. You’re getting some information on the number of uses, but do you have other information about whether people have attained mastery?

Joshua:: Oh, yes, we definitely do have other. So we don’t identify students by their questions and their performance, and you could imagine a world where some, for actual assessment, they give permission, and of course those get recorded, but there’s a reason for that. Is because we saw the sorts of questions students were asking, even if we didn’t know who they were, and like 40% of them were ones that they would be embarrassed to ask a professor, but it’s so much easier to ask the chatbot than to ferret around in their notes and textbook for the answer. So it’s really a smoothing tool, but if they knew the professor was looking over them, they probably wouldn’t want to do that. You want to encourage the students not to fear this thing and not to worry about, but at the same time, look at all this information you’re getting as a professor about what the students are asking about, where they’re struggling, and there could be also things that you expressed incorrectly. If the chatbots answering something incorrectly, chances are we know it’s because you failed to describe it properly, [LAUGHTER] and not because of the chatbot making stuff up. That’s not the way we’ve designed it. So, we provide basically, like you would with a really good TA, you’d sort of say, “Oh, where are the students struggling?” So, every week you get a summary of what they’ve been looking for, what they’ve been concentrating on, or where the difficulties are. You get access to all the questions, even though you don’t know who’s asked them, and you can look through all that. You can adjust your lectures next week because of the information you receive. You can also adjust the answers right on the spot, and say, “Oh, that’s not the answer I want to give here,” and I can adjust that. It all depends on how much time, of course, you want to spend with it, but all that information is there, and our experience is that professors love getting real-time feedback on what students understand. It turns out, for all our wanting to save time on things, we are interested in using our time well.

Rebecca: You’ve indicated a little bit about how many questions, the sheer quantity [LAUGHTER] of questions that students have asked. Do you have more feedback on how students are responding to the platform, and maybe what profiles of students are benefiting the most from All Day TA?

Joshua:: So, it’s varied on things. We’ve rolled out this platform that can be used in any course, regardless of the discipline, regardless of the subject, where it really has a nice fit, especially. And basically, the thing that gives them a nice fit is where those assessments are very high stakes, ironically, where you’ve got midterms, finals, and increasingly so at the moment, or where you’re in a profession like nursing, where not only do you have to do an exam, you’ve got to score high on it, very high usage of All Day TA for those circumstances. Where people have a PhD course, where they’ve got the assignments getting done, and other courses, which are a bit more discussion-oriented, and things like that, not so. It all depends. Now, we haven’t done nearly enough to know all those gradations, but we do know that as people currently move back towards in-person exams because of the AI issues, at least until we sort out something else. All Day TA is a huge component for that, because it allows you to do that without getting simultaneously overwhelmed with students in those exam periods, panics, and what have you. And we haven’t had a study of this, but I imagine also it alleviates student anxiety. I mean, we notice that most of these questions are coming out outside of work time, and that’s why we called it All Day TA, [LAUGHTER] because it really is around, and it’s there when the students actually need it, and so I think it can reduce anxiety just by its mere existence. Even though I would like it all to evolve into another system.

John: You mentioned this evolving into another system. Where would you like to see this go in the future?

Joshua:: Well, our business [LAUGHTER] is convincing universities to adopt this. I don’t know what your impression of university is, but fast to adopt technology is not their characteristic. The students are quick, [LAUGHTER] but the universities tend to be slow. Imagine you could integrate this into the LMS, and the students, you had all their courses and stuff like that. Imagine they’re taking a third-year course and they’re studying for it, and the previous prerequisite was in first year. Can you imagine that they have access to their first-year TA still in third year? That’s got to be a good thing. And then when you start thinking in those terms, you say, “Hey, why would we stop when the student leaves the university?” So the possibilities are quite enormous in terms of being able to provide something better for the students to learn and to work, taking into the very real account that they’re human beings [LAUGHTER] and have those sorts of issues with them. And so the immediate thing that I want to do is, I would like to, and we’ve had a couple of successes in this, being able to integrate right into the LMS at an institutional level. Fortunately, AI is actually not that expensive in terms of it requires authority, because doing things with LMS should require authority, but it’s not like a big budget item. [LAUGHTER] It’s actually quite small for what it is doing, and so I would like to do that, because I think that’s the path to providing a better student experience, but also providing the information we need to design whole curricula and other things. What is being remembered? Wouldn’t it be great to know in your third-year class what people are asking about on day one about the first-year class? Wouldn’t that be really helpful to know what they have retained and what they haven’t, and just be aware of it? We currently do all this kind of blind, and then once we’ve done that, working out how to put the assessment in, and the low stakes-assessments, and other things, is all possible, but at the moment, access to student records, and student assistance, and student privacy is extremely important, and we think it is the hallmark of this business, and these sorts of products, so it needs universities to be very proactive in wanting to do that.

Rebecca: One of the things that you just hinted at a bit is the idea of access to AI beyond the classroom and in jobs and beyond. So many college students are constantly expressing concern about the impact of AI on their future jobs, and what occupations might AI tools serve to complement, and where might they serve as a substitute for highly educated workers?

Joshua:: Oh, well, that is a very big and important question that we haven’t got a resolution to. We could have an entire other podcast about that issue, but the way I sort of thought about it in our modest example was, you’re a student, you’ve started your new job, you’ve had some on-the-job training and other things like that. Then something comes up, and they’re assuming you remember whatever, and you don’t. [LAUGHTER] So you can go to the internet, but it may be something that you recall your professor having talked about, and so you could then basically ask your professor again without emailing them for those things, and it could be brought back up, and it triggers the memories, and it makes those connections better, and it might just be much easier to take the knowledge that we’ve given the student, pour it into their head, force them to cram, but maybe they’ll just remember a bit of it, and they can pull that straight into the workplace and make them more productive on day one. That’s the kind of thing that I just feel we seem to say, “Well, you’re on your own,” [LAUGHTER] and everybody’s talked about lifelong learning and other ways of staying connected. Well, here’s another potential opportunity here that I can see how it might work.

Rebecca: Or an opportunity for quality control over the sources,

Joshua:: Yeah. Well, what if there are updates as well? It could be that, yeah, you learned it this way, but now there’s some additional things. I don’t know, the possibilities are endless. And, of course, this is not something any centralized company is going to be able to provide. It’s going to be the people who taught you in the past who are updating their own systems, and so long as you can push that out. It’s interesting, though. I think there’s a lot of faculty members who are concerned about such things. [LAUGHTER] I can imagine dire scenarios in which that occurs, but I have to admit I still have more than enough faith in what we add, in both developing content and also structuring it in nuance and thinking about learning and stuff that the replacement option isn’t there. I’m all about replacing the stuff that we don’t like doing, so we can concentrate on the stuff that we do like doing.

John: And colleges in general haven’t always done a very good job of encouraging that lifelong learning, because, for example, they set up systems to buy back student books, so that students don’t get to keep their books, and LMS systems, almost every institution will lock them down shortly after the semester ends, so all the material that they had and resources disappear. The practical reason for that is, if students have to retake a course, they don’t want them to have access to similar assessments, but for most students, having access to those resources would be really helpful, especially if that could continue after they left college, as you mentioned.

Joshua:: I mean, again, think about what having access to recent assessment is. Why is that the case? That’s the case because we kind of like to reuse things that work, but with AI, you can tweak it up enough that that wouldn’t be an issue either. And also that presumes that these are high-stakes assessments that people want access to, as opposed to low- stakes ones that they don’t. So, really, from my perspective, you can really see how we could push to a new system given the right set of circumstances. I’m hopeful one day one university will sort of like realize this and we’ll be able to evolve to it and really experiment with it. We’re doing a lot of that here at the Rotman School, but even here, where it’s been in our classrooms for three years, and a larger number of classrooms, we’re still not integrated with the LMS, because that’s controlled by the University of Toronto, which is one of the largest universities in the world, [LAUGHTER] and I must admit, with the current security issues that hit Canvas, no one’s jumping up and down to do things, so it’s a challenging environment.

Rebecca: You spoke a bit earlier about the concerns of faculty not necessarily having a lot of experience with AI, but still needing to help students prepare for a workforce that does use AI. Can you talk a little bit about additional ways that we can better prepare students for that work environment?

Joshua:: That is a very difficult question. One of the problems is that things are changing so quickly, so even when we get some research on what might be working for students or what they might need in the workplace, that was from a model that was a year ago, and now is it relevant? Now that will settle down eventually for that stuff, but I don’t see any short-term thing there. I think it’s more we have to understand that we’re running big experiments all the time now, and not only the students learning, but we are learning as well as we do them. A lot of faculty I’ve encountered have chosen: Well, what if we just pretend that the thing doesn’t exist?” [LAUGHTER] And the problem with that is you will not learn, you cannot learn, you cannot serve your students if you pretend the thing doesn’t exist. Now, there could be great rationales for trying to work out what you want the students to learn without AI and what you want them to learn with AI, and we’ve had this challenge before. We had the same challenge when we had calculators, and we had the same challenge when I was told, when I was going through high school, I wasn’t allowed to type up my assignments at all, because computers started to have spell checkers, and that boy did they not want me to use computers to spell correctly. I should just somehow do it myself, even though let’s face it, the second I was out of there, I never had to worry about that again. So [LAUGHTER] that’s the kind of things that we’re dealing with, but there are things you have to learn, and any new technology really sort of forces you to think about what is it that I need to get into a student’s head, and will AI get in the way of that, which it could.

Rebecca: Probably like the need to prioritize limited resources, limited time with students. You have to decide what’s the most important to focus on, and what maybe we can offset to technology.

Joshua:: Indeed.

Rebecca: It’s always about resource management, isn’t it? These economists….

Joshua:: Ah yeah, exactly, [LAUGHTER] that’s correct. [LAUGHTER]

John: Going back, though, to the labor force, as Rebecca had mentioned before, there’s a lot of students who are really anxious about what their job prospects are like, and we’ve seen in the last couple of years that the job prospects in the U.S., at least, for a new college graduate have deteriorated quite a bit, and it’s not clear how much of that is due to AI, how much, because of the random policy shifts that are occurring at the federal level, or whatever else might be causing these things. One concern that’s coming up a lot in discussions is whether college is still going to be relevant in a world in which people have access to AI tools, and can use them to substitute for the types of learning that occurs in college. Do you think college is going to disappear or change fundamentally in some way?

Joshua:: Well, that is a, of course, a great question, but it’s not a question that just exists because of AI. I think, actually, with proper introspection, we should wonder about whether we have been, for the past few decades, serving students well. Now the evidence is that they earn more, they can get more jobs and other things for a college degree than with not. In the U.S., at least they’re quite expensive. It’s not clear what the value of that is, since at other places it’s much cheaper and students seem to get similar benefits. So we could discuss that. So there’s always a constant problem of what is relevant, what is out of college, and also half of what you get out of college is not related to the formal education, and it’s related to other things, growing up and socializing, and becoming an adult, and all those other things we list [LAUGHTER] to justify our existence. But the student anxiety and stuff, I’m not surprised, students are anxious about AI, because all of the big proponents of AI are going running around saying they’re going to wipe out all the jobs, and I have never bought into that. I’m, amongst my colleagues, a very prolific user of AI. I use it for all manner of things, and some of those things are quite excellent. I’m a head of department, and I use AI now to get rid of all of those reports and things that I have to write for the university that might have taken a day. They now get done in seconds, and it’s great because no one was ever going to read those reports. They just wanted to see that they existed, and now that I can use AI, they exist, much like students producing assignments, and so I can’t blame them. Students are producing assignments, and there’s producing for professors who, for the most part, don’t want to actually read them. Anyway, nonetheless, [LAUGHTER] the thing that I’ve seen from that is that anything that is enjoyable and similarly meaningful to you, and also involves judgment and taste, these are things that you can’t get from the AI. These AI models, as we wrote in our first book, are prediction machines. They’re good at certain aspects of intelligence, but not others. They’re quite miraculous in the way they’ve evolved over the time I’ve been studying them. No doubt about that. They’re amazing things, but in all of it, I haven’t had any reason to doubt the value and usefulness of what humans do, even though it’s sometimes very difficult to define. It’s come in my own experience, it’s come in all of the data that is generated. As I said, we can’t identify whether people have been losing jobs from AI. Will some people lose jobs from AI? No doubt, because it’s going to reorganize things, that happens, but is there going to be the mass 50% unemployment that somehow engineers in Silicon Valley can imagine, but no one else can. That’s not going to happen. And fundamentally, it’s this: if you believe enough that humans matter, that even with the best AI, humans provide something of value. If you believe that, you can simultaneously believe that AI is going to be great, and AI is going to be great for people. It’s only where you are in a mindset that I think is what drove the Silicon Valley types, is that humans are crappy, stupid, and should be replaced, that you get big use of AI and big displacement of humans. And so, fundamentally, that’s the different assumptions, people who are anxious have to say: “Is there something… what is it that I can do?” Now, I mean, the problem is that, being older folk, we sort of have more opinions about our value [LAUGHTER] as we’ve learned it over time, and university graduates, all they’ve been doing is not getting that, [LAUGHTER] not yet. And so it’s sort of like you would have to take that on faith, but I must admit I’ve seen no evidence to the contrary. That’s my resident belief at the moment, and so I’m on team human.

Rebecca: So pro human, I hear.

Joshua:: And I was perfectly open to the idea that we weren’t. I mean, when it comes down to it, my use of AI has been a whole lot of things that I’ve decided the AI is better than I am. I’ve decided that literally answering student questions, I think it’s better than I am… [LAUGHTER] doing those sorts of things. But ther’re other things, there’s enough of the other stuff, enough of the other stuff that I think it works, and I can spend more time doing that, so even better.

John: And at two in the morning, AI tends to be much better at answering student questions than we do.

Joshua:: Exactly, and the students know that too. The students don’t like going… there’s some students who like seeing professors, students who are really at the top of the class want to see it, but not the seventy on the test. And then the students who are struggling also like to go because they need that extra help. And the beauty of it is, is if you use All Day TA, you don’t have the mass in the middle coming and bothering you, and you can concentrate on the parts where you can actually provide the most value. It’s absolutely true. What do we get most value out of a professor? It’s engaging with really smart students who we think are going to go somewhere, or a struggling student who really was close to giving up, that you sort of explain things to, and they’ve been able to come through. Those are our rewarding parts. That’s what we want to do. We don’t like the students to come in the middle saying, “Will this be on the test? They can ask the AI.

John: And there is a fair amount of research that suggests that first-generation students and students with imposter syndrome are very unlikely to go to their professor to ask questions, because that would reveal that they don’t understand things that well, and they’d rather keep that hidden, and many of them would just give up. So having access to free tutoring service without any judgment 24 hours a day is a valuable service.

Joshua:: I would go beyond that. Well, I’m in one of the elite universities, University of Toronto. The vast majority of tertiary institutions are not that, they are ones where a lot of students struggling financially, they have other jobs, they’re working part time, they’re studying part time, and things like that, and those are the students who drop out. And why do you imagine they drop out? Because they’ve got these assessments, because they have under-resourced teachers as well. So, they have these high-stakes assessments, and it’s basically on the night before, they haven’t been able to learn it, and they can’t get any help. And they said, “Well, I can’t do this anymore.” Now, if just a few of those students, [LAUGHTER] because of All Day TA, can get the answers they need and be able to hump over those moments, they can complete their degree. And so, if we can just move a few percentage points on dropout rates, I will be more than thrilled.

Rebecca: Well, we always wrap up by asking, what’s next?

Joshua:: Well, what’s next? It’s very interesting to think about what’s next, because normally when we talk about what’s next, we’re thinking like in five or 10 years. Now I’m talking about what’s next is maybe later this year… [LAUGHTER] would be the sorts of things we’re thinking of, and I actually no longer have a very clear view of it. I kind of feel, “Oh, it’ll be exciting and new,” and I get excited anytime some new thing comes forward. I think what we’re going to see, what’s next, more is that the AI is going to start coming into the fore in the other parts of our jobs. As I already said, it can help administration, it can help teaching. We kinda know those. I think in research it’s going to be a very interesting development, where we will be confronted with our desire to know what the answer is versus what used to be a complimentary desire to be the one who found it, [LAUGHTER] and so we will have to reckon with that in regard, and everybody will have sort of views to it on that, but it’s not until it actually happens, or it happens to you that you really know how you feel about.

Rebecca: Much to look forward to, that’s for sure.

Joshua:: It’s going to be exciting. [LAUGHTER]

John: It is, and the rate of evolution of AI tools has been phenomenal. I don’t recall seeing anything else develop as rapidly as this.

Joshua:: No, it’s crazy. It is crazy, and I was studying this seven years before it occurred, [LAUGHTER] all the latest stuff, and it was like a surprise, how quickly it happened.

John: And just a few years ago, there were all these reports about people saying AI will be there in the next five years, and that kept getting extended, and all of a sudden it was there.

Joshua:: There it is. We arrived through the Turing test, the whole thing. Amazing.

John: Well, thank you. This was a really interesting conversation, and it’s a really interesting time to experience these things.

Joshua:: Thanks for having me. I’ve enjoyed it greatly.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

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448. AI-Aware Teaching

While there is substantial debate over the appropriate role of generative AI in higher education, one area of agreement is that AI cannot be ignored. In this episode, Annette Vee, Marc Watkins, and Derek Bruff join us to discuss what faculty need to know to be AI-aware in their teaching.

Annette is an Associate Professor of English at the University of Pittsburgh where she has been actively involved with AI initiatives. She is the author of Coding Literacy: How Computer Programming is Changing Writing and is co-editor of TextGenEd: Teaching with Text Generation Technologies. Marc is a Lecturer in Writing and Rhetoric and an Assistant Director of Academic Innovation at the University of Mississippi, where he directs the AI Institute for Teachers. Derek Bruff is an Associate Director at the University of Virginia’s Center for Teaching Excellence, where he supports faculty in integrating generative AI in their teaching. He is the author of Intentional Tech: Principles to Guide the Use of Educational Technology in College Teaching and Teaching with Classroom Response Systems: Creating Active Learning Environments. He is the host and producer of the Intentional Teaching podcast. Annette, Marc, and Derek frequently serve as keynote speakers at academic conferences and write frequently about AI and higher education on their blogs. Annette, Marc, and Derek are the co-authors of The Norton Guide to AI-Aware Teaching.

Show Notes

Transcript

John: While there is substantial debate over the appropriate role of generative AI in higher education, one area of agreement is that AI cannot be ignored. In this episode, we discuss what faculty need to know to be AI-aware in their teaching.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

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Rebecca: Our guests today are Annette Vee, Marc Watkins, and Derek Bruff. Annette is an Associate Professor of English at the University of Pittsburgh where she has been actively involved with AI initiatives. She is the author of Coding Literacy: How Computer Programming is Changing Writing and is co-editor of TextGenEd: Teaching with Text Generation Technologies. Marc is a Lecturer in Writing and Rhetoric and an Assistant Director of Academic Innovation at the University of Mississippi, where he directs the AI Institute for Teachers. Derek Bruff is an Associate Director at the University of Virginia’s Center for Teaching Excellence, where he supports faculty in integrating generative AI in their teaching. He is the author of Intentional Tech: Principles to Guide the Use of Educational Technology in College Teaching and Teaching with Classroom Response Systems: Creating Active Learning Environments. He is the host and producer of the Intentional Teaching podcast. Annette, Marc, and Derek frequently serve as keynote speakers at academic conferences and write frequently about AI and higher education on their blogs (which we’ll link to in the show notes). Our guests are the co-authors of The Norton Guide to AI-Aware Teaching. Welcome, Annette, and welcome back, Marc and Derek.

Annette: Thank you.

Derek: Thanks for having us.

Marc: Yeah, thank you, guys.

John: Thank you for joining us. It’s great to talk to all of you today. Today’s teas are:… Annette, what is your tea? [LAUGHTER]

Annette: I have to confess that we are out of tea season. For me, it is warm, and I’m on my third floor office, where it is not air conditioned, so I’m just drinking water. But I like tea. I like herbal tea… when it’s cold outside.

John: And Marc, are you drinking tea?

Marc: I wish I was. Our tea kettle in the office is broken today, so I have a very anti-tea Celsius that is a Kiwi Guava flavor, so it has caffeine and its liquid, [LAUGHTER] that’s the sort of the most related thing you can have with tea. But yeah, unfortunately, I’m like Annette, I don’t have access to good tea right now.

John: And Derek?

Derek: Well, I’m 0 for three. I am drinking coffee, [LAUGHTER] but I am drinking it out of my tea for teaching coffee mug.[LAUGHTER]

Rebecca: I think that might be blasphemous. [LAUGHTER]

Derek: I think I should get some points for having the on-brain coffee mug today.

John: Yes.

Rebecca: I think it’s good. It’s good.

Derek: Thank you, John, for gifting me a coffee mug when I saw you a couple of years ago.

Rebecca: It’s a tea mug. [LAUGHTER]

Derek: Not right now.

John: it’s a tea for teaching mug, and I think it was a coffee mug last time too.[LAUGHTER]

Rebecca: Yeah.

Annette: Get into some Wickenstinian conversations about definitions here. [LAUGHTER]

John: Well, I went to the POD conference with, I think, 48 tea for teaching mugs, and I think I came back with about two or three of them, because the whole place was filled with former guests from the podcast.

Marc: That’s great.

Annette: That’s lovely.

John: It was nice to be able to distribute them. And Rebecca, your tea?

Rebecca: I have British breakfast, nice and piping hot.

John: I have darjeeling tea today, which I picked up at the tea caddy at Epcot at the OLC conference last fall.

Rebecca: So we’ve invited me here today to discuss the Norton guide to AI-aware teaching, apparently, and not tea. [LAUGHTER]

Annette: It’s good because the conversation’s over otherwise. [LAUGHTER]

Rebecca: So, can you tell us a little bit about how this project came about?

Annette: Sure, I can start with that. So, this project was kind of a brainchild of Norton themselves, and I had been working with Erica Wnek, who is one of their fabulous editors there, and who works with composition, in particular. I’d been doing the Everyone Is an Author book. They wanted to add a chapter on AI for their next edition, which just came out, I think, in the fall, and from there we started talking about what else Norton might be able to do to support teachers with AI, and I started writing for a Substack, a newsletter that they launched, and worked with Erica primarily on that. It was the first time that Norton had done a Substack like that, and it ended up being reasonably popular, and then there seemed to be some demand for further guidance about AI teaching, but in particular teaching in the midst of AI that wasn’t just, “Hey, let’s lean into AI or here’s how we kind of route around AI,” but something a little bit more measured that was kind of the voice that I had been using in that blog, and so Erica asked me, “What would it look like if we did some sort of guide like that?” At first, I thought, “Well, there’s so many guides out there,” but ultimately I was interested, and even better was that they were really interested in recruiting some excellent co-authors, and so from there they brought on Marc and Derek, and so I can pass that off to them, which I think has really been wonderful for the project.

Marc: It has indeed. I really have learned a lot from both of you writing this, and it was kind of a sprint to go through this, and I think as we were writing it, we were both going through our own sort of basic just feelings about AI and seeing how other people were dealing with it too. We’ve been there for the. Last three and a half, four years, and asking ourselves some very deep questions about our own work with this technology, too, where this is coming to intersect, and trying to help faculty tred a path forward.

Derek: I’ll add that, as Rebecca mentioned, I’ve written a couple of other books on teaching with technology, and I think I have a writing voice that works well with Annette and with Marc, and with the goal of this project, a mix of what does the research tell us, to the extent that it tells us anything, what are the big ideas we need to be grappling with, and what does this look like in practice? I think that’s one of the things I really like about this book, is there’s lots of examples of how faculty are adapting and responding to AI in very concrete ways. That’s what I love to do, is to talk to faculty about what other faculty are doing, and help them see how they can adapt that to their own teaching. Yeah, so happy to be a part. And also, I would say I haven’t co-written before this project.

Rebecca: I didn’t know that, Derek. You reveal that now. [LAUGHTER]

Derek: I was a little skeptical. I kind of like to be in my own space when I’m writing, but this has been a really, really enjoyable process of drafting and getting feedback and revising and commenting and figuring out the structure together. I like the co-writing a lot more than I expected I would.

Annette: That’s really nice to hear. I want to say, too, that Norton brought us together, which I think is wonderful, because they just had like great vision of how we could each bring different expertise, which I think was like absolutely true. But the other thing is that I knew Derek and Marc’s work before this, and so I was really, really glad to be able to collaborate with them, and I do think, like BTS or other bands that the studio brings together, sometimes there’s just synergy, and it works. [LAUGHTER]

John: I really do like the title of AI-aware teaching, which I think fits nicely with Annette’s comment that it was somewhat in the middle, not at one of the poles, as many of the other books and approaches tend to be. Before we start talking about AI and teaching with AI, could you explain what you meant by AI-aware teaching?

Annette: So, I can start with that one. As Marc said, we were all in the process of writing this book, confronting our own feelings about AI in complex ways, and I think all of us had been wrestling with this, of course, before the book, because we’d all been working in this space for years, but the fact that I am absolutely an AI user, I find it really interesting. My work is in writing technologies, it’s the most exciting writing technology, certainly in my lifetime. So, there’s all these really interesting feelings, and then as a teacher, there’s these other feelings about students and shortcutting and the future of higher ed, and all these things, but the main thing is, as a technology, I think it’s just really important to be aware of what it is and what’s out there, and what that means is, for AI-aware teaching, it means understanding what the technology is and does, and not, you know, what it did three years ago, but what it’s doing now, and what are the possible future trajectories for it, and that includes all sorts of things about environmental impacts and politics, as well as the technology, and then also what students are actually doing with it, not what we would like them to be doing with it, or what they say they’re doing with it, but what they’re actually doing with it, so that involves a little bit of research and listening, and then figuring out what it is that we want students to get out of our classes, rethinking our learning goals, things like that, and then kind of recalibrating, given the current landscape, how we get to those learning goals that we have had, or revisiting those learning goals, maybe they need to be revised, and that is, I guess, my kind of sense of AI-aware teaching.

Derek: And I’ll add that I feel like there is a lot of discourse in higher ed that seems to indicate there are two camps: either you are refusing to use AI or resisting it, or you’re all in and you’re doing all these things with it, and I like the AI-aware framing, which is to say those could both be reasonable choices, but we need to understand what we’re dealing with, both the technologies and our students and our own teaching goals, and then make informed intentional choices. And I would argue that most faculty, once they’ve gone through that process, land on some mix of resisting AI in some parts of their teaching and engaging with AI in other parts of their teaching, and I think that’s okay, and I think we have a lot to offer in the book for faculty who are any mix of those two poles in how they actually practice AI-aware teaching.

Marc: Absolutely. I find myself sometimes in the middle of an assignment, designing assignment, being drawn into, let’s use AI, this aspect, let’s absolutely resist, refuse using AI in these different aspects, and then thinking about, from the student experiences, how they’re going to go through those same types of things to confront it. I think Annette’s point too is very apt for a moment too. The interface behind ChatGPT has not changed, but the models have underneath it, and the expectations I have from a lot of faculty is that they’ve tested these systems back in 2022/3, or 24 that they have a singular experience using them, and it was not very good or productive. Now things have changed pretty radically. These are not just language models, they’re, in theory, world models gainng the simulations of these different types of experiences, and so we have to be aware of what that is going to do for ourselves and for our students, and there are some, I think, very good benefits that can come out of this, but also be very cautious about it too, just trying to find that balanced pathway.

Rebecca: Can you talk a little bit about the structure of the book? There’s a couple parts to it. Can you reveal the underlying structure?

Derek: Yeah, I’ll take a stab at that. The book has two parts, and like these books, you can read any chapter in any order you want. I never do that. That’s not who I am. I read things from front to back. [LAUGHTER]

Annette: You’re revealing a lot of things today about yourself, Derek. [LAUGHTER]

Derek: People tell me they read books in other ways. [LAUGHTER]

Rebecca: All these exclusives…

Derek: But, I do think part one is meant to be moved through somewhat linearly, and that it walks you through the AI-aware teaching idea, so that we’ve have a chapter on what AI is and what it does. We have a chapter on how students are using AI, as best we can tell from the research and the surveys and the focus groups that have been done. And so the goal of part one is to help you do those aspects of AI-aware teaching, and then hopefully land on an approach to AI that really fits your course and your goals, and to start to shape how you might communicate that approach to your students through policies or guidance. Part two is very much more a random access part. We have a chapter on in-class activities that are AI aware and out of class activities that are AI aware. We have a chapter on AI and reading, a chapter on AI and writing, a chapter on AI for tutoring, and so it’s much more about where do you see AI showing up in the particular pieces of your courses and your teaching, and what ideas can you get to help you make again more informed intentional choices in those particular aspects. We also have a chapter on AI and academic integrity, because that’s kind of the elephant in the room. [LAUGHTER]

Annette: …that’s the first thing everybody asks us at workshops.

Derek: So we put it last.

Marc: It is, yeah. yeah, yeah. You got to get through the book if you want to get to the last chapter.

Derek: Well, I do at least.

Annette: Yeah, yeah.

Rebecca: I like how it was almost an afterthought there, In the way that you’re presenting it, [LAUGHTER]like it really was an elephant in the room.

Derek: Yeah, because I feel like it’s where so many conversations start, but I don’t know that those conversations are always super productive. I think it’s much more productive to get more grounding in who our students are, what the tools are doing, what our goals are, and then once we’ve got some of that infrastructure, that kind of mental infrastructure in place, we’re in a much better position to make good choices about how we respond to academic integrity concerns.

Marc: Imagine that, approaching this from not an emotional standpoint, but one that’s actually based on evidence of those types of things. I kid, of course. I mean, look, a lot of people are at that really gut-wrenching situation three, almost four, years since ChatGPT was launched. I do think one of the goals that I had personally writing this book, too, was to figure out ways to help faculty move past that initial gut reaction of just thinking about ChatGPT, generative AIs, as a cheating tool, and to start thinking about more broadly about when is it acceptable to use this, when it is not. How can we articulate that to our students through our learning outcomes? And what we did, I think, a really good job in the book, too, is looking out at the world of academia, too, and finding wonderful examples from faculty in how they’re actually incorporating a lot of these different types of experiences for their students, and how they’re sort of wrestling with these questions too.

Annette: Yeah, absolutely. I do think that that’s really the most exciting part about this book, is that we feature so many really concrete, tested examples from teachers at community colleges, at research universities, teaching colleges, just like everywhere, and for a long time I ran our graduate training in the English department for teachers, and now I run faculty development for AI, and this book sort of answers a lot of questions of sources that I would like when I’m teaching those kinds of things, like what do I want to assign that gives a kind of theoretical background, and also that kind of trust students, acknowledges student agency, trusts teachers and their expertise in whatever disciplines they’re teaching in, because it is meant to be something that moves across disciplines, and then gives them a whole bunch of examples, and there’s not answers in there, but there’s a whole lot of things to adapt, and so hopefully that’s useful for working teachers.

John: As you’ve noted, one of the concerns that many faculty have is that students may use it in ways that are not conducive or supportive of their learning. And actually, Derek had a recent podcast where he was talking with some people in math, how earlier on it was easy to detect the use of AI, because AI tools often got things wrong, but that’s much less true today, especially for the paid versions and the deep thinking versions of these tools. So, what are some strategies that faculty can use to assess student learning and their skill development in a world where they have access to these increasingly capable AI tools?

[AWKWARD PAUSE]

Marc: Let’s keep the awkward pause in here… [LAUGHTER]

Annette: I do feel that awkward pause. [LAUGHTER]

Marc: I do. Well, I can take the lead here on this one, if you guys don’t mind, then I’ll hand it off. I think strategies are evolving. I think that how we are seeing these tools being deployed and marketed to students, too, is part of our awareness to see this sort of version of it too. AI detection is also changing alongside all these other tools as well. I’ve really leaned in personally for myself, and also included examples too, about emphasizing disclosure when AI is being used, both from the student perspectives, but I think it’s also important for faculty to start having these conversations themselves, too. We are not only seeing students use AI, we’re starting to see faculty now really adopt these in all parts of the course, including in some cases grading. So, if you’re going to use AI in that sort of manner, too, you probably want to think about disclosing it as just sort of a baseline expectation and giving some great examples about what, as I said, in a recent talk, too, there’s performative disclosure, there’s exhaustive disclosure, we give them everything, but then there’s authentic disclosure, where you want to have a conversation, we want to have a dialog about how AI is being used, because in some ways you’re genuinely curious about how this is actually being used by students, and if it can be able to support them, and there’s lots of analytics you can use with the course management system, too, to track student behavior. That’s one thing to kind of be there to just make it visible, but the main strategy there is that you really want to have a situation where you can have conversations with them in some way, shape, or form. That’s very difficult for a high enrollment class, or if you’re teaching six sections, which some of our faculty are doing, and can’t actually have those types of relationships,

Derek: I’ll add that what I hear fairly consistently from students through surveys and panels that I’ve been a part of is that they want some clarity on what the boundary lines are around what’s appropriate use of AI and what’s not, and there’s lots of different ways to try to communicate those boundary lines. There’s the AI assessment scale, which says for this assessment, these are the types of uses that are okay. You can do it at a course level and say it’s okay to use AI for figuring out a hard concept or brainstorming ideas, but you can’t use it for drafting, or you can use it for editing. We’ve seen lots of different examples of how faculty are helping to define those boundary lines for students. In some cases, to Marc’s point about good conversations, they are figuring out those boundary lines with their students through conversation. They’re kind of coming together to come up with good policies around this kind of thing. And I think, though, an important piece of this is that most students want to know kind of what’s allowed and what’s not allowed, right? That’s a big piece of it. But I think students are also concerned about what’s going to help my learning and what’s not going to help my learning, and they are not always in a good position to know what uses of AI are going to be conducive to their best learning. They’re novices, they’re figuring out how to learn. We may have a better idea of that. We may be figuring it out too, in many cases, because AI is so new, but it’s not just about how can we secure our assessments. And from the student’s perspective, it’s not just about how can I stay out of trouble, but there’s a strong element of how can we actually help learning happen in the ways that are meaningful, and figuring out those boundary lines together is one way to get at that shared goal that we and our students both have.

Annette: I think that’s important, that shared goal, because one of the things that we know from what students tell us about their experiences of AI in classes is that they are really frustrated about a lot of what’s happening in that there is lack of clarity in policies, most of what they see in policies is do not do this, rather than here’s some good ways to use AI, and they find that sometimes infantilizing, I think, and unrealistic, and then they also don’t like it when their peers are overusing AI, and that kind of cheapens their own experience and degree and their effort, and so they have a lot of feelings about it. Marc started out by saying, talking to students, I think that’s really the crucial thing, and I think there’s ways you can do that and certainly we talk about that in the book, you can do that in large lecture classes and through asynchronous surveys, all sorts of ways. It’s possible to hear from students in a lot of different ways, but I think that’s super important, and also one of the reasons why I said in the answer to the last question about student agency is that we need to make sure that students are getting what their goals are out of our classes, we have external goals and things that we want them to get out of classes, but they have their own goals, and they have their own interests in learning, as Derek mentioned. So, can we help them align those goals that they have to what we’re doing in the class, and help them articulate how this class is going to help them in the future, because our students are also nervous about job prospects and all sorts of things, and I think the more that we can lean into the fact that we’re there to support their learning, we can help all of those conceptual things. The other thing that we talk about in the book some is the University of Sydney two-lane assessment, which I think is a helpful framework, thinking about, on the one hand, secured assessment, and that is like proctored exams or things where you have a really clear idea that the person who is represented here in this space is the same person you’re assessing learning of this particular person, and then you’ve got a lot of spaces of open assessment where students are doing homework, low key, et cetera, they may or may not be able to use AI. And so, I think that one of the reasons why we get that question all the time, about like, how do you stop students from cheating, or whatever, in workshops, is that the labor that teachers are going through is so significant right now, and nobody wants to do the surveillance and policing, zero teachers want to do that, and trying to figure out how do you actually assess what a student is doing is so much work, and so if we can narrow that space down, and then let students play around a little bit more with here’s possible positive ways of using AI to enhance your learning, in ways that they may not necessarily know already. As Derek said, they want to work on their learning, but it’s not always clear what that looks like, and that’s part of why we have teaching experts in the room, that is us, right? So we’re giving all very complicated answers to this, and that’s because there is no silver bullet. We keep coming back to that in the book. There are no easy answers, and we do not give easy answers in this book, because they’re just aren’t present.

Marc: That’s absolutely right. And she also hit on something that I think we were all really attuned to, is the labor of this going through the sort of response to AI. Doesn’t matter if you want to engage AI to or refuse it to, you’re still going to have to really focus on work, and part of what we’ve been doing too is trying to find different strategies to support faculty, so that’s not so overwhelming, trying to think about even taking some steps from, like, Jim Lang’s small teaching moments to bring up these types of conversations, because we realize that not all faculty are going to have support from wonderful teaching and learning centers. Some faculty are going to be contingent and sometimes not have much support at all, too, and they still are going to want to practice some type of AI awareness with their class and their actual day to day lives, so labor, I think, is a really big one we’ve been talking about as we go through it.

Rebecca: I think one of the spaces we get a lot of questions is on the asynchronous online environment, in particular, because it’s extra challenging to figure out ways to do assessments or to do quick checks for learning in that environment where it’s completely vulnerable to AI in the ways that maybe we have historically done these things. Those that teach in in- class settings have moved to using blue books and some other things occasionally. What advice do you have for some faculty that are specifically in this asynchronous environment that are hitting some slightly different challenges than you might encounter in a face-to-face class.

Marc: I think the best piece of advice I could offer, because I drafted, I think, a chapter that we try to talk about this, is to breathe and not take this personally. It’s not against you, and there’s certain things you can control. Sometimes you have options with your modality you pick as a teacher, sometimes that’s picked for you, but you can also really search for ways that you are more present inside of your online class that makes it more embodied for yourself and for your students. I’m doing that myself now too, with my hybrid and online classes too, trying to be more there with them, having more conversations, and just also making myself more available to them too, because one thing that I think we’ve all noticed is that if you are doing an asynchronous course on autopilot, not only is that very prone to AI misuse, but also there’s a debate too, if you’re not there as the teaching expert, that you’re not there as the person in the room, too, what is necessarily being lost if a student is going to use AI in that context? So that does really reveal a lot of our practice that we’ve gone through to start thinking about it. For the security aspect, too, there are layers of things that you can do in these courses. You can, if you’d like to engage a lockdown browser for some assessment types, secure it. That’s not going to work for everything, but you can also switch over and think about doing a mix of video assignments. I do a lot of work instead of the discussion board in my online classes with videos. I don’t do the talking head thing anymore, too, where they just read a script, which, because that script is generally generated by AI, I have them actually write something or do something else there with, if they’re doing a multimodal project, share their screen and work in real time on the screen too, or they do a recording of that asynchronously, so I want them actually working in practice, showing me that process, because even the AI is involved in that too. They’re still working through those decisions, they’re still thinking about those things. So, there are some strategies we can use. They are going to be complicated. I do think we need a little bit more support from our course management systems to work with us, especially now that agentic AI is here. How many students know about agentic AI, I think is a very good question. I think most of them will associate generative AI with ChatGPT, just like Google is synonymous with search. I don’t think they’ve risen to the point, too, of they’re all going out to use agents to go through and crash their courses. I would actually say that faculty are maybe more aware of that than students are, which is a good thing to start thinking about where this is headed, but it is something to be thoughtful about as we go forward too, because as these systems become more and more advanced and also more autonomous in what they can do with the different workflows, it’s not just gonna be students that are going to be adopting them, we have news that Canvas is going to have an AI teaching agent there as well, with certain limitations and guardrails, but this is just sort of the start of this new version of working with these tools, and we need to start thinking about what sort of boundaries we want to put in place, both for students and for ourselves with them.

Derek: I think for the online space, as you say, Rebecca, the online asynchronous is kind of the perfect storm of AI problems. I’ve been thinking that there’s an impulse to try to solve that problem through better course design. If we can have more transparent learning objectives, if our assignments are more authentic, if we can combine those, so students know here’s why I’m doing this project, and here’s what I’m going to get out of it, and it aligns with my personal and professional goals. Those are all things that are going to help students resist an unhelpful use of AI and lean into the hard work of learning. I don’t think that’s sufficient. [LAUGHTER] I do think that’s necessary. I think that’s good teaching to begin with. I do think we also need to have a cybersecurity mindset that says there’s going to be bad actors who are using tools that are problematic, and we’re going to need help from our IT infrastructure to figure out how we can block things and prevent things. I think there’s sometimes an impulse just to do that, right? Like somehow we can technology our way out of this problem. I really do think we need both. I think we need better curricula that better align with students’ goals in more transparent ways, and we also need to think about the technology tools that we’re going to use. So, one of the metaphors I think I shared with our author group was the notion of a locked door, so I have a locked door in the front of my house. It’s my understanding I haven’t tried this, it’s my understanding that with about an hour and a few simple tools and YouTube, any one of us could figure out how to pick the lock on the front of my door. The technology itself is not super secure, but it communicates to anyone coming by, you’re not supposed to just walk in my house, you need to ask first, and so sometimes it’s tempting to think about the kind of anti-cheating technologies as like a secure barrier, like we’re going to keep out all of the cheating, we’re not going to keep out all the cheating, we’re going to keep out some of the cheating, but we’re also going to communicate what those boundary lines to students are a lot more clearly to say these are signals we give to students to say don’t do this and please do this instead. The research on academic integrity says most students don’t cheat because they’re evil people, [LAUGHTER] mostly it’s because of the current environmental situation that they’re facing. And so if we can create those signals and boundary lines that help students make a better choice in the moment, I think that’s a lot of the good that we can do in this case.

Annette: I love that metaphor of Derek’s. I think it’s really helpful, but one of the things that signals is not only to the students who would be cheaters, who might, if it were just a little bit easier, make a different decision, but it’s also indicating to students that their fellow students are getting the same message, and so there is a “here in this space we respect academic integrity, and that means you should also feel comfortable doing the real work and leaning into the hard work of learning,” as Derek said, because they do feel like suckers if they’re doing that hard work and their peers are not, even if it’s not on a curve, you know, whatever, there’s like that deep inherent feeling of unfairness, so I think that’s really crucial. Marc brought up the point of agentic AI, and how that might be more of an issue in online teaching, and that maybe faculty are more aware of it now than students, which is maybe a good space for us to be thinking about that. One of the reasons why I feel like I’m not as worried about agentic AI is that it is so clearly an academic integrity violation. Something else is impersonating you and doing the work of you on a platform, which isn’t to say that it won’t happen, of course it’s happening even now, but that casual, like, is this or is this not against the policy? It’s really against the policy you survey students, and they’re like, “No, flat out, that is just absolutely wrong,” versus, “well, this policy says no AI, but it doesn’t go into details, and I did some brainstorming, but I didn’t use it for writing,” like that is “who knows, like maybe,” they weren’t really specific. So I think that’s more the space that when we talk to students, we can help them think through, “Okay, this is why I really don’t want you to use AI for brainstorming… for these specific reasons here, and maybe it’s good in other contexts, but not here.” And so this is why I think agentic AI, certainly we should be paying attention to it, but I just do not envision a future in which all college students are absolutely botting their way through their degrees.

John: You talk about a number of strategies that can be used in terms of getting student buy-in to reduce the excessive reliance on AI, but are there any other strategies that you can think of that might be able to deter students from using AI in ways that are harmful?

Derek: So, I can think of a couple more. We’ve talked about the big ones around having transparency with students about learning objectives and what they’re going to gain out of this learning experience, we’ve talked about more authentic assignments that kind of connect more with students’ interests. I think a lot of faculty are experimenting with alternative grading schemes that are changing the motivational landscape for students and giving them more opportunities to revise and resubmit and try again. That’s going to take some of the high stakes away that might lead students to take shortcuts. Again, not a silver bullet, but it’s part of those environmental factors. We talked about online courses, and it’s easy sometimes to forget that even in online courses our students are humans with bodies in real physical environments, and so I’m seeing some faculty start to think creatively, like Marc was saying, about how could we do some process tracking where they are doing some work, and it’s recorded, or are there assignments that are going to get them out of their laptop zone and into the real world to do something that’s connected to your course? Again, not a silver bullet, but adding that to our mix of engagements, I think, can help students shy away from shortcuts. And the other thing I would say is, I referred to this earlier, students are learning how to learn when we encounter them. We are probably better learners than they are, maybe not in all cases, but we’re experienced academics, we’re pretty good at learning. AI is changing how we approach learning in our own disciplines, but what I would argue for is giving students more opportunities to do some metacognitive work, reflecting on their own learning in our courses, and thinking about how they used AI. Was it helpful? Was it not helpful? They had to read a hard chapter this week. What were the strategies they used to make it through that hard chapter? Which strategies were more or less helpful for that? And some of those strategies may involve AI, and some may not. Our students are going to go, and they’re going to have access to AI tools when they leave us. Hopefully, we can equip them to use those tools with some level of discernment, and that involves some self knowledge and some metacognitive thinking, and so I think we should start to build those opportunities in our courses for students.

Rebecca: Well, you’ve teed this up nicely, Derek. Thinking about the workplace and how AI is used in the workplace, are there ways that we should be rethinking or adjusting our learning objectives for our courses to either cycle out some skills or particular things that maybe were in there before, and put in some new things. I think at the top, Annette, you talked a little bit about a moment to reflect on [LAUGHTER] learning objectives and potentially take it as an opportunity to revise.

Annette: Yeah, absolutely. Derek used the word discernment, and that’s a word we really hooked into in the book, is thinking about how students can exercise careful judgment about when to use AI, when not to use AI. We all know a dozen examples of contexts in which people have used AI in places that are absolutely inappropriate, either for human means, either for signing off on something that shouldn’t have been signed off on, hallucinations, inaccuracies, all sorts of things, and I feel like my goal is to have students [LAUGHTER] not do that, like don’t put us in the headlines, you know,[LAUGHTER] which I think is something that goes over well with administrators at any university. But, really it’s like, how do you make good judgment on what AI looks like when you go into the workplace, and there are so many workplaces that really are leaning in, and it is kind of unregulated in a lot of ways. It’s surreptitious, it’s tacit, it’s the secret cyborg that Ethan Mollick talks about, and so I think it is really important for students to not just like, “here’s how you do prompt engineering,” but something that’s much more sophisticated about how are you supplementing the knowledge that you have when you’re using these tools.

Marc: Yeah, the goal with discernment, too. I mean, I’ll just give you an example, too. I just spoke with our law school, and they are getting a lot of interesting signals from the legal industry about how they want AI to be used and not used. You can actually go on to the AI hallucination case database. Someone’s actually set this up. There’s 1300 hallucinated cases, along with the sanction that the judge, who, of course, was very upset, found out AI was used in a way that created fictitious case law. So there is that issue too, of using AI inappropriate.

Derek: And just to clarify, these are real lawyers who have submitted briefs to courts where there were hallucinated content in those briefs, and they were caught on it and called out and punishment. And there’s a website that tracks all this, and I think it’s just a weird, strange world we’re living in, where that continues to happen, and someone is tracking all of that.

Marc: Indeed, yeah. And to make it even stranger now, the opposite end of the spectrum is they’re hearing from a lot of corporate interests too, is that when they go to bill them for their hours, they want to have AI listed there, not as disclosure for hallucinations, but how much money did you save us by using AI, or did you use enough AI. So there are some really wild mixed signals, depending on what your discipline is, about what your industry is, as well, too. And helping students navigate that is a big challenge. Helping faculty now navigate those questions, too. It’s like, okay, do I need my students to learn about AI to avoid hallucinations? Oh, wait, you have to learn about AI because you’re being billed for it if you don’t use it enough. So, it’s really fascinating just to see this play out. That’s just one example. We have this in different industries too. I’ve talked with folks in hospitality management. AI is a massive tool in that industry right now, and they’re having some issues that they’re doing this with too much AI for the audience and upsetting them too, but also, having that same sort of situation too, where your company you’re working with, or you’re supposed to be working with, is demanding you use AI to cut overhead. So it is going to be a challenge to navigate, for sure.

Rebecca: So, are we revising our learning objectives?

Derek: I think, to Marc’s point, I think it depends a lot on the discipline that you’re in and how things are shaking out. So, like in computer science, the goal of learning how to write basic code is less important now. The goal of figuring out how to evaluate the quality of some other code that you’ve been given, whether it was written by a human or an AI or not, the kind of reading and evaluating code that’s more important now. Over in a writing class, though, writing the first draft is kind of the equivalent to drafting that code, right? Is writing the first draft not important because AI can do it? I don’t think so. I think the ideation and brainstorming that happens during that first draft is super important, and so similar moves with AI in different disciplines may be thought of very differently in those disciplines because of kind of what’s under the surface there, and so that’s why I think a lot of these conversations, around learning objectives in particular, need to happen within the disciplines and within the fields, particularly if those disciplines are having some conversations with how the workforce is changing.

Annette: One of the things that we talk about in the writing chapter, to kind of extend a little bit what Derek was talking about, is that there are different important aspects of the intellectual process across different aspects of writing, so I teach in an English department, and that means that that ideation, the brainstorming, the writing is kind of the argument, and that’s the work. I also spent eight years working in a neuroscience lab, and in that lab I did some of the writing. That was inconsequential, because, like, I didn’t design the experiments, I didn’t, I mean, sometimes I actually did sleep deprive [LAUGHTER] the rats, but I didn’t really do the work, the intellectual work of the lab, and so I didn’t get an authorship credit, rightly so. And so when we look across disciplines, it is really important. This is why we keep saying in the book, we trust you to figure this out, is like you have to think about like what is the intellectual work that’s happening in your class and in your discipline and for your students, so that you can pinpoint that intellectual work and make sure that it’s enhanced or at least not undermined by AI. And then maybe, such as what Marc is talking about with the legal profession, you need to integrate AI or help students think about how they might thoughtfully do that, because they’ll need to have that skill moving forward,

John: And by connecting to what people in the fields where students are likely to go into when they graduate, it can create more authentic learning opportunities where students are using AI in the same way they might be expecting to use it later, I think. We’ve talked a little bit about all the concerns that faculty have, but there’s lots of ways in which AI might be useful to assist with student learning. Can you talk a little bit about some ways in which AI tools can be helpful in increasing student learning?

Derek: So, I’ll jump in. I’ve had a journey on this, because I feel like when I started writing with these two, I was the like all in on AI, and let’s use it to augment our teaching and then create new learning experiences for students. The more I went through the writing process, the more I’ve talked with faculty this past year, the more mixed I am, and the more kind of avoiding AI piece has been a much more important to think about, like we do want to protect the space for students to learn in meaningful ways. That said, here’s my current metaphor for thinking about this. [LAUGHTER] You’ve probably heard of the forklift in the gym metaphor. If you want to move weights around in the gym, you don’t bring a forklift in and move them, you go in and you lift your weights. That’s the point of the exercise, is for you to do it and to let your muscles get that strength training. Yes, the forklift will move the weights, but that’s not the point. Some uses of AI are like that, they are doing the work for students, they are moving the weights for students, and the students aren’t getting anything out of that. But I would argue that there’s another machine that actually you do find in gyms, which is the treadmill. I’m a runner, when I want to run and it’s 35 degrees outside and raining, I don’t want to run outside, I want to go to the gym and run on a treadmill. The treadmill is not doing the work for me, but it is creating conditions under which I can do the work, and so those are the uses of AI that I was initially drawn to, and I continue to be drawn to. How can we use AI in ways that are going to create new learning opportunities for students without robbing them of the exercise themselves? And so that’s things like having students critique the output of some AI chatbot that’s doing some task associated with your course. It gives them a chance to practice the knowledge and skills they’re building. This can be a tricky assignment if the AI does too good of a job in whatever you’ve asked it to do. There may not be enough to critique, but lots of faculty are finding places or particular AI tools that still have kind of big flaws in them. We have one example in our book of a math faculty member who had an AI generate flawed proofs to mathematical statements, and the student’s job was to evaluate those proofs. That’s a great opportunity for practice. Other faculty are having AI give feedback to the students on their work, and I think you have to do that in a thoughtful way, so that students aren’t just kind of taking the AI feedback. As gospel, that they are in a position where they can question that, maybe compare that to feedback they’re getting from their peers on a particular assignment. You also don’t want to kind of abdicate your own role of giving students feedback, so thinking of AI-generated feedback as one step in a process that later involves you giving feedback to your students. But well-designed prompts to AI can generate really useful and actionable feedback for students, and so that’s a treadmill kind of thing. And I guess the third big category I would mention is custom AI chatbots that are designed to simulate certain types of interactions or conversations for students, so that they can practice talking to a client or talking to a patient, or what have you. That’s a treadmill kind of use of AI, and a lot of faculty are having a lot of success with that, in part, because the fuzziness and unpredictability of an AI chatbot becomes a feature, not a bug, of that type of activity. The fact that it’s unreliable and contradicts itself is okay, because that often happens in the types of professional settings that we’re preparing students to enter.

Annette: One additional thing I think teachers are doing, which is super exciting, and I saw evidence of this in the faculty development seminar that I teach at University of Pittsburgh, is teachers are starting to basically vibe code their own apps that are supporting learning, which is just amazing to see, actually, because here is this opportunity to create this nichey little thing that is not going to be served by some big platform and subscription blah blah blah, it’s like I teach this particular really difficult concept. It draws on these different databases and government websites, and blah blah blah. We always have trouble with it. Students struggle to put this all together. It’s really hard to visualize, etc. Or you can ask Claude to make up a cool interface where students can play around with it, and then actually really get a good sense of this complex thing that I try to teach. Or here’s this visualization thing in a science class of we’re looking at a sample on their microscope, and you can change it into a diagram, and that can make it a little more easy for students to tell what it is, label the parts, things like that, and those kinds of real-time transitions across modes, say from image to diagram, or the interfaces. I hope that we see an explosion of those kinds of teaching tools that teachers are able to innovate with using AI on the back end, then, so, like, students aren’t directly using AI, but they’re interacting with the results of AI.

Marc: Yeah, I think for me the easiest way to start is to define AI’s role in the sort of relationship you have with your own teaching, and also set that up for your students to give it a specific role, develop rapport with your students, that level of trust as you go through this to see if this is effective in their learning outcomes. I am a little bit concerned about the fact too that some students have very expensive subscriptions to certain AI tools and services, while others are using the free plan, and what that’s going to do for assessment, and also equity and accessibility issues too, going forward. But you only get to find that out if they’re talking to you about this, if they’re doing this openly, and so when you have that rapport set up there too, like I’m doing right now, my advanced class about AI and society, my students are now given free rein to design things that they like with AI, after knowing about it too, and showing me and teaching me how they’re using these tools to learn, which I think is really cool, and as Annette mentioned, too, a lot of them are vibe coding now with Claude, which is just wild. When we were doing this six months ago, too, Claude really wouldn’t do a very good job. You do some very simple sort of things. Now we have some students vibe coding entire simulations of video games that are pretty impressive, and actual apps too. And teachers can do the same. So, once you’re comfortable with it, once you have that rapport there too, you can really start going into the creative possibilities about what AI can do to help. The challenge there is that there isn’t kind of a limit to it. It’s very hard to basically say where this learning stops with the technology when you can design so many different things based off of your imagination, and that is something to just kind of be thoughtful about, too, because it is a lot to just kind of press faculty on as well, especially if there are just one or two different use cases. Well, it’s this technology’s like clay, throw it on a pot, you can really turn it into anything that you want. And my response from some people have been like, “that’s a lot to take in,” like, “yes, it is.”

Rebecca: Just don’t forget to ask for accessibility features when you’re vibe coding,

Marc: Exactly, exactly. It’s a big thing.

Rebecca: So, we’ve talked a little bit about, well we’ve talked a lot about ways that generative AI might be able to be used to enhance student learning. In some of those discussions, we’ve talked a little bit about how it might also support teaching. Do you have any additional examples on how AI might be used to enhance our teaching?

Marc: Yeah, I’ll give you one that I’ve been using personally, that, like many faculty, I think I was in a bit of a secondary sprint here to try to bring my courses up to Title II compliance, and we got a reprieve that was sort of last minute, but it’s only a year-long rep, so we are still going to have to make sure our additional materials are fully accessible to be compliant with federal law. And I actually used a version of Claude to go through some of my course materials, and set it up to look at the different accessibility guidelines. I thought I’d done a very good job six months ago, and it turns out that no, I missed quite a few things, and it was actually very good to have that sort of experience for an AI tool to go through and sort of check your work. So, I think having AI as a tool that could sort of audit certain things – accessibility is one of them too – but also going through some of my course learning outcomes and seeing the ways that an AI system might look at that and compare it to a different university’s course learning outcomes. How up to speed am I and my peer institutions, and give me some options. I really do think if we were using AI iteratively, we can kind of make sure that we are the ones that are staying in the driver seat, if you will, and really center both not only student agency but in this case, teacher agency, to make sure that they’re making the right choices, and giving yourself some good options with AI to go through that process can be very helpful, and in my case, very eye-opening to me about these types of situations. I will say, my course, I had really done is one of my summer courses that I had not run in about two years. [LAUGHTER] I was just like, “oh yeah, I did that,” and no, I did not actually revisit that for accessibility concerns from two years ago. So AI can do quite a bit.

Annette: Yeah, like Marc, I’m very excited about the possibilities of AI to augment all of the details of teaching that we do, not the kind of human connection and the grading and the assessment and the reading and that kind of thing, but I think Marc’s example about how to make a course more accessible is excellent. The other thing I’ve been using right now is we have an institutional subscription to Claude, and I’ve used the MS 365 connector now, which basically hooks Claude up to my Outlook on my email, and what I found is having semantic search on my email is like a game changer. It’s just like I can ask questions of 15 years of emails and folders and all sorts of things, but I can ask questions of it that help me summarize conversations that are revived. It helps me right now think about my year-end report, like reporting that we have to do. How much time did I spend and what did I forget about the things that I did? Inevitably, you always forget things, and Claude is helping me locate those things right now. I have colleagues who have used Claude to design things that help fix the somewhat broken scheduling system that they have to deal with. It’s all these things that are super essential to the infrastructure of how a university runs, but there’s a lot of technologies that are either too big, clunky, they aren’t specific to whatever things that we need to do, and I think that I absolutely agree with Marc that the more we’re practicing these things as faculty, the better we are at understanding their limitations, their possibilities, and these are things too, like if I had a secretary, then I guess it would be replacing my secretary, but I don’t. This isn’t like undermining the intellectual work that I do as a professor. It’s not putting somebody out of a job, but it is really making my reporting more accurate and better. And so I think it’s important to recognize those possibilities.

Rebecca: I think that gets back to something you guys were talking about earlier, and using it to do some of the tasks, but it’s not the intellectual work of being a faculty member.

Marc: It’s also not the relationship aspect, too. That’s the big thing that, to me, again, part of AI awareness is just where are you going to put some boundaries in place, too. I use these tools all the time, like Annette does, but there are certain things I don’t want to do with my students. I’ve told them I’m not going to do this to write letters of recommendation, I’m not going to use it to answer emails, I’m also not going to use it to grade. Those are my sort of boundaries. Other people are going to be very different what they set up, but to me the best thing to do is just put yourself in conversation with that and start considering where you are going to draw the line and why.

Annette: And the other thing to note, too, that what I love about the way that Marc is talking about this is he’s really open with his students about that, like I’m not going to use it to grade, I’m not going to use it to do this, and I do think that as we ask our students to be transparent with their uses, it’s really important for us to be transparent with them about the uses that we have of AI, and I think they have some suspicions, they are frustrated, they’re interested. The students have all sorts of feelings about faculty uses of AI, and we need to take that into account when we use them. But I don’t think the students would care about me using AI in my email, [LAUGHTER] not for the writing of emails. I don’t use it for the writing of emails, but like for searching, like what are all the things I did for this committee for my department.

Marc: Now they’re not going to care about that, they’re going to care about the writing of emails. I agree, and you know the big thing to think about too, and I know we’re kind of dragging on the question, but this fall it’s going to be four years that high school students have had complete access to ChatGPT to generative AI coming forward. They’re not just going to be Plato exiting the cave, they never were entering our classes. They’re going to have experience using these tools, resisting these tools, and having that. And having this sort of conversation with them too, I think, is really important to think about their own agency when it comes to using or not using AI.

John: And the things you’ve discussed are ways where we can free up some time, so that we can have more time to devote to students, but there might be also some ways in which we could use AI directly to perhaps improve our teaching. One of the things I’ve done with faculty is have them use AI tools to help put their assignments in a TILT framework to make it more transparent to students, because we know what we mean when we ask students to do an assignment, but they don’t always know that, and that can be a nice way of evaluating it, to have an AI tool read it from the perspective of a student and put it in terms that may be meaningful, or perhaps to evaluate a syllabus for inclusive language and things that might get past us without the use of an AI tool, or use it to suggest some active learning exercises if we were mostly doing lectures, for people who want to move away from that, but aren’t quite as comfortable.

Marc: Yeah, absolutely. And there is an element of humility in that, too. You’re asking a system to help you perform your job, and that is something that I’ve talked with a lot of faculty about, that for them, they’re like, “no, I’m not going to do this,” but like, try it. Here’s an exercise, see if it is. And generally, there is that sort of aha moment when we do this in a workshop. They’re like, “oh, wow, so yeah, this is actually kind of helpful for me.” This is something I might take back and show to my colleagues and help them understand what’s going on too.

Rebecca: So we always wrap up by asking, what’s next?

Derek: I’m excited that the book is coming out this summer. That’s a big what’s next for me, I think, to help support that and to continue these conversations. I’ve been reaching out to a lot of the faculty whose work we mentioned in the book to have them on my Intentional Teaching podcast, and so I’m excited to continue some of these conversations in this audio format this summer.

Marc: Like Derek, I’m very excited about the book too, trying to plan what level of support that I can bring to faculty from a lot of these examples that we go out there to do lots of workshops and also lots of different engagements, both internally here at my university and also externally too. So happy to see that basically pop up and be out there in the world too, starting this summer for the ebook, and then the print book will be out at some point in time, early in the fall.

Annette: I’m excited, also like my co-authors, about the book being done. It’s always a big project, as Marc said at the beginning here, it’s a real sprint, it was a really intensive timeline. To take a commercial publisher and get something from conception to publication in less than a year is unbelievable, which is kind of the pace of AI, and it shows Norton’s priority for the book, too, which I was happy about. So, yeah, what’s next is, well, I’m working on an academic monograph about a long history of AI, and so I’m excited to do that. I feel like we learned so much in the process of writing the book, like all of the different worked examples from the teachers. I really am so grateful for the teachers that were happy to share those examples with us and with other teachers through this book. What I’d really like to do is I’ve been integrating some of those examples again with permission and workshops and things like that, and then to do that work at home too at Pitt and think more cutting edge about what we can do with the technologies that we have access to locally, and how I can work with the incredible colleagues that I have here at Pitt, and the students, how we might further student research too, which I’ve been working on here at Pitt too. So I’m excited about that next chapter, and I really am excited to learn more from other teachers, especially with the new innovations with vibe coding and all those kinds of experiments. I think we are on the precipice of some other hopefully interesting pivots in the ways that we approach education.

John: Well, thank you for joining us. We really enjoyed the conversation, and we’re really looking forward to seeing the book when it comes out. In fact, we’ve listed it as a reading group we’ll be running on campus in the fall, so we’re especially looking forward to seeing the final version.

Rebecca: Well, thanks. This was really great.

Annette: Thank you so much.

Marc: Thank you, guys. I appreciate it.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing Assistance provided by Madison Lee.

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441. Teaching = Coaching

Coaches and teachers work to develop skills by providing structured learning environments, motivational strategies, and individual feedback. In this episode, Christian Fauria and Constanza Bartholomae join us to discuss the similarities among coaching, teaching, and the work of educational developers.

Christian is a former NFL tight end who played 13 seasons and won two Super Bowl Championships with the New England Patriots, and a national champion at the University of Colorado. Following his retirement from football, Christian transitioned into sports media, working as a college football analyst for ESPN and CBS, while co-hosting a radio show on WEEI in Boston. He is now a Professional in Residence at Bryant University in the Communication and Language Studies Department, teaching courses in sports broadcasting and organizational leadership in sports. Constanza is the Interim Director of the Center of Teaching Excellence at Bryant University.

Show Notes

Transcript

John: Coaches and teachers work to develop skills by providing structured learning environments, motivational strategies, and individual feedback. In this episode, we discuss the similarities among coaching, teaching, and the work of educational developers.

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John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

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Rebecca: Our guests today are Christian Fauria and Constanza Bartholomae. Christian is a former NFL tight end who played 13 seasons and won two Super Bowl Championships with the New England Patriots, and a national champion at the University of Colorado. Following his retirement from football, Christian transitioned into sports media, working as a college football analyst for ESPN and CBS, while co-hosting a radio show on WEEI in Boston. He is now a Professional in Residence at Bryant University in the Communication and Language Studies Department, teaching courses in sports broadcasting and organizational leadership in sports. Constanza is the Interim Director of the Center of Teaching Excellence at Bryant University. Welcome Christian and welcome back, Constanza.

Christian: Thanks for having us. It’s my first time. I’m excited.

John: It’s great to meet you. Today’s teas are:… Christian, are you drinking any tea?

Christian: I do. I call this my hearty mug. I got a little green tea in there. Nothing but the tea, no sugar, no nothing, no additives. I keep it clean. That’s what I got this morning.

Rebecca: That’s the way to do it.

John: And Constanza?

Constanza: I’ve got my favorite tea. It’s African Nectar from Mighty Leaf. And if you’re someone who doesn’t like tea, or you’re up for an adventure, this is the tea to try. I have convinced many folks who are not tea drinkers, that tea is an option with this particular one.

Christian: Nice.

John: And Rebecca?

Rebecca: I have a Scottish Breakfast tea today, John.

John: An old favorite.

Rebecca: It is.

John: And I have a Lapsang Souchong today, a smoked tea.

Christian: Ooh, fancy.

John: it is. It smells like there’s a wood fire right next to me, and I’m hoping that’s not the case, because I am in an office building here on campus.

Rebecca: We invited you both here today to discuss the working relationship that you’ve developed at Bryant University. Can you each tell us a little bit about how you began working together?

Christian: Okay, so I’ll make a long, long story short, because this will take about an hour. So like you mentioned in the intro, played in the NFL, worked in media forever, got fired some random day, and then just kind of walked around trying to figure out, like, what was I going to do next? I had no idea what I was going to do, and I decided that I wanted to teach. I never wanted to coach a sport other than my kids, because coaches in the college level and in the pro level, they have no life. I wanted a life, so I decided that, with some input from my wife, I said, Yeah, let’s do that. So what I did is I created a syllabus about broadcasting, things that I thought were important. I sent it out an email to like, 10 different schools here in the Northeast region. The only school that did not reply to me was Harvard, and then all the other nine replied to me, and then I just narrowed it down to Bryant. And I’ve never taught before other than a sport, so I was really kind of lost as far as how I should go about it. I knew what I wanted to say, but I didn’t really know how to form it. I didn’t know how to create it. I didn’t know a grading scale. I was very, very green. And when I found out about the CTE center, I met Constanza, and she was like a beacon of hope for me, because she really just explained things as simple as possible for me to understand them. And not only was she good at the technical side. I thought that she was really good at the emotional and physical side as well, because I had no real belief in myself that I could do it. I know I could say what I wanted to say, but I didn’t really know if I could actually get my message across. And then I hijacked all her time, because that’s what I basically did. I hijacked all her time. I would come in there unannounced. I would ask her at the last minute: “I’m stressing out. My class is in five minutes. What should I do?” She was always available. She was always encouraging, she always motivated me the right way. And for me, it was like she was doing all the same things that my coaches did throughout my 13 year NFL career, my five year college career, and then my high school career. It was this exact same thing. It was just a different message, a different tool that she was using, as far as, instead of, like blocking and catching, it was rubrics and teaching philosophies and structure. So again, to me, she became such a good, valuable asset, and then she just became a good friend. So that’s my approach to how we met and how we started working together.
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Constanza: And on my end, Christian’s department chair said, “I’ve got someone for you to meet. We’ve got this new faculty member. You’re going to love him. And oh, by the way, he used to play for the Patriots.” Now, what his department chair didn’t know? What Christian didn’t know is because I grew up in a family where my dad was European and not into American football, didn’t understand it. And my mom also did not watch sports. I knew nothing about football, like I’ve heard of the Patriots, of course, I live in Massachusetts. I didn’t know who Christian was. I didn’t know how famous he was, what his reputation was. So in comes walking this very energetic and excited human being. And again, I had no idea about anything about who he was or what his subject matter was. And I went home and I said “I had a really cool teaching consult today” to my husband, and he’s like, “Who is it with?” And I was like, “Christian Fauria? And he’s like, “No way. I used to listen to him on the radio on the way home from work.” And I was like, “Oh, that’s cool,” but the more that we interacted, the more I got to know Christian and I was like, “Wow, what a great hire. You’ve got someone who’s so full of life and personality and experience,” and although a lot of times what will happen in the Center for Teaching Excellence is that folks will come in and I don’t necessarily have the same subject matter expertise as they do. I’ve never had, clearly, a career in football or in sports broadcasting. I get to hear about what matters to them, what makes them excited, what they love to learn about. And then we get to talk about, okay, how do we convey this over to your students? And then on top of that, as you both have seen from us talking with Christian he’s just really, really funny and just uplifting and human sunshine. So that was an added benefit. Yeah, for sure,

John: Christian, your experience of coming in without much teaching experience or without much of a background in effective teaching practices is something that’s pretty common for many faculty, but your reaction to it was a little bit different than most faculty. Most faculty just tend to rely on what they had experienced when they were students, and that’s not always the best way of preparing for teaching at the college level. What made you decide to approach the teaching center? Was it a suggestion from the chair, or was this something that you just wanted to find out more about before getting into the class?

Christian: So I had an idea of how I wanted to approach the class, but I graduated college in 1995, so it’d been a long time, and I never really had any professors that I remember making any sort of impact on my life whatsoever. The impact that was made on my life was always from my coaches. So I really wanted to find a way to coach the students or teach the students like I was coached and I didn’t know what I was allowed to do, I didn’t know how far I could go, and that’s why I came to Constanza. I was like, “Listen, this is kind of my idea,” so I really relied on her a lot, to mold my class and to guide me through how I should do things and why I should do these things. Because I really wanted to create, like, first day standards. I wanted to really hammer that home, and I really want to let them know that this is not your ordinary class, like what you’re used to. We’re going to turn that on its head. So yes, that was kind of my approach in the very beginning. Now it’s changed over time, but the initial idea was just that, like, here’s all this information I have. It reminds me of like someone who waits for the last minute to do their taxes, and they got, like, receipts everywhere, and then Constanza’s my accountant. I said, “Here, fix it.” “Okay, how much time do I got?” “20 minutes.” That’s kind of the way I was. And I never left there, I never left her office, scared. I always left her office with not necessarily a pep talk, but just a reassurance that I was doing it the right way, and I shouldn’t listen to the insecurities in my head about what I thought the students were going to think about me or say about me while I was saying it.

Constanza: I think to add on to that, we all need to feel like the work that we are doing matters and is important. And I think that that’s really, if there is a secret sauce to teaching, it’s okay, how do we work with faculty to remind them about why they’re here and why what they’re doing matters, especially in an age where we’re talking a lot about a lack of focus or attention or disengagement. I was listening to Jessica Riddell talk at the AAC&U annual meeting, and she said, “I’ve been thinking a lot about the time that we’re in, and I don’t know if necessarily it’s disruption, but a rupture in the educational system, first with the covid pandemic, then with the advent of generative AI, and so there’s a lot of reworking going on in the teaching field right now, trying to really explain, like, what is our teaching philosophy? Why is what we’re doing important?” And so I think that working with faculty in the educational development space, like how I’m able to work with Christian is such an honor, because we really get to help them re- explore their passions and think about how they can translate that over. And one thing that I noticed about Christian right away is, as he said, he cared a lot. I know he’s made comments and said, “How can I do this in 20 minutes?” But actually, we were meeting so much more frequently than that, and he was thinking things through and planning out his whole semester. And so in certain ways, I was like, “Okay, well, we’re in January. Let’s not worry about April for a little bit, but let’s focus on the next few weeks.” I think, in that sense, I knew here’s someone who cares about their students, their outcomes, but also their well being, and that is someone I can really, really work with, because ultimately, at the end of the day, I tell my students, I care about you learning content, but I care more about how you feel in the classroom. And that was Christian. He’s like, “I’ve got the content in my head. I’m ready to go, but how do I make sure that it lands in a way where students are able to accept and interpret it, are able to digest it, and are able to show me that they can demonstrate understanding and meaning from it.” So it was really like the educational development perfect connection, if you will, and reminded me also of why I do what I do. So it had that impact as well.

Christian: Now, that was well said. That’s exactly how I feel about it.

Rebecca: Coaching is really relational. I’ve been observing my daughter, who is ice skating and working with coaches. And the way coaches work with individuals is often different depending on their personality and who they are. So I’m curious how you see the relationships between coaching and teaching, because I know that I’ve been observing successful coaching and thinking about, “Hmm, what is that secret sauce, and how does that translate over to teaching?” And some of it is like this coachability piece, is this willingness to accept some feedback, or wanting to have a good impact on the learner, whoever that learner might be.

Christian: I’ll go first on this one. So there’s an old saying in the sports ranks where the players don’t care about what you know until they know that you care. And I would say that’s exactly the same way, at least that’s my approach to it. And it looks different for every teacher, every coach, and I would say it looks different for every classroom, because there needs to be expectations, there needs to be trust, there needs to be accountability, there needs to be passion, there needs to be knowledge, all that stuff. It’s all the same thing. I’m telling you. It’s no different from coaching and teaching, at least from where I sit, because I’ve seen it now, and I’ve only been there for over a year, and I know that I have to show some sort of appreciation for them showing up, trying. It doesn’t mean I let them pull a fast one on me, because it goes both ways, and so I echo that sentiment to a T. And the old way of doing things in sports overall is gone the way of the typewriter. Nobody is hardcore authoritarian. It’s more transactional now in sports, based on all the different ways athletes go to school say, “I’ll give you money if you do this.” That’s not the way it is in the classroom, but I would call it, and I don’t know if I can curse on this show, there needs to be a high level of a give-a-shit factor, like you have to give a shit about what you’re doing and what you’re saying. And I think it’s very, very obvious to the students, they’re on to you quick, so you can’t pull a fast one over them. And Costanza, that’s kind of the way I feel about it.

Constanza: I think, as teachers, we’re helping students find meaning in whatever subject matter we’re teaching. And Arthur Brooks is coming out with a new book next week titled The Meaning of Your Life: Finding Purpose in an Age of Emptiness. And so Oprah interviewed him last week, I think, and I listened to the podcast episode, and I was like, “Tell me more. You’re a professor and you’re studying the meaning of life. What is that?” And so for him, his definition of happiness is combined with three things: satisfaction, enjoyment and meaning. And he found in his work that meaning is where folks under 30, especially college-age students, perhaps we’re struggling the most, and so I think our job as instructors is to deliver that sense of meaning in our subject matter and really connect the dots to students now more than ever, in an age of generative AI, where folks can look up information and disinformation very easily, we need to show them the humanness behind it, the application behind it. It’s so important. The same happens in the educational development space and in the coaching space. If someone feels like they do not matter or that you wouldn’t notice them if they were absent in your class, they are far less likely to be engaged. It’s very important in my work that in the space that I’m in, where faculty come in and they are vulnerable, they’re usually talking to me about something that isn’t working. I do get folks come in and say, “This worked great. I’m so excited.” Or occasionally I’ll get someone that’ll say, “I want to upend my classroom. Let’s totally do things differently.” But usually they’re coming to me because they’re struggling with something in the classroom, and it’s really hard to be vulnerable if the person that you’re speaking with doesn’t know you or isn’t willing to be vulnerable with you in turn. So I always talk about my teaching failures, or even just to the degree that I feel comfortable, of course, and even just things that I’m going through in life at the level that I feel comfortable with, because I feel like the faculty that I work with need to get to know me. We need to develop a relationship of trust, and then we can talk about all right. “Well, here are some things that I think might need to be changed.” So that if I’m giving them feedback, even if it might be not exactly what they wanted to hear or might be difficult, they know, okay, the person who’s talking to me on the other end of this conversation cares about me as a human first and as an educator second, and they then feel more comfortable letting me know exactly what’s going on without having to sugarcoat it, or they feel comfortable walking in my door in the first place. So I think, in that sense, if we’re working in teaching centers to coach faculty on strategies that they can then use to coach students. It’s really developing what Peter Felton and Leo Lambert talk about in terms of relationship-rich education that expands beyond the classroom space that really makes a campus community full of that. I also think too, reminding ourselves that just like students who are first year that come onto campus and are new, our faculty who are new are experiencing that too. Christian is a very energetic and outgoing person, but it was his first year on campus. So like, how do we connect our faculty to different folks on campus that they can reach out to if they’re in need of support or their students are? How do we let them know about what’s available to them to make sure that they feel supported and seen? It’s not something that we always talk about, but establishing that for faculty is really, really important in faculty development work. So I think our job really is to connect and to help folks discover or rediscover their own meaning, why they matter, so that they can then, in turn, do that for their students.

Christian: Let me jump in real quick, because I do think this is important. So how do you connect? In the pros, you’re with the same group of guys for eight months, seven days a week, nine hours a day. So it’s easy to make a connection. It’s so much harder to make a real connection in college, teaching kids. So you may see them once a week, twice a week, for two hours a week. Who knows? I would say this, and it’s a pain in the butt, learn their names. Tell them you’re going to learn their names. I try to do it. I challenge, say, “Listen, by the end of the semester, if I don’t know everybody’s first and last name, if I can’t say it correctly, I will get everybody one letter grade higher than what they already have.” So I put my butt on the line because I do think most of them kind of feel like I’m just like a finance major. I’m a whatever major. I do think learning their names, or at least trying to learn their names, in the short period of time that you’re with them, I think makes a huge, huge connection. Right off the bat, I let them know I’m gonna learn everybody’s name, and it’s freaking hard, man, because there’s so many damn kids. We got so much going on, but that’s one of my goals, always.

John: And that is a topic we’ve explored on past episodes of the podcast, and specifically with Michelle Miller, who wrote a book on how to learn names. And we’ll share a link to that in the show notes. It’s a great book, and it’s a short book, too. It is difficult to learn names. You both emphasize the importance of building connection and building trust to set a foundation for everything else that happens in the classroom. What are some of the other ways in which coaching is similar to teaching, or some of the things that you may have learned from coaching that you brought into the classroom in terms of things like increasing student motivation, because we’ve all been concerned about students becoming a bit more disengaged over the last several years.

Constanza: I think, apart from getting to know your students’ names, genuinely learning who they are as individuals, is something that, unless you’re in a classroom full of 300 students… at Bryant, we do not have that. We have the luxury of having smaller class sizes. And there are many ways that you can do that. We’ve talked for years about intro surveys, but I also think too asking deeper questions. In today’s age, we need to ask questions that really ask our students what they’re hoping to gain from class, but also ask them what would make you feel like you belonged in this classroom space, or what type of classroom space has been a really great one for you, and what did that instructor do and why? So that we understand how they are able to best flourish in the classroom space. And I think that this happens in coaching a lot, too. If we ask folks, what’s the best way for you to receive feedback, some people might want you to start with accountability up front. And they might want you to say, “Well, you told me your goal was this, and you haven’t been meeting it.” For me, that would hit me with so much guilt that if somebody coached me in that way, it’s like when my husband says, Constanza, “I know that you were supposed to be working on your dissertation today. Did you do any writing?” And I’m like, “Oh, I didn’t get any writing today. I’m sorry.” Like, if I start off with that, I get really nervous. So I know, for me, that isn’t always the best way to go about it. Sometimes I have days that are anomalies where I want that, but asking students like, “What’s the best way for you to get feedback? …and sticking to that as best as you can.” I do think we need to personalize the educational space more. I think it’s hard, because just like Christian said, we’ve seen the educational space change. We didn’t talk about belonging as much as we do now, even 10 years ago. Now, it’s so crucial. I think the challenge is balancing that, because instructors are humans too, and so, for them, they’re one instructor, maybe in a room of 35 students, times three or four classes. So it’s working with instructors to coach them into okay, how do you build those connections with your students? And how do you do it in a way that isn’t going to cause you to burn out? Perhaps that’s by asking better questions. Perhaps that’s by changing the way that we ask the questions. Perhaps that’s by talking to them about different ways that they can synthesize that information, or working with their teaching center to do that. I think those are the ways that we start to do it today, because students want that. And if students feel like their instructor doesn’t care about them, they are going to be less engaged. And if faculty feel like the teaching center on their campus is not a friendly place, or is not a place where they feel comfortable or safe sharing, they’re not going to go there. And so we need to make sure that we’re really listening to the people that we are working with, whether they be students or faculty or administrators or staff, and speak their language in terms of feedback, in terms of praise. And that’s another thing too. I think in teaching, we’re always looking for ways in which our students are struggling, that we forget sometimes, that we need to also give praise and feedback on what they’re doing right, and why. Because so often we’ll see our high performers and say, “Oh, well, Susie’s fine,” like she’s got this, she did really great on this assessment, and we don’t tell them how they did really great on the assessment. And that can be a form of isolation on campus as well, if those high performers are not feeling seen. And so that’s another thing I think we really need to be thoughtful about, and we also need to be thoughtful about the faculty members that we see spotlighted a lot. They’re still human, checking in with them, seeing how they’re doing, asking what they did over the weekend. Those small things really, really matter, and can help someone feel like they’re connected or disconnected to campus.

Christian: So Constanza, you said something I was literally cracking up. That’s why the connection between the two worlds, I think, is fascinating to me. If I respected and knew that my coach cared about me and was pushing me and was challenging me the right way, and I went out there and I let them down. I felt like crap. I felt like crap because I appreciated the effort that they put into developing me, and it’s those uncomfortable conversations, because, like, I sit there, I walk in there, man, I feel like these kids are fragile… You got to be real careful with what you say to one and how you say it, and you don’t want to say the wrong word or stick your foot in your mouth. I think they want, and I don’t have a better phrase for this, Constanza, maybe we can workshop this… tough teaching. I think they want it. I think they want to be challenged. They want somebody to challenge them. Because that, to me, is like the “Oh,” like, “this was really worth it. This is a great class. I learned a lot,” and that’s what I do. And I can tell the students listen. Some of them don’t care at all. They just want to get through it. But I would say 80% of them are dialed in, and it always makes me feel better when I see them accepting the tough teaching. This was not good enough. You know what our expectations are. Why would you do it this way? You know what the standards are, like I told you the day one, and I keep reiterating it every single day. You’re so much better than that. It’s a little bit of mix of scolding but reaffirming your love for them. I’m on you. I’m talking to you this way because I know you can do better. I know you can, so don’t tell me that I don’t believe in you, because I’m showing it right now. It may not be the way you want it, but that’s the way it’s going to be given to you, because this is the way I think I can get the most out of you, and I’m real open about that. And to Constanza’s point, that’s why I was laughing, because my coaches, whenever they were like this, I was at my best; when they just kind of walked through and didn’t say anything, I didn’t care at all if I messed up, because he was a jerk. So I was fine with it.

John: That reminds me a little bit of the concept of wise feedback that’s been developed by David Yaeger and others, which is, let students know that you’re giving them feedback because you believe that they’re capable of more. In several randomized, controlled studies, it’s been shown that that increases student effort substantially.

Christian: Yeah. And to be honest with you, I was giving them feedback during the class, but I wasn’t when I was grading them, and I remember Costanza saying, “You got to give them feedback.” In my head, I’m like, “Oh, geez, there’s so many kids, like, I’m going to be writing like, a long, two page paper for each kid.” And I really appreciated that, because she was right. I felt so ashamed that I was not giving the feedback that I should have been giving them, because, to your point, it’s important for them to hear what they did wrong, what they continue to work on, and how I know they can do it. I’ve changed since the first day. I think you’re like, “You’re not giving them feedback?” I was like, “Well, I am. I’m just not giving probably enough.”

Constanza: And I think, too, we talk in higher ed a lot about the phrase “compassionate challenge.” And so Christian is able to have difficult conversations with students, or what others might perceive as difficult conversations with students, because he shows them how much he cares. And so when he says things like “Hey, like what happened here?” from someone else who doesn’t have that connection established with their student, that might be the most intimidating question ever. Christian also will say to them, like, “Hey, this is going to be challenging.” What he didn’t tell you is he brought a group of students out to radio row to cover the Super Bowl. And I’m sure he said to them, “This is going to be challenging. You’re going to have to meet people that you’ve never spoken to before.” We know for students that just speaking up in general, in today’s age, in a classroom with an instructor that they’ve gotten to know over the course of this semester can be intimidating. And I was so impressed by the work that the students were putting out and the little clips that I saw on Instagram, in the coverage that I was seeing. And that’s really a moment where, as an educational developer, you take a step back and you’re like, “Wow, this is public evidence of someone who has taken teaching, who has taken coaching… literally taken students out of their element into a brand new experience. And look at them thriving. Look at them doing the thing that, in the classroom maybe was talked about as theoretical. Look at them applying it.” And so I’ve been just really proud to watch Christian’s evolution in terms of the way that he coaches his students and teaches them too.

Rebecca: I’m curious, at what level, both in faculty developer working with a faculty member and also a faculty member working with a student, how much work there is, maybe, to overcome impostor phenomenon. So moving into a new space, a new discipline, even if the subject matter is familiar, how that can kind of play into [LAUGHTER] like either desperately wanting feedback or also some fear, and how students can be the same, maybe in a class with someone famous [LAUGHTER] might be really intimidating, and how to kind of work through some of those kinds of challenges students and faculty face?

Constanza: Yeah, I think first, to go back to what I said earlier, you have to develop a connection with the faculty member that you’re working with as well. In my experience, that is the only way that you can be successful in terms of educational development, because you can give a ton of advice, but if the faculty member doesn’t know that you mean what you say and that you care, and if they don’t know who you are as a human, it doesn’t matter. And so folks might come in and they might be skeptical of you. They might be skeptical of the theories or the teaching center or the work, and then you have to demonstrate to them in a way that isn’t condescending, that’s inviting, “here’s my subject matter expertise.” We forget sometimes that educational developers have that too. We have a whole lot of subject matter expertise, and my colleague, Michelle Munoz in our teaching center is always talking about that, which I love. It’s a good reminder for me too. So before we get into any of that, first we have to sort of human to human talk, and then we can get into coaching. And then we can say, “All right, what’s your plan? Let’s talk about it.” And if it doesn’t go according to plan, I love reminding faculty, the only person who had the playbook was you. So, if you didn’t cover all five things that you wanted to cover, the only person to know that necessarily is you. But also, that is what we need to remind faculty. And also it can be a positive thing. Maybe students got so fascinated about a particular area that you were talking about, you had all of this conversation that time flew by and you didn’t cover everything. Faculty in today’s world are so worried, at least in the work that I do, about covering all of the material. We had a huge snowstorm, and then a blizzard in Rhode Island. And so we had multiple days this semester that either school was canceled or we went remote. And so there was a lot of nervous energy there about, “oh no, I had planned for X many course sessions. The classic snow day happened, and now I have to figure out how to compensate.” And so working with faculty on saying, like, “Hey, it isn’t about covering all the content necessarily. It’s the quality.” And let’s rethink and reframe what that looks like, not just for this week, but for the semester. Because, again, the semester long playbook is yours, and you are the subject matter expert. You determine what students really, really need to know in order to have meaning come out of this class, let’s work together on it. So that’s how I think I would respond to that question.

Christian: Listen, from a teaching standpoint, for me, is: “absolutely what am I doing here? I don’t belong. I don’t know anybody all that stuff. I don’t have a master’s degree, but I know I know what I’m talking about because I’m an expert in my field. I just don’t have a degree for it.” Nobody has been like, “Here’s a trophy. I don’t have a Marconi. I have 20 years in the business,” but, yeah, to me, that’s a Costanza thing. It’s almost like, you are good enough, you are smart enough. You know, it’s one of those things. [LAUGHTER] I’m like, “Yeah, let’s go. There’s no brick wall to run through, there’s just a door, so I have to consume my energy.” So that was real. I’m not gonna lie. Admitting that is hard to do because, like, I just didn’t really feel like I belonged at first, and it took some time and some coaching to feel comfortable in my own skin, for a bunch of like, 19 to 20 year olds.

Rebecca: They’re tough crowd.

Christian: They are a tough crowd. I look at them sometimes and I’m like, they don’t give a crap what I’m saying right now. And I leave there depressed, and I feel like I didn’t do a good job. That’s the worst feeling, to be honest with you, is leaving the classroom going, “Oh man, that sucked. I could have done so much better than that.” But I will say this, I know I’m kind of changing the direction, but even when that happens, I’ll have some students that’ll come up to me, and I swear up and down, this kid was not paying attention, could care less, was not engaged at all. When those guys come up to me, or those girls come up to me and they say, “This is the best class I’ve ever had.” I’m like, “Really? You didn’t say one word.” [LAUGHTER] You didn’t say anything. You acted like I had a big hole in my head. And that, to me, is very rewarding.

Constanza: Yeah, and I think to go back to the point about impostor syndrome, we all feel it. So sharing it… I started teaching in the college space when I was 23 and so like Christian, I tell faculty this, like Christian, I was like, “No, I’m supposed to be on the other side of the desk, I’m just supposed to be learning. I’m not supposed to be teaching. And the first day I taught, the technology didn’t work. I couldn’t get my roster. It was, in all senses, a disaster. And I remember going home and called my dad, and I was like, “Dad, I don’t know how you do this teaching thing. You have to get up in front of people, and they’re staring at you, and everyone depends on you,” and that’s a lot like you said, Rebecca, it’s a tough crowd, and I think too, we’re navigating again like a rupture in higher education, like I mentioned earlier, that Jessica Riddell talks about. And so it’s showing faculty different skills and strategies to reconnect with their audiences in different ways, and they’re learning how to do that. And beginner learning experiences when you’re doing something in a different way can be daunting. And so it’s everything that Christian said. It’s connecting on stories of, okay, well, this didn’t go the way that I expected in the classroom, knowing you’re not alone. I mean, if we could tell tales from the teaching center, which we can talk about things in broad terms, but we’re a non-evaluative, confidential space, but if we had tales from the teaching center, I think a lot of people would say, “Oh, I didn’t realize that so many people were going through the same thing that I was going through. And that’s what we hear on a daily basis in the teaching center. So when folks come to me and say, “I didn’t feel great in that class,” or “the students seem really tired this week,” I can be like, “yeah, there’s a lot going on this week, from what we’ve heard,” and I can reaffirm that for them, but I can also talk to them about my silly moments too, and I’ll also say what I love. My sister is a daycare teacher, and she talks, and she’s like, sometimes things don’t go according to plan in daycare and I’m like, “What do you do?” Like, talk to me about what that looks like, because I only know how to work with adults. I don’t know how to work with toddlers. And she’s like, sometimes we just have to redirect and shake the sillies out. And she, quite literally, will have the students just like, shake it out. And I feel like we need to just shake it out in higher education too.

Christian: I’m doing that. [LAUGHTER]I’m so doing that.

Constanza: Isn’t that adorable. They shake the sillies… [LAUGHTER]

Christian: I bet you, they can probably laugh about it. It probably completely brings them back to their childhood, and it does kind of reset during that time. I’m literally going to do that.

Constanza: I love that, and I will say too, when students get up to present in front of the class, I think that’s probably when they have the most empathy for us, because they realize, “oh, I have to be the focus of attention, and I have to be the content expert right now, and I also need to be not robotic, but engaging.” And those moments, I think, can be helpful in reestablishing connection, not that I think you should just assign students a presentation for no reason. I think that would be very anxiety inducing [LAUGHTER] for most students today. But I think talking about how it might be really daunting for us to be up in front of the class, or letting students know, like, “Hey, I didn’t sleep really well last night. So if I say something and it doesn’t fully make sense, please ask a question,” or just reminding students that we get paid to answer questions, I love saying to faculty, “They actually pay me to answer your questions,” because they’ll be like, “I don’t want to bother you. I know you’re so busy, you’ve got so much going on, I don’t want to take up all your time,” unless you’re Christian. He just said he loves to hijack my time, but like, I get paid to answer questions, come ask questions. We love that in the teaching center, and I know that faculty love it too, because to Christian’s point when someone comes up and asks a question or says, “Wow, that was a really cool lesson.” Oh my gosh, like my heart swells. And I wrote a piece for Inside Higher Ed a while back about keeping a jar of those types of comments, because teaching evaluations, especially, we hold on to that one comment that is slightly negative or totally negative, and we don’t see all the other comments that are like, “this was really eye opening and inspirational,” and “this instructor really cared.” No, we just focus on that one. I think we need to talk about our wins. And if I may, one other thing that I’ll say, I had my husband do this just yesterday, so it’s not just in the educational development space. He works in property management, and he had a woman come in yesterday and her internet was disconnected, and he was like, “I will come stop by during lunch and I’ll help you out.” And she wrote him back and she said, “You made me feel connected to the world again by helping me out.” And I was like, “Michael, you better frame this. I’m not kidding. Like this is the best piece of feedback that you’ve gotten,” because usually folks come to him and they’re like, “I’ve got a problem in my living space,” and it’s really stressful. And he did. He did because he knows I talk about it all the time. I’m like, celebrate your wins, put them as a reminder, so when you have a bad day, you can go back and look at it and be like, “Oh, okay, this is why I matter. This is why I’m here.” We all need that.

John: I’m going to put in a plug for another podcast that came out in the middle of March, about a month or so ago on impostor phenomenon by Sara Kien. We had a really good discussion of that, so I’d encourage people to go back and listen to that if they’d like to learn more about it. In earlier conversations, you both mentioned how surprised you were about the strong similarities between the ways in which skills are developed in sports and the way in which skills are developed in the classroom. How has the experience of sports perhaps informed the way in which you work with students to develop their skills?

Christian: So I would say I have 14 classes with them total in a semester, it’s a lecture class, and there’s not a chance in hell, in 14 classes, I will have them camera ready at a high level to be ready to perform. So what I do is, to develop those skills, we practice it, every day when we get in, we practice certain skills and we practice them again. And part of that would be my game plan. For a game every Monday, when I would come into the facility, my coach would hand me a game plan. Here’s your game plan. Here’s what we’re going to do. Before we even practice, we would do the basic stuff, chopsticks, you know what I mean? Like just to warm up. And the funny thing is, what I’m realizing now is like, I keep telling them, “You guys don’t even understand what I’m doing right now. You’ll understand it in about two months,” because I make them do it over and over and over and over and over again. I said it’ll make sense in the end, and that’s kind of the way I look at it. I think specifically for broadcasting, there needs to be repetition. There needs to be a massive amount of repetition. And there’s a technical side to broadcasting. There’s a physical side to broadcasting, and there’s an emotional side. The first two, I can teach that all day long, easy, like we’ll hammer it home. The emotional side is the more difficult avenue that I’ve seen, getting them comfortable in their own skin. So to that, how do I develop that skill, which is the hardest one to develop, especially for someone who’s not comfortable being in front of people, who’s not comfortable speaking? I’ve created certain… I’ll just call them competitions, either solo or as a group… I’ll divide them into left side versus right side, and constantly putting them in those situations that make sense when they do their final, and then when their final comes around, listen and I tell them, “You’ve done this. We’ve done that. We’ve done this again. We did it over and over again. You guys are ready. The skills are there. The information is there. Now let’s put it into practice.” Constanza was actually… and I hijacked her for one of my classes, for one of the presentations, because I created our own version of Shark Tank, and she was one of the judges. And she was supposed to be real critical and challenge them. I want them to be challenged. And there was two other professors that helped out. So that’s what I would say, developing those skill sets. As far as the stuff that I teach is just constantly, constantly repping it. It has to be wrapped, and it has to be rep in the sports world, with different situations, different scenarios, different games, different endings, different circumstances, tough. “Hey, can I really say this?” “Well, no, but say it this way,” making them a goal again, but then at the same time, making sure that that person knows, because we’re talking about insecurities now, people judging me, making fun of me, like even though they’re probably not, that everybody else in the class needs to know. This is a workshop. This is a dojo, like we’re practicing here. This is not the final event. This is not our Olympic 100 meter relay. Like, this is the dojo. We’re in the dojo now, and this is where you’re allowed to make mistakes. So that’s kind of the approach that I take.

Constanza: Yeah, I think similarly, when I teach Spanish language classes, I say, “look, first, in order to learn, you have to be here, right?” And I explained to my students why it matters, and I get to know them on a personal level, so that if someone isn’t here, you’ll hear my students say, “Yeah, I had to take a picture of an empty chair and text it to my friend Bobby, because they weren’t in class today,” because I’ll say “We missed you. You’re not here.” Like, “hope you’re doing okay. Check in.” I like to let them know. First and foremost, I notice if they’re not there. Second, this goes along with faculty too. Like, if I haven’t seen one of my faculty in a while, I’ll email them and say, “Oh my goodness, we haven’t connected. How are you? What’s going on? What are you teaching this week?” Because I want them to know I’m thinking about them, even if we don’t cross paths in the hallway. In educational development, similarly, if we’re not practicing our teaching skills or staying informed about what different and new pedagogies are, or different approaches, then it becomes really hard to pivot if we need to. And gosh, if COVID taught us anything, it’s that we need to be ready to pivot if something happens. And the blizzard showed us that as well, we suddenly went for a couple of days to remote learning. So we need to know how to be adaptable in a way that our students are able to absorb it. We need to be able to do that for educational development too. So we’ll do things in the teaching center like have one-on-one consults, and we’ll ask for follow ups from faculty if they want to. We hope that they do. And we’ll also not just do one off workshops, but we’ll do a workshop series, because I think you all can relate to the idea that sometimes you go to a workshop, you have tons of great ideas, but there isn’t follow up. And so that repetition that Christian’s talking about, we have a series of events, like a book group or a four-series workshop. Then folks have time to reflect upon what they’ve learned, think about implementation, and come back with questions, checking in with folks to say, “how was the pace of this session,” or “we talked about this approach you were going to take in the classroom, how did it go?” And actually showing that we care as educational developers in a way that is coaching, but also just again, genuine, is so important as well, because sometimes faculty might have tried something and it didn’t go the way that they planned, and they might be embarrassed or shy to come and talk to us and say “this didn’t work out.” So normalizing that as part of the process, and then to feedback like we talked about earlier, letting folks know, “Hey, you’re not the only one who’s experiencing disengagement. This week, we’ve seen an uptick in that” or “Yeah, like there does seem to be a lot of absences the day before spring break. It’s not just your class,” letting them know that it might feel personal, right? Because you’re saying, “oh my gosh, why did three students not show up to my class? I had a really great topic prepared.” “It’s not just you. There’s a trend that we’re seeing here.” I think that is really helpful.

John: One of the things you mentioned a couple of times was the importance of letting faculty know that they’re not alone. And as you said, reading groups or a series of workshops where faculty get to work together repeatedly, allows those things to naturally come out, and it’s so reassuring for faculty when they feel that they’re not the only ones experiencing those challenges.

Constanza: Yeah, and I think that is one of the beautiful things that we do in the teaching center that isn’t necessarily labeled as a service that we offer, but maybe it should be, and maybe that’s a way that we need to reframe the types of consults that we do. Maybe it’s a belonging consult, but everyone is different, right? So for me, I’m just like, “Oh, I’m meeting with Christian today, and we’ll see what we talk about.” And I genuinely get to know him as a human. I get to know what he teaches about. I get to say, “Huh, I have no idea about this. Can you teach me?” But yeah, I do think that one of the unspoken and unwritten rules in educational development is helping faculty engage with their students but also find their niche in the campus and sort of connecting the dots between what’s available for them and what can help enhance their teaching. So when we talk about coaching on the field and we talk about performance, we want athletes to be better, to feel more motivated, to have that drive and energy. We want the same thing for our instructors, too, but we also want them to know that not every single day goes perfectly. Just because we’re subject matter experts doesn’t mean that we have all the answers. And I think when we talk about that feeling of being alone on campus, that also means, if a student is going through something, and you’re a faculty member and you care about… I tell my faculty all the time, we have a lot of student-facing resources on campus. That is when you refer, like you’ve got something going on with a student and they need support that is outside of the scope of being an instructor in the room. That’s why we have care report, so that you can get that student the support that they need. And for faculty, we’re often the only faculty facing office on campus. So letting faculty know, “Oh, by the way, you’ve got somebody here. You’ve got a team here that is here just for you, so that if you’re navigating, let’s say, classroom management, we’re here to consult with you, to talk about what are some ways that you can navigate this. What are some approaches that you can take that are different than perhaps the ones that you’ve been taking?” So yeah, it’s really important not to feel alone, especially in teaching. It’s such a relational field.

Rebecca: One thing that I heard come out in the conversation today is maybe a need for this practice and repetition for fluency, but with variety. And I think repetition kind of implies, by its nature, that it could get boring really quick. So how do you, as a coach, bring in that variety so that it stays engaging?

Christian: Well, I think you just said it. Sports is all about repetition, shooting the same free throw 100 times, blocking the same way over and believe me, it gets boring, and you’re hoping that your coach can get creative. So we’ll just change the drill, and you feel like it’s new, but it’s technically the same thing. It just looks different, but the coach is still getting the same outcome that he wants, but now he’s got you reengaged, and you’re not bitching and complaining about being in the same old thing in week 10. I think that’s the same thing for teaching. It is, how could I create the same lesson plan or try to get the same thing across and they think that it’s actually different? And I try to do that as much as possible. Either I change the topic, I change the theme of it, because, you know, I get bored too. [LAUGHTER] I get bored. So, like, Man, [snap fingers] [LAUGHTER] I gotta snap out of this. Let’s kind of change where everybody sits. I don’t know, like, just do something different. That’s about as simple as I can get with that. Like, I think it’s the repetition is key, and you can’t get rid of it. You can’t ignore it. But I do think creating a different lesson plan that still has the same teeth as how it was originally set up to be, I think is important also.

Constanza: Yeah, and I think the same type of coaching practice happens in educational development too. Number one, not every single strategy or approach or recommendation, I make… Christian will laugh when I say this, but in our first weeks of mentorship, I was like, “Look, you don’t have to listen to me.” And he’s like, “But Constanza, I’m here. I want to listen to you.” And he’d always say that over and over again. And I was like, “Well, no Christian. What I mean by that is, I’m going to give you a variety of options. You choose what feels right for you, for your teaching style, for your teaching approach.” So we have to offer variety, because folks may or may not be comfortable with one solution that we give. We might need to give them a variety so that they say, “Oh, this one feels like it fits for me or for my context,” because, again, as I mentioned at the beginning of this episode, I often am not very familiar with the subject matter that the faculty member is coming in with. So I’m there to talk about the teaching strategies. Number two, there are so many books on educational development, they keep coming out. Why? Because our students and our faculty are changing in terms of their expectations, what they need. So we need to adapt, and there will always be books on educational development, because we’re still learning more about how we learn, and again, the people who are in front of us change. So I think, on that note, too, we need to constantly be offering variety. If I only offered the same three workshops every single year, I would not have attendance, because faculty will say, “Well, I’ve seen that. That’s old. Teach me something new.” So if we want to engage faculty, then we need to give them new approaches. Or if we’re talking about student engagement, which is something that we’ve always been talking about, we need to talk about different ways that they can approach that. So to Christian’s point, yes, it’s the same topic, but it’s a different way of explaining it, or it’s a different idea, or it’s having faculty perhaps present themselves and say, “This has really worked well for me in the classroom. Let me show you how I do it.” We have one instructor who, he came in last summer, and he said, “Constanza, I’m going to upend my class, and I want to talk to you about it.” And I said, “All right, tell me more.” And so he decided to use flipped learning for all of his classes. He started with one, and now this semester, he’s using it for all of them in a really different and unique way. And then he delivered a presentation about it a few weeks ago to talk about the implications of that in the age of AI, which is not how we would have talked about flipped learning even five years ago. So we all need a refresh on our teaching. And the other thing I’ll add to what Christian said is, if you don’t, it gets boring for you too. I know even teaching three sections of the same course back to back, by the third section, I’m like, “Okay, did I say this in this section, or did I say it in the section before?” We need to spice it up for ourselves so that we can have the passion to then share with our students. It’s so important.

John: We always end with the question: “what’s next?”

Christian: Is world domination too aggressive? [LAUGHTER]

John: It seems to be really popular right now.

Christian: Oh, that’s true. [LAUGHTER] True. World domination in the teaching profession, that is what’s next for us. I’m going to speak for Constanza as well, because we feel like we have a really good message, and I think we have a real unique way of sharing and explaining that message. And so what’s next for us, from a partnership, is trying to find unique and creative ways to share what we have learned. And that’s the easiest way I could express it. Constanza, you do the academic version and clean it up for me.

Constanza: I think just to add to what Christian has said, I really, really enjoy working with Christian. It’s not every single day that you get to go home and say, “Yeah, today I work with an NFL two time Super Bowl champion,” and remember, I know nothing about football. So it’s really cool how we came from such different spaces, and yet found so many parallels in the work that we do. I really appreciate the openness that Christian has. It takes a lot of courage to be vulnerable in teaching. We know this, and to talk so openly about “Here’s what I’ve learned in my last year or so of teaching.” And I’m really excited, because I think, to Christian’s point, we’ll be able to help a lot of other instructors just by talking about the work that we’re doing. So I’m really excited that Christian has agreed to work with me in this way, and we’re just so grateful for being able to be here with you both today to talk a little bit about what we’re doing.

John: Well, thank you. It’s been great talking to both of you, and to meet you, Christian and to talk to you again, Constanza, and the message that you have about working together to improve teaching is one, as the director of a teaching center, I’d like to see a whole lot more of because we can all learn from each other and sharing our ideas in a world in which things are evolving so rapidly in higher ed, can be really helpful rather than trying to deal with everything alone.

Rebecca: Finding ways to reframe, using coaching and other frames can be really helpful to spice things up and provide that variety, even if the underlying techniques and things are really the same.

Christian: This last thing, and we’ll cap it, when I talk to coaches, coaches in the NFL and college, when I talk to them about their philosophy on coaching, they all end up saying, you know, we’re just teachers. They say the same thing that we’re saying I’m saying, “Oh…” they go, “No, we’re just teachers,” and they just use a different word for it.

Rebecca: Well, thank you so much for joining us, and we look forward to sharing this with our audience,

Christian: Yeah, can’t wait to hear it. Appreciate you guys having us on.

Constanza: Thank you both.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing Assistance provided by Madison Lee.

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434. TUnE Bio

Students planning to major in STEM fields, especially students from groups historically underrepresented in these fields, are often deterred by their experiences in introductory courses. In this episode, Sarah Rose Cavanagh joins us to discuss an NSF-funded initiative designed to enhance student success in introductory Biology classes.

Sarah is a psychologist and the author of four books related to teaching and learning. She is the senior associate director for teaching and learning and associate professor of practice at Simmons University and also is a regular contributor to The Chronicle and many other publications. Sarah often serves as a keynote speaker and we were very fortunate to have her join us for a keynote address at a recent Academic Affairs Retreat on our  campus.

Show Notes

Transcript

John: Students planning to major in STEM fields, especially students from groups historically underrepresented in these fields, are often deterred by their experiences in introductory courses. In this episode, we explore an NSF-funded initiative designed to enhance student success in introductory Biology classes.

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John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

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John: Our guest today is Sarah Rose Cavanagh. Sarah is a psychologist and the author of four books related to teaching and learning. She is the senior associate director for teaching and learning and associate professor of practice at Simmons University and also is a regular contributor to The Chronicle and many other publications. Sarah often serves as a keynote speaker and we were very fortunate to have her join us for a keynote address at a recent Academic Affairs Retreat on our campus. We’re also looking forward to her upcoming keynote address at the SUNY Conference on Instruction and Technology later this year in May. Welcome back, Sarah.

Sarah: Thanks for having me.

Rebecca: Today’s teas are:… Sarah, are you drinking tea with us today?

Sarah: Ah… I never am. I actually have a confession. I’m actually drinking a Celsius energy drink. [LAUGHTER] I’ve graduated from even coffee, but I only do this. I’m like, I don’t know if you’ve seen this, but Zoran Mamdani, he has a Red Bull once a week, and so, like Mamdani, I have a Celsius once a week. Gives me a real boost.

Rebecca: Right now is the time. We’re catching you at this moment this week?

Sarah: Well, yes, and we also ran out of coffee filters, so… [LAUGHTER]

Rebecca: That’ll do it. How about you, John?

John: I am drinking an Irish Breakfast tea today.

Rebecca: Oh, seems to be like a new favorite, John.

John: Actually, it’s because we’re snowed in and most of my other teas are up at the office.

Rebecca: I have an English afternoon today.

Sarah: What time of day is it? This seems to be a conflict. One of you is doing breakfast? One of you is afternoon.

John: It is afternoon, isn’t it?

Rebecca: yeah, I don’t know. Yesterday we recorded at 5pm and we both had breakfast teas, so… [LAUGHTER]

John: We did.

Rebecca: It might just be an indicator of how much caffeine we needed at a given time. [LAUGHTER]

Sarah: Gotcha.

John: We’ve invited you here today to discuss the TUnE-Bio project, an NSF-funded academic initiative, for which you are a principal investigator and network leader. Can you give our listeners an overview of this project?

Sarah: Sure. So the focus of the project’s right in our title. So it’s Transforming Learning and Equity in Undergraduate Biology Education by Cultivating an Ecosystem of Feedback, which is a big mouthful, so we call it TUnE-Bio. So we’re tuning biology education. And the focus of the project is really on tackling the problem of high attrition and weed out in undergraduate biology education, and by extension, STEM fields in general, the fact that so many students come to the study of biology and the study of STEM fields in general and want to be doctors and surgeons and scientists, and then they encounter difficulties in their introductory courses and leave for other majors. There’s something that is known as the STEM grading penalty, which is when students get really low course grades in STEM courses, but then they get high grades in their other courses, then that in particular seems to kind of lure them out of the field. And we want to change that, in general, because we need more scientists and we need more nurses and surgeons, but we also want to change that, because there’s also a lot of inequities in terms of who is weeded out, and disproportionately women, students of various races and ethnicities, first-generation status. All these things tend to increase the chances that you will leave STEM fields, and we want a really educated, diverse STEM workforce, and so we’re really trying to do something about that by improving pedagogy.

Rebecca: Sounds like a good initiative… [LAUGHTER]

Sarah: We think so.

Rebecca: …right up our alley around here, right? [LAUGHTER]

Rebecca: At the beginning of the project, you first gathered information on the current landscape of biology education using surveys and focus groups. Can you tell us a little bit about the process of data collection?

Sarah: Yeah, sure. So we started this grant with what’s called an incubator. So NSF has two versions of this particular mechanism, and the incubator is meant to, just like it sounds, to give you a little bit of money to start building a network, start showing the NSF that you can do what you are promising to do. And then we got a full version of the grant a few years later. So this really goes all the way back to, I think, 2020, and we were unsuccessful a few times before we were successful. [LAUGHTER] So it actually goes back to more like 2018, I think, but we got the incubator grant, and one of the things reviewers said to us is, all right, you’re saying you want to innovate in pedagogy in biology, but before you can innovate, don’t you need to know the current state of play, like, what are people doing in their classrooms currently, before you start asking them to do something different, like, maybe they’re already innovating. So we took that charge, and in the incubator year or two, we did a really large survey of people who were teaching introductory biology, all sorts of different institutions all over the country, about 300 of them, and then did qualitative interviews of 27, I think, of them… took a deeper dive. And we’re really asking people about their practices and philosophies in teaching intro bio. What were they doing in terms of assessment, in terms of feedback, in terms of grading? We asked about what they perceived as barriers to innovation. In the early phases, we’re a little more focused on alternative grading. And so we asked a lot of questions about: had they heard of it? had they tried it? Questions about that. And so that was our attempt to figure out the landscape before we started innovating with the full proposal.

John: And what did the data collection tell you about the landscape?

Sarah: Well, first, I have to say that I do think we got a bit of an unusual sample. It’s always the case in research, because we tried to keep the survey under 15 minutes. But still, who has 15 minutes to do something completely optional, and then the people who spoke with us in the qualitative interview study, that was a significant investment of time. And so I do think we got a sample of people who were really interested in pedagogy already, but, that said, we saw a lot of innovation already. So the old school model of two exams and a final in intro courses, we really didn’t see a lot of that. People were using a lot of quizzes, a lot of clickers. They were really thinking deeply, especially in the interviews. They had thought deeply about equity. A lot of the folks we talked to, they really felt the pull of doing more like differentiated instruction and really changing their pedagogy, student by student. And had a lot of frustrations with not being able to find the time to really do that better. And we also asked about barriers to innovation. And shockingly, the top three barriers were time, time, and time, I think the top was no time to implement. The second was the perception that some of these innovations would take more time. And then the third was no time to even like, figure it out, do the reading, restructure the course ahead of time, do those things. So we saw a lot of innovation, but a lot of desire for more, I think.

Rebecca: So that kind of sets the ground for the pedagogical side of the landscape. Can you go into a little bit more detail about attrition rates and the equity gaps that the project is meant to overcome?

Sarah: Yeah, sure. So a pretty significant number of students leave early STEM education for other majors, and it does seem like investigations that have probed the why of that and the motivation has really found it tying to these early low grades in these introductory courses, and I think, in particular, students who are drawn to health sciences, just the way everything set up in secondary education, tend to be students who are quite used to getting high grades, [LAUGHTER] and so having this experience of just getting As and As and As and then you hit college, and you take Intro bio, and then all of a sudden you have a C for the first time in your life. And that being very alarming. There’s also, and certainly we heard this in the survey and the interviews, in terms of the equity, something that people really thought a lot about and felt very deeply about, was the fact that, especially courses that are introductory courses, like intro bio, students would be coming to them with very different backgrounds. Some students had taken AP Bio with a tutor and all these extra meetings and already knew half the material that they were going to be taught. And other students from lower-resourced high schools did not have that experience, and faculty really struggled with that. Starting out from the gate, these students were on paths to differential outcomes, and then that would shape whether they stayed with the STEM study or not. And none of them had really easy solutions, and one of our goals in going forward is trying to give faculty tools to improve pedagogy in ways that equitize that landscape a little bit more. I mean, that basic problem is still always going to be there. It’s a societal structural problem. But if we can nudge faculty to improve how much feedback they’re giving, how motivational that feedback is, how timely it is, all of these things that maybe we can bring those students on those two paths a little closer together.

John: This issue of disparate gender outcomes in STEM fields is something we addressed a little bit on an earlier podcast with Peter Arcidiacono. This seems to be common in many disciplines, so addressing these issues, I think, could be really helpful. And one of the things that really impressed me was looking at the people in the network, or the people who are involved in this program. I’m not a biologist, but I recognize an awful lot of the names there. How was the TUnE-Bio network assembled?

Sarah: Dialing it back to those incubator years, and before our unsuccessful to successful outcomes, originally, Jim Lang and I were co-PIs, so we were both still at Assumption, and were both in the same center, and so we were working together on this, and I was actually really new to the field of teaching and learning, and didn’t know a lot of people. I started out as a psychology, emotion science, person, and only in the last 8 to 10 years moved into teaching and learning. So a lot of them Jim recommended, and then it was like a snowball. So we recommended them and some of those people said, “No, but have you thought about asking this person?” And so we kind of snowballed out, and we had our steering committee, we asked them for recommendations, and they went out from that. And then Jim retired. Then that’s when Michele Lemons my co PI who is a neuroscientist, biologist at Assumption, that’s when she joined. Jim’s still on our steering committee, but now Michele and I are co-PIs on the full proposal, and then we did a big webinar, we did a big launch meeting, and we just actively recruited. So it’s really kind of a snowball method. Also, Bryan Dewsbury is on our steering committee, and he has a beautiful essay, I think, called the soul of my pedagogy in Scientific American, and I had read that, and I was like, “Let’s invite that guy,” [LAUGHTER] and Jim was like, “Great suggestion.”

Rebecca: It’s always fun to see all of those folks assembled in the same place, virtually or otherwise. [LAUGHTER] How does this project connect to the concept of compassionate challenge that you focused on in Mind over Monsters?

Sarah: Well, since we’re focused on feedback, and I think we’ll talk about that in a little bit more, I see feedback at the heart of compassionate challenge. So my framing of compassionate challenge is that students, to be motivated and to learn effectively, they need both compassion and challenge, kind of that false dichotomy that we get into and dueling Chronicle op eds between like, “oh academic rigor” and “oh student focusedness.” Like we need all of it. And I think that we respond best and are motivated when we both are in a place where we feel psychological safety and where we feel understood and seen as people, but then where we also are challenged to rise to expectations. And feedback, I think, is just so critical for student learning, because what feedback is, is you’re giving students information about “okay, where are you at in your current learning journey, and where could you go, and how can you get there?” In that way, it’s stretching them saying, like, “Great job at the learning so far, but here’s all the other places we could go.” [LAUGHTER] But then we’re really focused in the project, on how can we give better feedback, and better feedback being compassionate, growth mindset, motivational, offered in time that they can use it, including more revisions of work, so that there’s a reason for them to read the feedback. If they assess them on a learning outcome, you give them feedback on how they did, and then they’re never assessed on that learning outcome again, I wouldn’t read that feedback. [LAUGHTER] And so I think feedback is really at the heart of compassionate challenge.

John: So right now, it sounds like much of the work of the group is done in working groups, and you have five working groups which are focused around different ways of providing that feedback. Could you talk a little bit about some of the work of these working groups?

Sarah: Absolutely, in the grant, what we’re doing currently is we’re developing tool kits, so we want to give faculty really practical ways that they could improve their feedback. And one of the other aspects of the grant is that it intentionally puts in the same room, often the same Zoom room, that we are gathering in person this May, educational developers and people from the scholarship of teaching and learning, and then biologists. And so we’re putting these minds together so that the biologists can provide the context of teaching biology, and then educational developers can provide the scholarship on teaching and learning. And we want to put them all together in a room, develop these toolkits that will help biology faculty teach in ways that improve feedback. And so one of the first things we did is we assembled these toolkit working groups, as you’re referencing John, and we just today decided to call them twigs. We keep saying T, W, G, and that’s very difficult to say out loud. And then we were on a meeting with one of our steering committee members, Linda Fergusson-Kolmes, and she was like, I keep wanting to call it twig. And I was like, “Let’s do it. Let’s do it.” So these twigs, [LAUGHTER] these each have co-leaders, and then they have members who are also amazing folks, and they’re focused on five different themes. So those themes are, first, how do we get students motivated to actually use feedback and actually read it? Because there’s some data that suggests that, especially when feedback is paired with a poor grade, students won’t even read it. So one group is developing tools to help faculty motivate students to uptake and use that feedback. Another is focused on peer feedback. Some of the tools in that toolkit will relate to LAs, learning assistants and teaching assistants, but then also ways that you can provide peer feedback in class. So motivation, peer feedback. One group’s all focused on classroom assessment techniques like clicker questions and other ways to quickly assess and get feedback on student learning. One of the groups is developing a toolkit on the seven principles of good feedback, asset-based feedback. And then finally, my favorite topic in the world, but we have some lovely volunteers who are willing to go there, AI. [LAUGHTER] if we’re doing something on feedback, they have a challenge ahead of them.

John: I don’t think we’ve had a podcast in the last couple of years that have not at least mentioned AI in some way.

Rebecca: This was yours, Sarah. [LAUGHTER] Now you can move on. [LAUGHTER]

Sarah: Yes, we’re done. Check the box. Check the box.

Rebecca: One of the things that we often hear is like feedback and grading are intermingled or tangled up together. Can you talk a little bit about decoupling of the two?

Sarah: Yeah, sure. We definitely saw this, especially in the qualitative interview. We tried to ask faculty separately about assessment, feedback, and grading, and we defined those for them ahead of time and in the process of the interview. And we defined feedback as information that’s given to students about the current state of learning or their performance on the assessment, other than grading. And then we wanted grading to be grading. Faculty found that very difficult to respond to those three things separately, because they’re all so integrated, and grading is one form of feedback, of course. We really tried to get them to focus more on things like written feedback on short-answer questions, and we have a subcommittee working on the qualitative interview coding. That’s Brian Dewsbury and Lindsay Wheeler and Michele and I, and we have a conceptual model of feedback that we’re building, and we really are thinking of feedback even as things like body language in response to students’ questions, like really expansively about feedback. And I think that the more we can decouple feedback from grading the better. And there’s actually studies showing that if you give feedback before you give the grade, if you like, kind of withhold the grade and you give the feedback, students are more likely to use it and implement it, because I think that the emotional power of the grade just blows everything else out.

Rebecca: As in, like the grade feels too final?

Sarah: Yeah, and if the grade is lower than you expected or desired, especially if it’s significantly lower, I think a lot of students get very anxious, and it can be very difficult to think really logically when your body’s all kind of in a disarray.

John: For many years, when I gave back exams, the students who did well would sit there and they’d be really happy and really not focused that much on going over it and answering questions, and the students that did badly just look kind of unhappy and just kind of set their scowling, which is one of the reasons why I switched to two-stage exams, where they took it first individually, and then it was such a different experience. I remember the time I took a short video clip of the students in the second stage, and I shared it with Rebecca, because it was just so impressive to see them actively discussing things and working through it, because it was so different than earlier experiences going over exams in class.

Sarah: Yeah, I love two stage exams.

John: And I’m reinstituting them after moving away during COVID [LAUGHTER] in one of the classes that I’m teaching. I’m looking forward to that again. You mentioned before that one of the main barriers that faculty face in trying to implement things was time, and certainly that’s a major factor. But are those the only barriers that faculty face when they try to change their teaching techniques, especially if they’re younger faculty, junior faculty in departments which may not all be using these types of techniques.

Sarah: Right off the bat, you identified one of the major ones that we heard is, does your department support innovation, and is there that support? And another thing that came up a lot, and I see this a lot across all my work in different institutions, is students [LAUGHTER] often resist innovations. And I think health science students tend to be disproportionately this type of student who is used to a certain level of grades and a certain level of positive feedback from their secondary education, and then they hit college and they want to do that same thing. And this has always worked for them, you know, these big exams, memorizing stuff, putting out the answer, like that’s worked for them in the past. And so I find when I’m working with faculty who are innovating, especially in the arena of grading, they get the most resistance from the students, who probably are going to get an A at the end of the day, because of that resistance. But those two things can combine, and I saw this at my previous institution, in the biology department actually, and there were some faculty who were really doing innovative stuff, both with grading, but also with active learning and kind of flipped classroom sorts of things. But then most of the department was not doing that, and they experienced more student resistance, I think, because they were in this setting where students would have that reassuring kind of traditional experience in other faculty at that institution, and then they would hit this one course with all this innovation and dislike it more than if more the department were innovating. So that’s another big one. There’s also, and I think this was in your question as well, but precarity of position, and that was one thing that we asked about on the survey so that we could contextualize the responses. Contract faculty and pre-tenure faculty, sometimes they innovate somehow, and it goes not so well, and then they really get burned on their course evaluations at the end of the semester, that that could put their livelihood at risk or their tenure trajectory. So that’s another big barrier, time, time, time, departments, really large classes, that it being difficult to increase the amount of feedback you’re giving students, especially things like written feedback or in person, audio feedback. If you have 500 students, that’s very difficult. So large class sizes.

Rebecca: One of the innovations you mentioned at the top was about alternative grading practices. And given that low grades are discouraging, and certainly one of the barriers for students succeeding in STEM fields, how does alternative grading sit with some of the recommendations coming out of your network?

Sarah: Interestingly, we were more focused on alternative grading in the early stages of the grant, we actually thought that was going to be a major focus of the grant, so the incubator was not honed in on feedback. It was more broadly assessment, feedback, and grading, and we asked all those questions about alternative grading. We moved toward feedback for a couple of different reasons, one being our sense that there was already amazing work being done on alternative grading by other networks and other authors, an entire ungrading conference, and feeling like that need was already being filled a little bit in the world of teaching and learning. But then also, we observed two things in the surveys and the interviews. One of the questions we asked is, How aware of these alternative grading methods? And then we asked about a bunch of critiques of traditional grading, and the extent to which people agreed or disagreed with them, and whether they had tried them, if they hadn’t tried them, why? And there were a lot of folks kind of innovating at the margins. So they weren’t going full ungrading, but they were including pieces of that, and there was some resistance to it. We heard from some people that they were not interested in it because of a variety of reasons, and so we didn’t see as high interest as we might have expected on alternative grading. But then we also saw that people were most dissatisfied with their feedback practices. So when we asked people to rate how satisfied they were with their current assessment feedback and grading practices, we saw overall, a fair degree of satisfaction, but much lower for feedback, significantly lower than for assessment or grading. And this came up on the interviews as well, that we heard a lot of dissatisfaction. People would often wince, [LAUGHTER] when we’re like, “What about your feedback? How satisfied are you with how often and the types of feedback you give?” And they would be like, “Oh, I should do better.” [LAUGHTER] So we saw that, both on the survey and the interviews, with such a high degree of interest in improving feedback compared to assessment and grading. And then also, we felt like all the alternative grading folks were already doing a great job working on that, and we thought we could maybe contribute more and have more of a novel contribution if we honed in on feedback instead.

John: So this project has been going on for quite a while now. What are the next stages?

Sarah: The next stage is very exciting. So the twigs, the toolkit working groups with the five focuses, have been working really hard, and we’re so grateful to them developing a draft of their toolkits. And then in May, we’re going to gather at Simmons University in Boston, although it’s going to be a hybrid meeting. So some folks will be joining virtually, but a lot of us will be in person, and the toolkit working groups are going to present their toolkits to the rest of the network, so not just people on twigs and the steering committee, but also people involved in the network more generally, and we’re going to give them, wait for it, feedback, [LAUGHTER] feedback on their toolkits, and then they will have a whole another year to refine the toolkits based on the network’s feedback on the toolkits, what people would see is useful to include or ways they could shape or tune the toolkits. And then there’s a last stage of the grant. The last two years of the grant is actually, and this is one of the reasons why I’m really happy to be talking with you today, maybe it can spark some interest out there. We also are going to offer some money from the grant to form regional hubs to apply for some funding to use the toolkits in local events. And so we imagine maybe there’d be like New England, Midwest, West Coast, and a couple different institutions can gather together. There’s a few stipulations. There has to be at least one community college or minority-serving institution involved from a non-deficit framework. There has to be multiple institutions interested, someone willing to host, but they can apply for these kind of vouchers to use, to bring in a speaker, or pay for some food or something in these regional events, to use some part of the toolkit in an event regionally.

Rebecca: It’s an exciting new development.

Sarah: Yeah. I like the idea of it. You just give people money to improve pedagogy and gather together. It’s like rare. [LAUGHTER]

Rebecca: It’s a nice luxury. [LAUGHTER]

Sarah: Yeah.

Rebecca: Weeding out is not just a biology thing. [LAUGHTER] It’s common in a lot of different STEM departments. Do you see that the recommendations from the TUnE-Bio project to be applied and expand maybe to these other disciplines as well?

Sarah: Absolutely, and we know it’s not just STEM but certainly feedbacks everybody. And we’re focused on undergraduate biology because that’s what the money’s for, [LAUGHTER] but a lot of us have at the heart, we just want to improve pedagogy across the board, and a lot of the toolkits, as they’re being developed, because we’ve been meeting with the twig co-chairs regularly… I think the classroom assessment technique twig is a little more biology focused. I think they’re developing actual biology modules that people can use in intro bio, but a lot of the other ones, certainly the seven principles of asset-based feedback and how to get students be motivated to implement feedback are not going to be biology specific, and are going to be useful to anyone teaching anything, I think. And the toolkits will be distributed through the regional hubs, but they’re also going to be just downloadable. We’re going to put them on our Qubes site. Qubes is a open website run by the NSF, and we have a membership. So all of these toolkits, people and teaching centers can download a workshop in a box. People who are teaching in their individual class can download the peer feedback, how to train students to give better peer feedback, so they’ll be widely open to everyone, and a lot of them are not going to be biology specific or stem specific.

John: When we were talking about the AI groups, you didn’t seem quite as enthused about the AI twig. Could you talk a little bit about some of your concerns about the increasing reliance on AI in higher ed?

Sarah: Yeah, we could have a whole thing about AI. I don’t want to have a whole [LAUGHTER] conversation about AI, but Pooja Agarwal, I don’t know if you know her, she does retrieval practice, is a widely known speaker in higher ed, but we had a bunch of meetings. We were going to do an edited collection called F-A-I, and it was all going to be just critical AI. So I’m not a fan of AI. I do think there are roles for AI in feedback, in particular, and I’m not completely resistant to that. And I think that if a student were studying alone in their dorm anyway, but there was some kind of responsive tutoring type of program that instead of doing flash cards or just underlining their notes again, they had this experience that was differentiated to them and where they were at in their learning journey, that could be very effective. My concern is that we’re going to instead replace not the studying alone in the dorm with AI, but a student coming to office hours and sitting across from an instructor and having this one-on-one conversation and developing a relationship and, oh, you just happen to also talk about an internship that the student could apply to, or students working together in small groups and giving each other feedback. So if AI is like, on top of all the other stuff we’re doing, like, I think it could be powerful and interesting.

John: Those are really good points. On the other hand, just to bring up one counter argument, we do know that first-generation students and students from historically minoritized groups are less likely to go to office hours and ask questions, and also maybe less likely to ask questions in class because of imposter syndrome or some concern that they don’t want to reveal that they don’t understand something. If they have a Socratic tutor, which is available 24 hours a day, seven days a week, they can ask it anything without fear of embarrassment, which may help them build up more confidence, so that they can then approach the instructor with questions, without being as concerned about not being able to phrase things well or revealing that they don’t understand things.

Sarah: That is a good counter argument. I would still go with the additive one, though, like, okay, let’s make that available to them, but let’s not stop all of the efforts that we do to try to draw them into office hours and into the work, into the relationships in the class, as long as we don’t, then be like, “Oh, it’s fine. I don’t have to do any of that inclusive teaching stuff anymore, because we have this chatbot,” additive, not subtractive, um hmm. [LAUGHTER]

John: And a great reference on some ways of creating a safer environment for students, a more comfortable environment where they feel more comfortable talking to their instructors is this book, I think, Mind Over Monsters is the title. [LAUGHTER]

Sarah: I may have heard of that one.

Rebecca: So we always wrap up by asking: “What’s next?”

Sarah: For the grant, the May meeting. This is definitely for me and my co-PI, Michele Lemons, and a lot of my steering committee and the twig co-leaders. This is going to be an intense four months I guess, now. So that’s the big thing, meeting together. I’m hoping it’s also gonna be really fun. I think that the network’s supposed to be a network, and I want everyone to feel like they belong to this, like community. But so far, you know, the twigs have been working kind of on their own, and the subcommittees of the steering committee. And I’m really looking forward to having everyone working together and playing together a little bit in the evenings, probably. So that’s what’s next for the grant. In terms of what’s next for me, I just signed a new book contract.

Rebecca: That’s exciting.

Sarah: So it’s called Where are all the Happy People? So it’s a psychology book, not a teaching book, and it’s about the fact that no one is happy, and how can we be happier? And so I go to different spots to try to find happy people and then build a model of happiness. So some of those spots are the nunnery. So I interviewed some nuns. Nude beaches is another. The college classroom, actually is one, and I interview Bryan Dewsbury for that. Burning Man is another. So I’m Lithuanian in part and there’s a burgeoning pagan revival there. So I’m hoping to get over to Lithuania with my sib kid who has been learning Lithuanian.

John: A somewhat different sort of research. That sounds like it could be a lot of fun.

Sarah: I’m hoping so. I just wanted to spend the next three years thinking about happiness.

John: Happiness has been a little sparse for academics, especially for the last year.

Rebecca: Well, I hope we find out where all the happy people are, and we can join them.

Sarah: Meet at a nude beach. [LAUGHTER]

John: It is 15 degrees right here today…

Sarah: Yeah. [LAUGHTER] maybe not. [LAUGHTER]

John: …or that’s the projected high later today. It would have to be one of those polar bear ones.

Sarah: Mmm, doesn’t sound safe.

Rebecca: …or happy. [LAUGHTER]

Sarah: No. Safe or happy. Shall be tucked in around a fireplace about now, I think.

John: Well, thank you. This is a wonderful project, and we’re looking forward to seeing those final toolkits. And I’m wondering if maybe there might be some TUnE math and some TUnE chemistry and TUnE economics and other projects in the future coming out of this.

Rebecca: Perhaps we’ll just tune the academy.

John: That would be much better.

Sarah: Oh, thank you both so much.

Rebecca: It’s always a pleasure.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing Assistance provided by Fred Llerena.

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428. The Science of Learning Meets AI

The widespread adoption of and the rapid evolution of generative AI platforms have created substantial challenges for faculty in how we assess student learning. In this episode, Lew Ludwig and Todd Zakrajsek join us to discuss a new resource they have created that is designed to help faculty use AI to efficiently support teaching practices based on the science of learning.

Lew is a Professor of Mathematics at Denison University, where he served as Director of the Center for Learning and Teaching from 2020 to 2025. Much of his recent work has focused on innovative methods for utilizing generative AI. Todd is an Adjunct Associate Research Professor at the University of North Carolina at Chapel Hill, from which he just retired a few months ago after 17 years. He is also the Director of the International Teaching Learning Cooperative and the Director of four Lilly conferences on evidence-based teaching and learning. Todd is the author of many superb books, and has published six books (so far) in the past five years.

Show Notes

Transcript

John: The widespread adoption of and the rapid evolution of generative AI platforms have created substantial challenges for faculty in how we assess student learning. In this episode, we explore a new resource designed to help faculty use AI to efficiently support teaching practices based on the science of learning.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

[MUSIC]

Rebecca: Our guests today are Lew Ludwig and Todd Zakrajsek. Lew is a Professor of Mathematics at Denison University, where he served as Director of the Center for Learning and Teaching from 2020 to 2025. Much of his recent work has focused on innovative methods for utilizing generative AI. Todd is an Adjunct Associate Research Professor at the University of North Carolina at Chapel Hill, from which he just retired a few months ago after 17 years. He is also the Director of the International Teaching Learning Cooperative and the Director of four Lilly conferences on evidence-based teaching and learning. Todd is the author of many superb books, and has published six books (so far) in the past five years. Welcome, Lew, and welcome back, Todd.

Lew: Thank you.

Todd: Thank you, Rebecca,

John: Our teas today are:… Lew, are you drinking tea?

Lew: I am. I’m usually a coffee drinker, but I have a lovely Cha Atiso. This is a Vietnamese artichoke tea, which a student brought back for me from the summer, and I’ve really, really been enjoying that. So that’s my tea for today.

Rebecca: What does it taste like?

Lew: It’s got a little sweetness to it, which I like. I don’t put anything in it, but I really enjoy it. And it’s supposed to be healthy, no caffeine, all that sort of stuff.

John: And Todd, are you drinking tea?

Todd: Yeah, I went with a roasted dandelion spice tea. It says detox on the package, and I can always use a good detoxing. So I’m doing that and sipping it from my favorite cup. Mmmh.

Rebecca: That’s right, the tea for teaching mug.

Todd: I have my tea for teaching mug here. I never leave home without it. Actually, I never left it home. [LAUGHTER]

John: And Rebecca?

Rebecca: I have a Swiss Breakfast tea, John.

John: Very nice… on a chilly morning here in upstate New York. And I have a ginger peach green tea today.

Todd: Wow. Quite a variety.

Rebecca: Nice, this big variety. So we’ve invited you here today to discuss your newest book project, The Science of Learning Meets AI: A Practical Faculty Guide to Purposeful Integration, Student Engagement, and Ethical Practice. Can you tell us a little bit about the origin of this book?

Lew: Yeah, sure. I can get us started on that. So as you know, Todd and I both have backgrounds in teaching centers of faculty development. And our national conference is something called POD, Professional Organizational Development Network. And in 2024 we were at that conference, and here ChatGPT had already been out for about two years, and we were still kind of struggling with the idea of, how are faculty supposed to use this? A lot of the questions we were getting is, “Hey, how can I use AI in my classroom?” And it wasn’t until this conference was up in Chicago in 2024 where we realized we were asking the wrong question. It’s not really how to use AI in the classroom, but instead, maybe we should be thinking about, why are we using AI?

Todd: Yeah, and actually, I’ll jump in too, because Lew went right to that technical part and everything. Another thing that happened, which is why I love it when people get together, is Lew asked me if I would help out or just talk to him a little bit about some of the science of learning, because he wanted to really work on this project with bringing AI into this, explaining why it’s important to use it. And I said, “Well, that’s interesting, because I just started to outline a book that I want to do on AI, and I was going to check with you about the AI part, because I got the learning part down.” [LAUGHTER] So we just said, “What the heck, we’ll do it.” And Lew didn’t know me very well at the time, so he said, “Yes.” [LAUGHTER] Anyway, we made it through. But the big thing was that we really wanted a book that would teach faculty and help them understand the learning theory behind why the learning happens and everything else, and use AI to augment that. So this isn’t like using AI to do things, it’s using AI to help us do the things that we’ve been doing.

John: You start the book by acknowledging that faculty didn’t ask for this AI disruption or for any of the evolutions of this. Why start with this instead of with excitement about AI’s potential?

Lew: Oh, it’s called learning from one’s mistakes. So again, ChatGPT came out in November of 2022, by August of 23, I remember on my campus, we had a fairly large across the board faculty meeting about AI. It was about a half-day session. So administration was there, IT was there. And as the Teaching Center Director, I gave probably a 90-minute session on, “Hey, here’s this new technology and how we might be able to use it in our classroom.” And what happened was, just by saying, “Hey, here’s how we might be able to use it,” some folks labeled me as AI pro. So I was just pro-AI, hey, you talked to Ludwig, and he’s just going to be all in on AI. And that was almost like my scarlet letter [LAUGHTER]that I was wearing around campus to some folks. So I learned from that. So any of the workshops, any of the things I do since then, I start off with this exact phrasing, just to kind of level the playing field here, I say “You didn’t ask for this. You didn’t request an unregulated, untested, and rapidly evolving technology to suddenly upend education and nearly every other sector of society.” Now, I’ve been delivering that, like I said, quite a few workshops. I did it back in August for a group of high school teachers in Richmond, Virginia. They applauded me. [LAUGHTER] So it’s just kind of getting it out there and acknowledging it that, yes, this is a disruptive tool. We kind of have to acknowledge that grief and then kind of move on from there.

Todd: Yeah, I would say too, is really, I really thought Lew had a good handle on that part of it. I wouldn’t have put that in there, quite frankly, for the book here, and Lew was pretty insistent that it go in there. But as we started talking it through, I realized I always started any COVID workshops this way. When we were doing emergency remote teaching, if you just started talking about emergency remote teaching and didn’t acknowledge just all of the stuff that happened with COVID at the time, on the number of deaths, for instance, you can’t just jump into something and ignore it. So I think that Lew had a really good point here, and then I started pulling together what I did with that, with what he was talking about. And I think the whole concept is we started thinking within the book of, how do we do this? To recognize people are grieving, that they’re struggling, and we say it’s hard that we see you there, the frustration is real. Teaching is changing, just as it did with COVID. We don’t have a choice of that, and AI isn’t going to go away, so let’s recognize what’s going on, and recognize even some of the impact of AI, and then from there, then move on with a sense of acknowledging what’s going on.

Rebecca: It’s almost like you’re modeling some good teaching practices there. [LAUGHTER]

Todd: Kind of. Periodically it happens.

Lew: We try.

Rebecca: This adapt, create, embed, progression, the ACE model, is used throughout the book. Can you talk a little bit about what this looks like in practice for a faculty member who’s never used AI?

Lew: Yeah, so this work kind of, again, developed that starting point back in November of 24 after we had that initial meeting in Chicago and started to realize we needed some early wins for faculty. So we got a lot of faculty out there who are kind of hesitant about this new technology. And if you gave them something like a prompt or something that didn’t work, they’d be just like, “See, I told you, this AI thing, it’s a disruptive thing. So we’ve been workshopping this quite a bit early January onward, and refining the model. Originally, you ready for this? It was, instead of adapt, create, embed, it was improve, create, integrate, which, as an acronym, is icky. [LAUGHTER] We didn’t think that would land very well, so we went with ACE instead. So for example, the adapt, that’s kind of the easiest win. So what you do is, you take an existing assignment, you have that assignment that it’s a big project, but for some reason, it just doesn’t land with students. They always hand in something a little bit different than what you asked for. Well, that assignment’s begging to be TILTed, right? So Mary-Ann Winkelmes has this idea of transparency in learning and teaching, and it’s a technique that you can do for your assignment to make sure that your students understand why they’re actually doing the assignment. And it’s a kind of a three-step process. And AI is great at TILTing your assignment, and faculty members can take an existing assignment that they have. They’re experts on that assignment. AI can help them TILT that with this framework, and then they can see how that might better fit into their language for their course, and help improve what their students are doing. And the TILT framework has been really good. It kind of raises all student boats. But in particular, it does well for underrepresented and first-gen students do exceptionally well with this. So again, just kind of an early win with the adapt model.

Todd: Should mention quickly that we kind of started out and we based a lot of this on Ruben Puentedura’s model of SAMR, which is the substitution, augmentation, modification, redefinition for all of technology. If you have any new technology out there, you started with substituting it. How can we do this instead? You can augment it. And that’s a really interesting model. Didn’t quite work with what we were after. And so Lew had lots of workshops he had been doing. And so we kind of used that as a foundation. But as Lew just pointed out, the adapt, which would be similar to that original substitution, is to get you rolling. And I’m going to go ahead, Lew and take I’ll take the other two real quickly. So next is create. And essentially, it’s looking at how you can do something that you really couldn’t do before. And there’s lots of things that are out there that we could use for this one. We’ll talk a little bit more about those, probably later on, as we’re looking at some specific examples. But when you think about what you can do and couldn’t do before, one quick thing is a daughter, one time, was struggling with something, and she said, “Can you help me?: And I said, “I’m busy right now. I can help you a little bit.” 15 minutes later, I said, “I can help you now.” She said, “Oh, took care of it.” I said, “How’d you do that?” She says, “Oh, I just got on a group, a small online group, and then we solved it together.” And I said, “Who was it?” And this was years ago. And she said, “Oh, there was a student from Germany, one from South America, somebody from Nebraska, and myself.” That was never possible before. And so many things in AI do things like that. They’ll set up things we could never do before. So the “create” is, how can we do things different? And then once you’ve adapted it and created it, the next thing is, just embed it. Just make it part of the regular aspects of your course. How do you do that? And what we like to point out over and over again is that, you do this wherever you’re at. If you’re just getting started, you just adapt some things you’re doing. And that’s where Lew is really good at setting some of those things up. If you’re a little bit nervous, just take something you’ve always done, do it with AI, do a little bit better, faster, and then just get started, but don’t get overwhelmed with it.

Todd: And the other thing about doing with something you’ve already done is then you’re the expert, and you can critique what AI is doing for you. I wouldn’t go off and learn a brand new subject you learn using the AI, because I’d have no idea. But again, coming from that wheelhouse of your own expertise, don’t forget you are the expert with the content as well as your students and context. So keep that in mind whenever you’re dealing with AI.

John: And that example you use of the TILT model is a good one. I’ve given workshops to faculty about it, and a lot of people were reluctant to put in the effort to make all those changes. But when you show them how AI can do that, or better yet, if you share perhaps a prompt, or if you share a GPT or a chatbot tool that they can plug in their assignments to and their learning objectives, and it can do it for them, or at least give them a starting point on it, it makes it so much more likely that people are going to do that. So you talked a little bit about the ACE model, which is used throughout the book. But could you tell us a little bit about the organization of the book?

Lew: Yeah, sure. So the book’s kind of broken up into three parts. The first part, we call it “Creating your own Gen AI roadmap.” And again, chapter one, you didn’t ask for this, but here it is. So just kind of, again, acknowledging that situation that we find ourselves in and how to move forward with it. Then in chapter two, we start talking about what it means to think about shifting our teaching in the world of generative AI, so the idea of being transparent with our students and how we’re using it and setting up good communication with them, and then also the idea of as faculty members, as we’re embracing this, we really have to be curious and flexible as we work with this technology. We’re always pushing our students to take risks, be flexible, be curious. Well, guess what, that shoe is going to now fit on our foot as we try to move forward with this model.

Todd: So that kind of got things up and running, and then we kind of ran right into the learning aspect of it. Just to give you an idea of the book, I guess is probably the easiest way to do this is. Chapter four is on universal design. I’ve been a long time advocate of universal design, and I think AI can be now done to do things that were really challenging before. So we did dedicate an entire chapter to universal design. Then chapter five, we really thought community was important. So chapter five is about building classroom community and trust, looking at belonging, engagement, and integrity, kinds of issues. Chapter six comes into backward design, a huge aspect of teaching and learning, kind of knowing where you’re going as your goal. And then chapter seven is looking more at learning science of cognitive load, attention, spacing, interleaving, retrieval practice. And that’s the “building the foundation section.” And the last section of the book, section three, is for kind of deeper learning. We looked at some things about teaching and learning that just we know works well. So chapter eight is guiding students from novice to expert. We put together in that section zones of proximal development, scaffolding, and the gradual release of responsibility as ways of helping students kind of go into deeper aspects of learning. Chapter nine is on strategic learning. So we looked at transparency, TILT, cognitive apprenticeships, metacognition and those types of issues. And then we wrapped it up with chapter 10 of persistence and motivation. So there we pulled together the concept of self efficacy, growth mindset and self-determination theory. So the chapters have two or three strong learning principles to them, but they’re put together in ways that not only help explain the individual things, but also overall this concept, like persistence and what kinds of things go into that. And then the last thing I’ll say, since I’m on a roll right now, is overall, I think we really thought it to be helpful, repetition is just helpful for learning, right? We know that. And so we did is we built a book that has strong repetition. So chapter after chapter, we basically started with chapter four, and then we go all the way through chapter 10, and it’s set up so that there’s always an opening story or anecdote to set it up, which we’d want to do for just getting the learning focus there. Then from the model, there’s an improve activity, and then we look at the science components, and from there we might be looking at metacognition or Bloom’s taxonomy or something. So that comes into the second two parts of the model. Here we have a create and then embed, those activities are in there. And then we talk about the ACE model, and we have more activities that come into there. Then we look at risks and guardrails. And again, we’re going to acknowledge everything has risks to it. We’re not going to just pretend like it’s all great. And then we have a conclusion, pull it together, and we finished every chapter with discussion questions and reflection questions. So if you’re a faculty developer and you want to use this as a book group, for instance, we’ve got your discussion questions already built in, but for having faculty work through it, there’s reflection questions in there. The last thing I’ll mention is that we talked really extensively about how to cite this stuff, big APA kind of person, but we really wanted this readable, and so we went with a model where we put everything in as end notes, so as you read through it, you don’t get the standard citations with dates all the way through it, it just reads like a novel, almost, and I think it’s really easy to read. So you don’t have to read it cover to cover, although you should, [LAUGHTER] maybe three times. You can just go through and pick out what you like, but if you do go through, we’ve actually built it so that you start with simple kinds of examples and move to more complex ones. And Lew is masterful at putting together how to like, ask questions and do prompts and specifically with a separate font in there, we just have paste this in and add your components, and it spits out the answer. Pretty cool.

Lew: So what Todd’s explaining there is, we have a lot of different techniques. We have things called activities as well as examples. So each chapter has six activities, which are kind of page-long discussions of how you might use AI. And it’ll start off with some prompting, like he said, in that different font, but you put it in your context. We don’t do this: “Oh, a biologist would do this. Now you do it for yourself.” You actually, in the context of this thing, put in your information so you’re directly dealing with it. So we have, like I said, six of those for seven chapters. So 42 of those things. Each one could be a workshop. And then also we have smaller examples for seven chapters, we have 12 smaller examples, which is another 84 prompts. Those aren’t prompts, those are just more ideas. But Todd mentioned this idea of meta prompting. We give you a way to turn those examples into a prompt for yourself. So you’re using AI to create your prompt for you, and I think it’s called meta prompting, so that’s what he was referring to there.

Rebecca: There’s a lot written about the science of learning and Todd, obviously, you’ve written some books on the subject. How did you decide what science of learning concepts to include?

Todd: I just put a whole bunch of stuff in there. And then Lew started saying, “Nope, nope, nope.” [LAUGHTER] No, we got along great on these things. It basically boiled to Lew does tons of workshops, I do workshops, I think I’ve been at 300 campuses so far. So I looked through the different presentations I’ve given, and when things kind of hit and when they didn’t hit. Cognitive load is something a lot of people just don’t quite understand, and is a huge foundation of learning, because within cognitive load, you’ve got automaticity and schema activation and all kinds of different things that are practicing retrieval kinds of things. So I just went back to the warehouse of stuff and picked the things that worked, which is what I still think is really, really valuable about the book, is that these are things that work, and it’ll help explain them as you go. Big things like the metacognition, Bloom’s Taxonomy, cognitive load, automaticity, practice, retrieval, interleaving, the kinds of things we’ve heard. And it was hard, I will say, Rebecca, it was really hard. There’s a lot out there. I mean, obviously I got what is it a 270 page book on learning. So how do you just fold in some of that? And so, it doesn’t have everything, but I think it has the big things.

John: So you mentioned before that AI allows us to do things that we couldn’t easily do before. Could you give us an example and walk us through that in terms of how AI could be used to create learning activities that could not be done as easily in the past.

Lew: Sure, individualization of instruction. I’m at a pretty small school here at Denison, but I could imagine, wow, what if I was teaching a class of 400. So I might end my class here at my small school with, you know, a muddiest point, I get 20 responses. I look those over, what did students struggle with that day? And then next class, I can kind of address that. Imagine if you had 400 students, what could you do with that? Well, now, with AI, you can take this classroom assessment technique of muddiest moment and, on the spot, have students type their prompt into, say, a Google form or something like that, and as maybe the students are off working on some kind of project or discussion together, within two or three minutes, you could use AI to organize those muddiest points and give me the top three things that these 400 students are struggling with, something that was just was not even feasible before. So I think you know, at scale, this thing can be really useful for those types of examples. And with regards to CATs, the classroom assessment techniques, Todd’s got a book of like, 50 of these things, right? So there’s so many things that you can kind of do on the fly that AI can then help you with, kind of the managerial stuff, just the paperwork thing of keeping track of that stuff so you can respond in the moment for what your students are doing.

Todd: And Lew, I appreciate you mentioning that book, it’s interesting, got me thinking real quickly here. So that book is Classroom Assessment Techniques, which not too many years ago was like the standard. Everybody had a copy of this thing, and they did because the first edition was 1988 I believe it was, second edition was 93 though, and in 93 was right about the time we started talking about learning versus teaching, and how would you know if students are learning? And along comes Angelo and C ross, their book was out, I guess, already, and so it explained it. I mentioned this quickly because I then worked with Tom Angelo to do the updated version, the third edition, which is now out 30 years after the second edition. And the second edition was still selling, but we were removing things like “hand out papers to the students, have them respond on the muddiest point, and then take them home, but make sure you don’t ask questions that you don’t have time to respond to. Don’t ask elaborate things because it will take too much time.” Lots of stuff about CATs was about being careful not to overwhelm yourself. And if you think about that in the old days, that’s what you do. Pieces of paper were handed to, you’d read through them and everything. So we took out things like “share your results on the overhead protector,” and those are gone, and then we replace them with things like Lew was saying is, just put your students into a small think-pair-share, have them respond to this thing with a PollEverywhere or something, and then while they’re talking, then come up with the four muddiest things going on, and then teach those in the second half of class. And so, all of those classroom assessment techniques that we’ve been using for 30 years can now be made easier with AI. So I’m really excited about this.

Rebecca: You mentioned earlier about the chapter on Universal Design for Learning, and I know you’ve been a long time advocate, Todd. Can you talk a little bit about how faculty can use AI to design courses that are better for diverse learners and really have kind of access in mind, rather than retrofitting accommodations.

Todd: Yeah. So number one, I think the thing to point out here is, so I teach, this Lilly teaching conferences. They’ve been around for a long time. There’s different types of sessions that I’ll include in the program, and some of the sessions, and I talk to the presenters about this all the time, some of the sessions are just hot topics that you know are just going to go: engaging students in asynchronous classes, it’s going to fill a room. How to do active learning strategies, going to fill a room. They always have. Sometimes you have topics, though, that you put on the program because you have to talk about them. And if you don’t put them on the program, even though they’re not popular, then it doesn’t get picked up. And I remember putting sessions in the late 1990s, early 2000 about diversity that would have four people in a room, and individuals would say “there’s only four people in there.” And I’d say, “We’re still putting it on the program.” UDL was another one, anything on universal design, I can tell you I’d put in the smallest room in the conference, because it would be a great session, but it would draw like seven people. And one of the reasons it didn’t draw many people, and quite frankly, across all of higher education, people might love the concept, it’s really hard to do. If we’re going to do things like multiple means of expression, which is a key factor of universal design, it can’t just say that you can share your results in one way. What if you’re not good at putting text together, but you really have the idea down. So maybe you create a video, or maybe you have a play, or maybe you get in small group and have a discussion. Multiple means of demonstrating the information, so presentation, how do I explain this in a way that people can get it that’s always been hard, it is now being made really easy because we can go to AI with the correct prompts, and we can come up with multiple ways of expression, multiple ways of presentation. Lew was talking about having a class of 400 with the students in there. You could say to the students: “Here’s what I want to do. We’re teaching metacognition. Everybody think of something you love to do now, type into AI. I love to blank. And I want to learn more about metacognition so that I could explain it to my friend.” So I could type in I love long distance bicycling, and I want to learn metacognition. Rebecca might put in that she loves rock climbing. John might put in like he likes baking, and Lew might probably put in there he likes street fighting. [LAUGHTER] So you put those prompts in there, and then what pops out is everybody in the class gets to learn it in their own… I mean, what could be more universal than that? And so I’m just really excited about it. And we did the whole chapter, and Lew and I talked about it, and I think at the beginning I wasn’t even going to do a whole chapter, and he said, “Yeah, we need to do a whole chapter.” And so I think AI will open the gate so that people can finally do universal design in a way that’s meaningful without overwhelming them.

John: Faculty have been overwhelmed, first with COVID and now with AI, and many other things that are happening externally. How does your book address the reality that faculty don’t have enough time or energy to learn a whole new technology on top of everything else they’ve had to learn in the last several years.

Lew: Fair point. I think the thing we need to start with is the idea that we want to be careful and not this chase the technology, because if you do that’s going to make things worse, you’re going to get frustrated. What you want to do is try to use the tool to make things better. So I often talk about there’s 2, t-w-0, too many shiny objects out there. And one of the ideas with the too many shiny objects is there’s all these little prompts that people: try this, try that, try that, and that’s really frustrating for faculty, because it’s just a little bit beyond their skill set of what they want to do. It’s a biology example, and they have to bring it down into what they want to do. The other thing is, with the shiny object, is the idea of treating AI like this big shiny hammer, and then you’re running around trying to find all these curricular nails to try to beat down of how I’m going to use AI to do that, that is just such the wrong way to address AI, letting the technology lead first, you’re just going to make things worse and you’re going to stress yourself out even more. So what we want to do is we want to go back to basics. We want to think about backward design: at the end of this course, what should my students know? What should they understand? Then how am I going to assess whether they understand that? And then finally, how would I go about teaching that? And that’s where we decide whether AI might fit in or not. So again, it’s not the idea of trying to get AI in the classroom. I think as faculty members, we want to be really careful about how do we use it to be a creative assistant for us and kind of level up and improve our existing teaching practices. And the thing is, the book, all these teaching practices that we’re doing, these are well reasoned research techniques that have been laid out in chapters four through 10, backward design, ZPD, TILT, all the fun letters, UDL, all that stuff. But again, it’s trying to use that in a practical way, trying to develop your teaching skills more, and then just using AI as the tool to help you with that guidance along the way.

Todd: I wrote an article years ago. I was like, we just want to be well, that’s all we want. [LAUGHTER] So this concept of what we’re doing, if we think about what we do as faculty members out, as Lew pointed out, we’re chasing things a lot of times, and that’s just a problem. And this goes back to… remember when clickers came out, student response systems. Everybody’s like, “Oh, I want to use a student response system because my colleague’s using it,” and as faculty development directors, we are constantly trying to talk people out of using technology unless they actually needed it. Backward design. Why do you want to use it? Student response systems are always fun, because it’s like, I want to use student response systems and my friends and I would say, “Okay, so why?” “So we can gather student responses” and I’d say, “Okay, you know, you could just have them raise hands. Like, how many of you think this?” They say, “Well, yeah, but I want to know like anonymous.” I say, “Okay, have them close their eyes and raise their hands well.” “But I want to tally them.” “Alright, well have them close their eyes, raise their hands and then count them.” I mean, these things are not necessary. But then when they start to say, “Okay, but I want to know some qualitative aspects, what are they thinking and why?” Now we can use the technology. So I think that’s helpful. And so when you go back and look at what really takes time in teaching, those types of issues do, I can’t, like have everybody talk about muddiest point, because I don’t have time to read through those. Now you do, because AI can skim through those quickly for you. If you’re prep timed, you put in exhausted in there. I was exhausted because when I taught history and systems of psychology the first time, I would be up from 10 o’clock at night till two in the morning putting together a lecture. And I knew the stuff generally, but I had to go look for it. But I spent all my time looking. I was looking for the information. Then I’d find it, and then I would embed it. With AI I’m going to get the same kind of information, I’m just going to get it really faster, and because I can pull that together with the knowledge base I have, then I can put it out there. So prep time is going to drop, grading time is going to drop in terms of figuring out how to read through these types of things. Underpaid, by the way, I think you might have mentioned that one, just so we can’t do a lot about that. So sorry, yeah, for the underpaid part. But I want to finish with one quick thing here. There’s a book that I did actually get accepted for publication that I have never written. The contract was signed in 1994 and the reason I mentioned this is that what I wanted to do was take a stats book, but I thought we’re teaching statistics without kind of framework. So what if we took the old psych studies that were really the cool studies, took the data set, retrofit a data set basically that would give you the same data. Have students analyze the data, come up with what they’ve got, and then talk about what the data means, and then explain to them how that fits into like, that was the Bobo doll experiment, where Bandura had models hit a bobo doll, a blow up doll, and then they give the same doll to kids and see if the kids would do the same thing. You could replicate these studies while learning statistics. And I was told I couldn’t write that because most faculty members teaching statistics don’t know those studies. And so at the time, there was no way to do it. Now, with AI, you actually could, it would be very easy to say, I want to know more about Bandura’s Bobo doll study. And so I think that’s some changes that we’re going to have that are really going to be helpful.

Rebecca: One of the things that you’ve both underscored is leaning on the faculty member’s expertise because they can validate what’s coming through the AI tools. [LAUGHTER] So you’re not worried about the reliability, because you have that expertise, and the things that you’re suggesting people use are relying on that expertise to some extent.

Todd: Yeah, and that’s the thing. They’ve got the expertise. How can we help them with it? And help them by putting together a book that’s very modular. You can just go through and work through these things. Actually, the activities are set up, and Lew did this, I thought it was fantastic, they’re put in boxes. You could pull the whole thing out and make a workshop out of it. Very, very easy to pull this stuff out and do that, but by making these things very consistent, we know that reduces cognitive load, which is going to reduce some of that exhaustion and extra work.

Lew: And the other thing is, the examples were set up in a way, again, that they relate directly to what you’re doing in your classroom, your assignment, your example, not something that, again, the cognitive load that you have to translate from somebody else’s existing example. And again, that’s one of the neat things that AI allows you to do. Because now, instead of me saying in a book, do this example, I can put a little bracket there and say, insert your example from your discipline here, and all of a sudden you have individualized learning, individualized application for the teaching technique that you’re trying to do using the power of AI to do that.

John: And one of the things I’ve seen is that we have a lot of people attend workshops on AI just to learn how they can block it and how they can ignore it. But when the focus is on activities that actually allow them to do their work more productively or more easily or more efficiently, they get a bit more excited about it and then start thinking about how they could be using it to improve their courses and to improve their lives in general. And I think that’s a nice way of introducing people to AI who are initially somewhat resistant to it.

Lew: Yeah, it’s critical. I don’t think you can walk into a classroom and say, okay, students, we’re going to use AI today without understanding it yourself. And you hit the nail on the head there, the best way to do that is to do something that you are the expert in, and then learn to explore this technology on your own. It’s going to take a couple years for us to kind of get used to what’s going on. I think in a few years, we’ll be ready to think about how we might use this with students and things like that. But as a faculty writ large, we’re going to have to have a better understanding ourselves of just how to use those tools. And I think to your point about students misusing it and things, we can now have better conversations with them if we better understand how to use the tools ourselves for our own work.

Todd: And how people learn. [LAUGHTER] So we have tht published in the book too. That’s what it does. It helps you understand how these processes work, and so you learn about things like cognitive load. So then you can not teach cognitive load, but using that, build assignments that have lower cognitive load. That was one of the things that Lew came up… I love that one assignment that you came up with, Lew, is basically it’s you load the assignment in and says, “Where are students going to have high cognitive load in terms of really struggling with this assignment? And then AI says, “Oh, they’re going to stumble here, here and here.” And then you start rewriting it. It’s not a lot of extra work for you, and it helps the students. You don’t have to go through examples and have all those students lined up outside your door in office hours, saying, “I don’t understand why this thing works.” And so yeah, I think it’d fantastic.

Rebecca: I appreciate the idea of meeting people where they’re at, for sure. You mentioned something about a couple of years, maybe we’ll be ready to really integrate it with student learning. In five years, maybe, what does good teaching with AI look like?

Todd: This is kind of a fun one. I actually was asked to do a workshop recently that they were looking for a speaker, and they said they were interviewing different people, and they said, “We just want somebody come in and talk to us about what teaching will look like in 5 to 10 years.” I said, “If somebody tells you that they know what teaching would look like in 5 to 10 years, I would not ask them to give that presentation.” Because I don’t know, we used to talk five years out. Tom Angelo’s book with CATs was good for 20 years and still kind of worked. That stuff’s not going to handle anymore, because just in the last three to four years. Can you imagine, just before AI took off, go back just a few years and predict that it would look like this? So I don’t think we have a clue about what overall it’s going to look like. I do think we can look at some trends, though, and we can look at some directions we’re taking, because we can’t just throw our hands up and say “we don’t know,” but we want to be careful about what it’s going to be and it’s just going to be difficult, but there’s lots of good stuff coming out. And I’m going to actually ask Lew to do this, because earlier today he was explaining to me an article he’s reading. I think this stuff is fascinating that’s coming out.

Lew: Sure, I’ll jump in. So one of the things, not only do I keep an eye on higher ed and what’s going on with AI, but also the K through 12 space, and I follow a lot of folks on substack, it’s interesting. I think a lot of the discourse right now is going on in substack, as far as with AI and teaching and learning things. But anyway, there’s a fellow I’m going to try to pronounce his name, Nick Potkalitsky from Ohio, and he’s working with our Ohio Public Schools and writing about that. So the state of Ohio has mandated that by July 1, 2026 all school systems are supposed to have some kind of AI policy in place. So Nick is working with some of the larger school systems, Cincinnati, Columbus, Cleveland and things like that. And in this article he talked about on substack, he’s like, “Listen folks, this is going to be a three to five year rebuild. This is not going to be a quick policy fix. Rebuild means rethinking instruction, assessment, and curriculum, basically from the ground up. And how might we support teachers to make that happen and things like that. But teachers clearly need to be in that conversation. They need to be in that room.” This is the K through 12 space, and I think that’s only going to translate to higher ed as well. So the sooner faculty can get out there and start engaging in this technology and seeing what it works for them, then they will be able to kind of think down the road of how that’s going to affect their students in their classroom.

Todd: We have an epilogue in the book we did too after chapter 10, there’s a section in terms of what we think is coming along. And I just want to add one more quick thing here, when I think about what good teaching is, throughout my career… so I started teaching in 1984 so I’ve been teaching for a minute… and the concept here is that throughout all of that time, when I think about things I wanted to do, and what frustrated me were things that I couldn’t do because I didn’t have time or I didn’t have a way to do them. Universal design was just one example of that, but lots of things. With 400 students, I would love to ask them an essay question, but there was no way to look at that, even to get a sense of what they were writing. I would like to put students in small groups and looked at multiple versions of what they were talking about. There’s so many things out there. And now, as I start looking at what the future of teaching might look like, it’s kind of like one after another we just remove those barriers. So I don’t know what it will look like, but I know it will look better because it is removing things that we can’t do. And I will just end with my chunk of saying this is that we so quickly, when this came out, went right to cheating. Just immediately, the first articles out were, how are we going to stop students from cheating? How are we going to stop them? Students were cheating before AI came along. Don McCabe did a lot of work on this, and the proportion of students cheating in some way was off the chart. It made it easier, no doubt about that, and it does create a real problem, because if students can just jump in and do AI, then there’re many that will do that. But I don’t think we have to go there. And Lew and I talked a lot about this too, is that we shouldn’t just go there. We should start looking at opportunities. And I just talked to someone the other day who said, like, “How are we going to know when we can fully trust AI when it gives us an answer?” And that’s the kind of stuff that just drives me nuts, because when we talked about hallucinations, and so many faculty immediately say, “Oh, I put that in and it made a mistake.” So do I, sometimes. There are times when I make mistakes. I’ve heard my colleagues say stuff and I say, “That’s not true.” And so it’s no different than humans in that respect. We’re going to be able to trust it when we can look at it and we know it’s right, but it is getting better all the time. And Lew said it earlier in this discussion we’ve had here today too, is we can’t focus on what it was. We have to focus on what it can be. And I think looking forward for teaching, there’s some really cool stuff coming.

Rebecca: I think one of the things that you’re highlighting, both in your book and how you responded, is it remains anchored in the science of learning and just how we’re using the technology, and things might shift, but if we keep focusing on good teaching and learning practices [LAUGHTER] and just get them better, it’s not a surprise.

Lew: Well, it’s interesting, especially at the higher ed. We’re all trained in a very specific discipline, a very specific area. We’re experts in that field. We were never trained to teach, or very few of us maybe had that one course as we were TAs or something like that. And again, what the AI is allowing us to do is to further our expertise and our teaching and to kind of push the boundaries where we just didn’t have that training before. So I think, as Todd pointed out, I think there’s going to be a lot of opportunities just to make things better, hopefully.

Todd: Before we wrap up this question too, I’m just going to kind of put a charge out there, because Lew mentioned a little bit earlier, but in the model we have for adapt and stuff, for those of you who are listening, if you haven’t done it yet, just try dumping one of your assignments in for a topic or an area that you know students struggle with a little bit put into the AI. And the book’s got some examples on how to do this, but you could just say, “Where will the students struggle with this?” When I teach statistics, distribution of sample means, always give them fits. It took me years to figure out how to get around that, but if I was a brand new teacher and never taught before, I could put distributional sample means in there and say, Where will my students struggle? And it will tell you, here’s some places. And then, if you want to set that off to the side and then teach it and see where they struggled and make a comparison. And if it kind of lines up a little bit, you know it’s a gateway to kind of figuring out where the problems are going to be before they emerge. Wouldn’t that be cool?

John: So this is the first time you’ve worked together on a project like this. Could you talk a little bit about the working relationship and how that evolved?

Todd: Yeah, let me jump in there real quickly. Number one is writing a book is hard. I mean, it is really, really hard. And AI actually has helped us out with this too. So I wrote a book, this New Science of Learning first edition took two and a half years to write that thing. This book was nowhere near that, six, seven months, and we put a lot of time and energy into it. But the point is, it’s hard. Working with somebody is even harder because now you got to talk about what you’re doing and look at different points of view and everything else. And I think what was really cool with the way Lew and I kind of set it up is that we were coming from different directions. I had the learning theory. He was coming in from the AI, I would write a bunch of stuff, and he’d say, that’s really cool about all that metacognition. Maybe we can change it from like 3000 words down to, I think, 42, one time. And I said, I’m not explaining metacognition in 42 words. And then he gave me like 150 so we had to compromise and go through that. I would just say, working with him, he gets stuff done fast. He is really, really good at things in terms of understanding AI component. His prompting was amazing. I don’t know he’s a little mathematician-ey for me sometimes, but, you know, it was alright working with him. So all in all it was a positive experience. And I think Lew would say the same thing. So what’s the next question? [LAUGHTER]

Lew: No, it was a lot of fun. I really appreciated, as you’ve already mentioned, Todd is a very prolific author, so he was able to help us navigate all of those rough and challenging waters. So I was so much appreciative. But I think we complemented each other well, as far as what we brought to the table, and it’s exactly you know, what the book needed. And in particular, I remember Todd that GLCA, the Great Lakes College Association talk that you gave, what was the title of that? You remember something about neurodiverse learners?

Todd: It was basically how AI can help neurodiverse learners to be better learners. The title is way better than that.

Lew: Right, and Todd, he graciously used himself as an example, I will say. So a lot of things made sense to me [LAUGHTER] after that presentation Todd, [LAUGHTER] I’ll just leave it at that.

Todd: So, let me just say this. Yeah, I explained a little bit about ADHD and some of the characteristics of people who have ADHD, like time blindness, where you think something’s gonna take 40 minutes, but you don’t have a clue? Might be 10 minutes. Might be 17 hours. Who knows. You set up your task list, but you’ve never, ever finished a to-do list. I have never finished to-do list. And yeah, I went through all of those types of things and a couple of other things that I kind of struggle with. And then Lew when we finished, he says, it’s been great working with you, but I’m not sure if I’d heard that presentation first I would have signed the contract. [LAUGHTER] Well, you did it.

Lew: No, it was fine. It was fine.

Rebecca: So Todd, some of our long-term listeners might be curious what role opera played in this book. Didn’t you listen to a lot of opera last time?

Todd: Yeah, you know what? Let’s just throw this in there real quickly, because this is part of the ADH stuff that I’m learning, to whatever extent this fits in with this whole interview here. So ADHD is one of those things that, much like dealing with gluten intolerance and several other things, people think, if you’ve really got it, you must have known it your whole life. There are people who find out later in life that they have this thing. I always knew that kind of general things with ADHD which always struggled with a little bit. And I knew I had some of it. But, I mean, I was 60 years old when I was diagnosed, and so that concept there is suddenly I started to realize and read bunch of books about things like time blindness and some other things, but I had a terrible time writing. And a person, Tamara Rosier, who wrote a fantastic book, Your Brain’s Not Broken. It’s been reviewed in the New York Times and stuff, she said, “listen to opera, like, crank it up. Don’t do ear damage, but crank it up and do that while you’re writing.” And I said, “that will never work, because I have to have it quiet.” And she said, “Just try it.” And I put opera on and cranked it up, and I churned out work like I’d never turned it out in my life. And she pointed out to me that my brain’s always looking for stuff. Part of it would settle on that, and then it just kind of left this focus for the rest. Still had to have absolute quiet when I read. But for writing, that worked. I got put on some ADHD medicine, and two days after I started the medicine, I cannot listen to anything while I write. [LAUGHTER] To me, that’s fascinating, because what it now means is my brain’s actually kind of slowed down enough that that becomes a distraction. But that to me as a cognitive person, that differentiation is fascinating.

Rebecca: That is fascinating,

John: And that’s a nice preview of our next podcast that will be coming out next week. We’re talking to Karen Costa about ADHD and her experiences with that.

Todd: She’s really good.

Rebecca: So we always wrap up by asking, what’s next?

Lew: So I’m a mathematician by training, and I was just at a recent large gathering of many of our professional organizations, and for years now, they’ve been calling for, especially in like the high school setting, that we need to teach more statistics, data analytics, modeling, in other words, creating mathematical pathways, kind of beyond the calculus curriculum. The rub is, is that the way folks are trained, they’re basically trained for the calculus curriculum and not these other things. So what I’m doing is, I’m actually working with some organizations like Math for America to consider how we might take frameworks to increase the number of teachers that were able to develop these courses in these different areas. So what we’re doing is we’re looking at something I like to call AI-assisted retooling, so teachers are learning new content alongside an AI partner to expand their mathematical pathways. So, that’s kind of a really exciting thing that I’m starting to work with. We got a few case examples that we’ve been doing. I’ve been a case study myself of doing that. So, I’m really excited about that. And of course, you know, I’m continuing to work with teaching centers on workshops and things around the book.

Todd: Nice. For me, so I got another book started, because why not? These are always fun. This particular one’s going to be different, though. So I’ve probably said about as much as I can in terms of some of the teaching tips and kinds of things we do. I really think that what’s going to happen pretty soon is there’s so much stuff coming out with AI, real life stuff is going to become something I think people are going to cherish. So the project I’m working on right now is looking more at how I’ve taught in different aspects of it, but what it really is going to boil down to are things that I experienced in the classroom. They’re really kind of unique, interesting things, and then how it played out and how it could be different. And so I had a student one time have a grand mal seizure in my class. And the quick example here is I quickly ran down to a nursing station. It was just around the corner. I ran over there, got somebody, brought them back, and the nurse came back, and we came into the classroom. And I, right now, would tell you… that was my first year of teaching that happened… I would never leave a classroom when an emergency is going on. I should have said to somebody, “Hey, go to the nursing station.” So what I want to do is talk a little bit about what I’ve done, some of the ways I’ve done it, how I would have done it differently, and then how that ties into learning and different aspects. So I really want to make more of a personal story. I don’t know if anybody will be interested in it, but, you know, we’ll give it a try.

Rebecca: Sounds like an interesting book. I’d read it.

Todd: Good. I just sold one. Oooh.

Rebecca: Yeah. [LAUGHTER]

John: Maybe two. [LAUGHTER]

Rebecca: Yeah, maybe two.

Todd: No worries, but thank you, appreciate that.

Rebecca: People have anxiety about, like, what if X, Y and Z happens in my class, and hearing personal stories about how those sorts of things have been addressed and strategies for addressing them, I think eases some worry for folks, so it could fill a gap.

Todd: Yeah, I appreciate you saying that, and I think one of the big ones is that it’s been about 40 years of teaching, and the 40 years of teaching, I think the framework I’m gonna go with, I’ve made almost every mistake you can make, and I still am seen as kind of a person who’s written these books. I do know a lot about teaching, and I’ve learned a lot about it, but you don’t become a better teacher without making mistakes along the way. I don’t think anybody can. And so I’ve done that. And I probably should point out, too, as Lew said, I run around some campuses. I love meeting people and going to campuses. I’ll still keep doing that.

Lew: So Todd, three years ago, I did a column for the MAA, it’s the Mathematical Association America. It’s called Still Learning From My Misstakes. And mistakes was spelled with two S’s, anyway, but yeah, exactly that, that idea of, I think, showing your vulnerability, especially at the part in the career that he’s at, like, oh, Todd Zakrajsek does everything perfect. I think peeling back the curtain and showing the mistakes that we made for the folks who are coming up, that is so refreshing to see that and what we did well and what we didn’t do well, and then kind of examples of moving that forward. So yeah, maybe three books, you might sell three. [LAUGHTER]

Rebecca: Definitely worth the investment now. [LAUGHTER]

Todd: Lew, that will cover my next tea. [LAUGHTER] But thank you, appreciate that.

Rebecca: Well, thank you both for joining us and for sharing your work.

Lew: Thank you so much. Appreciate it. It was fun.

Todd: Appreciate the opportunity.

John: Well, thank you.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

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425. Authentic Voice in the Age of AI

Student use of AI tools presents challenges for faculty teaching writing. In this episode, Anna Mills joins us to discuss when and how AI tools can be used to help students develop their writing skills.

Anna has been a leader in exploring effective strategies for integrating AI into higher education in a manner that fosters the development of student critical literacy. Anna serves on the MLA Task Force on Writing and AI and as a lead advisor on the instructional design for MyEssayFeedback.ai. She also has served as the only educational specialist recruited by Open AI to test GPT-4 pre-release. Anna is also an OER advocate who has released numerous OER resources including two OER textbooks, one on How Arguments Work: A Guide to Writing and Analyzing Texts in College, and the other on AI in College Writing: An Orientation. She is also one of the developers of the PAIRR process in which students develop writing skills through feedback from peers, AI, and individual reflection.

Show Notes

Transcript

John: Student use of AI tools presents challenges for faculty teaching writing. In this episode, we examine when and how AI tools can be used to help students develop their writing skills.

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John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

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John: Our guest today is Anna Mills. Anna has been teaching college writing courses for 18 years and currently teaches writing at the College of Marin. She has been a leader in exploring effective strategies for integrating AI into higher education in a manner that fosters the development of student critical literacy. Anna serves on the MLA Task Force on Writing and AI and as a lead advisor on the instructional design for MyEssayFeedback.ai. She also has served as the only educational specialist recruited by Open AI to test GPT-4 pre-release. Anna is also an OER advocate who has released numerous OER resources including two OER textbooks, one on How Arguments Work: A Guide to Writing and Analyzing Texts in College, and the other on AI in College Writing: An Orientation. She is also one of the developers of the PAIRR process in which students develop writing skills through feedback from peers, AI, and individual reflection. Welcome Anna.

Anna: Thank you. I’m excited to be here.

John: We’re very happy to talk to you.

Rebecca: Today’s teas are:… Anna, are you drinking any tea today?

Anna: Oh, yes, I am drinking Yorkshire Gold. It’s my standby.

Rebecca: A classic.

John: That’s long been a favorite of some of our British colleagues, and I am drinking ginger peach green tea.

Rebecca: Nice. I have switched on to Jasmine green tea.

John: Very good. So we’ve invited you to here today to discuss issues that you’ve been writing about and talking about quite a bit related to fostering academic integrity while helping students develop AI literacy skills. First, though, can you tell us a bit about how you became interested in the possibilities for using AI in education?

Anna: Sure, so I actually grew up in Silicon Valley, and my dad was a software engineer working with the dinosaur age AI of the time, and so when I kind of stumbled onto today’s large language models, before ChatGPT, I was playing around with how well it would do generating essays that fit my assignment criteria, and I realized that it had made a point that I hadn’t thought of in critiquing a sample essay that I had written. And I just thought, this can complement my thinking. This can stimulate my own thinking, this can stimulate and complement what I already do. And if it can do that for me, then it might also stimulate my students, so it might support their learning, depending on how it’s used. But I think I’ve always been interested, growing up in Silicon Valley, in the divergences and convergences between human experience, human thinking, and mechanical processes’ logic and early computing. So I’m always interested in those kinds of tensions and questions, and I just was electrified when I realized that systems that can simulate thought are here. The Turing test has been passed, and that’s the strange age we’re in. [LAUGHTER] So, I think it’s an exciting moment.

Rebecca: Not all faculty lean in [LAUGHTER] necessarily in the same ways that you do, given your past history and your curiosities. One of the major concerns that faculty have about student use of generative AI platforms is that students might use it to substitute learning, rather than complement it, like you had just kind of noted. Many faculty have reacted to the availability of generative AI by simply banning its use. Is that a productive strategy?

Anna: I actually think it’s a pretty reasonable strategy. I would just say it can’t be the end, right? It’s a beginning, it’s not the full answer. And certainly, if we’ve designed a learning activity for the student to do, then it makes sense to ask them to do it, right? and not to have a machine auto-complete it for them. So as a first step, I think banning it for a learning activity that’s not designed for it makes some sense. I just think it’s sort of similar to the dawn of the internet, in that we might ban it for some things, but we also will start to build AI literacy curriculum to guide students as to how to understand this phenomenon, how to think about it, how to consider whether to engage with it or not, and we start to develop some assessments that might involve it as we figure out how it actually could support learning rather than getting in the way. So not everything has to involve it, and certainly we need students to do something, right? So we have to ban it in some contexts and in some ways, even if we want to work with it. So I don’t think instructors who ban it should be demonized at all. I think they just need a little more support to think about those next steps, as far as, how can I continue to guide students in this evolving landscape? How should I start to consider applications, limited applications and pedagogy. I don’t want to demonize anybody for banning it. I think it’s a very reasonable first step.

John: Would limiting or prohibiting its use on lower-level tasks be appropriate so that students could develop some basic skills in the discipline that they could then use to evaluate AI output? Because AI can be really productive, as you’ve noted, but it does also hallucinate, and it does produce output sometimes, which is not very reliable, but in order for students to be able to evaluate that, they need to develop some skill in order to be able to distinguish between what is quality output and what is not. So might bans be more appropriate as students are beginning to develop the skills and then perhaps progressively allowing more use of AI?

Anna: Absolutely, yeah, I’m completely with you on that, and I think there are parallels to calculators and computer use. And I’m very concerned about setting some guardrails, some guidelines, so that students still have these formative experiences, so they can develop the kinds of skills that they need in order to do their own thinking, make their own decisions, to interact with AI productively, to be skeptical of what it gives them, to improve on what it gives them. So absolutely, I want to do that, and that’s kind of why I’ve gravitated toward not inviting my students to use it in hybrid ways in their writing, I want them to have those experiences in college of developing an essay from start to finish, with their own words, their own ideas, their own organization. They’re making the decisions and AI is used in my pedagogy mainly for feedback, that they then reflect on critically. So, absolutely and I think, unlike the calculator, I think you have to wait longer before you’re really ready to be learning with AI in these hybrid ways. I think we have to protect space for students to develop their own skills, making their own choices.

Rebecca: We talked a little bit about disciplinary skills, but we’ve also mentioned AI literacy. Can you define what that is? I think we like vaguely have an idea of what that is, and we’ve been using that term over a couple of episodes, but haven’t really defined it.

Anna: Yeah, I think it’s hotly contested. Sometimes people use it to mean sort of technical knowledge about AI systems, and that’s not usually what I mean. But I think that you have to know enough to be skeptical, as John said, to know that there are errors and hallucinations, that these are fundamentally sort of pattern-matching systems. And beyond that, I think a lot of the things that help you interact well with AI are not specific to AI. So there can be general information literacy skills, reading, writing, and critical thinking skills, expertise in a subject matter, but I think they also involve knowing what current systems are capable of and some of the ways that people use them strategically, so some familiarity with this landscape of what people are doing with these systems and how well they perform. And that’s, of course, a moving target, but I think it is important for educators and students to be keeping up, to some extent, with what’s possible, because that will be part of workplaces, certainly. And at the same time, it’s not about the technical details. It’s not about learning Python and being able to build these systems yourself. It’s not even about prompt, quote, unquote, engineering. What we’re hearing is that, with the current systems, as they improve, you really don’t need these specific technical approaches. You need expertise and thinking and reading and writing skills. So I think any look at AI literacy should really emphasize those. But I think that there is also a certain familiarity that we need to support teachers and students to develop with what can you do now with these systems? What are people doing with them, and what are the pitfalls? What are the dangers as you use them?

John: One of the things that’s happened over quite a period of time is that more and more assessments in classes have been shifted outside of the classroom. That is especially true with online work, and if you are trying to restrict student use of AI tools while they’re developing those basic skills, one of the things that many faculty have tried to do is rely on AI detectors. How reliable are these? When is it appropriate to use them? And in what ways might it be appropriate to use them?

Anna: So they’re definitely not reliable, even the companies will acknowledge that those detection systems sometimes label AI text as human written and sometimes they label human written text as likely AI. So any use of them has to take that into account and not rely on them. That said, I do think they have a limited place in my practice, I have a lot of colleagues who strongly disagree with me, and I recognize some of the concerns that we don’t have rigorous testing of these systems for things like bias against English language learners, and I would like to see more testing and regulation around them, but that said, we do need ways to deter misuse, to make it less tempting to students, and I think AI detection does quite a lot in that respect when you use it to complement other strategies that reduce misuse, and when you reassure students that you know it makes mistakes and that you will take that into account, that you will meet with them, you will use other approaches to determine whether they’ve used AI. In my case, I’m not using it punitively. I am offering students another chance to rewrite. I’m giving them the benefit of the doubt where that’s necessary. If I meet with them and they can explain the essay pretty well, I’m not going to penalize them, even if the writing was flagged as AI. So I think there is a judicious way to use them, but I would also like to see a lot of guardrails around their use.

John: And we have seen a lot of cases where faculty just universally will accuse students of AI use when the detectors flag a high probability. But as you noted, that probably is not appropriate without some additional evidence, is that the approach you’d take?

Anna: Definitely, so I’m using detection, but I let students know that if their work is flagged, that means that we’re going to meet and talk about the essay. And when I meet with them, I let them know that I’m not trusting it, but that I am concerned. I want them to be learning, to be developing their confidence, their voice, and we can work together on that. If there is something that was more AI than they intended or they should have done, then that means they haven’t done the learning yet, and there’s still space for that in the class. And then I’m using it in association with a lot of other things. First and foremost, I’m emphasizing what can I do so that they feel engaged with the class and intrinsically motivated. I’m giving them a lot of choices about essay assignments. We’re talking about how writing helps us develop our thinking skills and how that’s applicable to many other things in life. And I’m checking in with them a lot about their engagement with the assignment. I’m offering a lot of support for their process, a lot of formative assignments along the way that are credit/no credit, a lot of tutoring support, scaffolding for the skills to help them build that confidence in their own voice. And I’m also offering ways to use AI that support their learning, that are not cheating and so kind of trying to guide them into that other path, where it stimulates their thinking rather than replacing it. So I think it all goes together. There’s not one right approach and one wrong approach. There’s a lot of things that can help. And you can get around detection, also. There are tools that will, quote, unquote humanize AI text to get around detectors. You can get around process tracking. You can simulate typing into a Google Doc and make it look like you’ve spent an hour on that Google Doc. So these approaches, they’re not adequate gotchas, and they shouldn’t be used in that spirit. But that said, we need accountability in most areas of human life, even if we mean well, sometimes we need help sticking to that, and we need deterrence for those actions that deviate from the path. And I think AI detection can be that, it can help with that. And as you said, we’ve got quite a situation with online learning, and we may need some proctored assessment to complement it as well going forward. But I think this is a little bit like cybersecurity, so it’s never going to be perfect, and yet you can’t stop working on it. And I think some technical mechanisms to discourage misuse can support educators, can be an ethical tool. So I think we should keep asking for ethical tools that support academic integrity.

Rebecca: Both you and John have talked about online learning and the challenge of assessments [LAUGHTER] in the age of AI, so other than oral exams or in class blue book exams, proctored exams, what else can we do in the age of AI to make sure that we have good assessments for students when these other ones might be a bit vulnerable?

Anna: Well, I think that there’s a great literature on humanizing online learning. People like Michelle pacansky Brock have really shared a whole bunch of wonderful practices that are good pedagogy anyway, so things like collaborative annotation, where students are commenting on the readings for class in a social way and responding to each other. Video and audio assignments, using platforms like VoiceThread or Canvas Studio, so building courses so that they are social in a variety of ways, including discussions, but beyond discussions, I think those things can help. Online learning doesn’t have to be sort of one direction, content delivery and then performance. There’s a lot more possibilities than that. I’m interested in things like lockdown browsers for a little bit more of a guardrail around some online assessment. But I also recognize that’s not secure either. Even with a lockdown browser, you can have another device and have aI going on that device. So I do think we might need some complimentary sort of proctored assessments. There’s this two-lane approach coming out of Australia that says we can have formative assessments where students may or may not use AI, but then we need to validate that there has been learning with some form of secured assessment. And I think we need to keep looking at what that looks like for online courses, balancing things like validity with student access. We got to keep going with online learning, because so many students would not be able to access their education otherwise, and we have to find ways to validate that, and that might mean various forms of observing student learning, close observation, whether that’s proctored assessment in person or some form of online process observation.

John: One of the things that I found somewhat troubling recently is the number of browser extensions that have been developed to automatically detect questions on the screen and to offer to answer those questions. In fact, Chrome provided one for a short period before taking it down. Would a lockdown browser be a strategy to prevent that, or would they not recognize the fact that there is this AI agent hiding in there that is doing the data input, rather than the students themselves?

Anna: Yeah, I have been wanting to test that. I’m not sure if lockdown browsers can block agentic AI browsers like Perplexity Comet or ChatGPT Atlas that will just move around and click and fill out forms for you in the learning management system. I think they might help. But long term, I don’t think teachers should have the burden of trying to design around this. I really think we need the learning management companies and the AI companies to help, to say to these systems, don’t complete work for students. It’s a very simple step that the big AI companies could take to drastically reduce this kind of threat to learning. I think we’ve got to pressure them to do that. And I think there can be mounting public pressure from parents who want their kids to learn, and they want their kids learning to count for something and not be judged against somebody else’s cheating. So I think it’s sort of a common sense societal interest that we’ve really got to push. We’ve got to make space for learning online, and that means don’t let the AI systems jump in and take over. Make sure that it’s the students. And there’s not a whole lot that I think I can do as a teacher to block AI agents. I might be wrong. There are educators experimenting with this. Tim Mousel just released a video where he shows how he built this little system to try to detect if it’s an AI agent and not a student. And maybe that can inspire the learning management companies to do something similar. But I think for most educators, we’re not running the Canvas or the Blackboard, and there’s not too much we can do. Multimodal assignments might slow down an agentic browser, like it’s probably not going to be able to generate a video with my face too easily without a little help. So yes, those things might help a bit, but I don’t think it makes sense to ask teachers to redesign our entire pedagogy when we really don’t have a lot we can even do to block this, and the big companies do have things they can do to block it.

John: We’ll include a link to that video in the show notes. I saw it this morning on LinkedIn myself and I. He was able to block two of the three things he tested with his work. But it’s an escalating battle. We have better things to do with our time as educators than to fight that.

Anna: And why should we throw out pedagogy that works and that is research based? Why should we deprive students of those kinds of learning experiences that we know how to provide? So I really want to protect things that we know are valuable. I want my 14-year old son to have those learning experiences, and teachers shouldn’t have to throw them away. That doesn’t make any sense.

Rebecca: You talked a little bit earlier about ways to preserve academic integrity, but also to motivate students. And you hinted at a few things like process, scaffolding, and things like that. Can you dive a little bit more into what that looks like, maybe in one of your classes?

Anna: Sure. So in writing pedagogy, we’ve been teaching writing processes and strategies for years. So in my class, that starts with collaborative annotation of readings for the class, so students are speaking back to the text in the margins and talking to each other about it, and that starts to lay the foundation for what they want to say in their essays and for making space for their voices. Then there are a lot of low-stakes assignments where they’re brainstorming or they’re writing drafts where they have permission for them to be terrible. We’re reading about writing strategies that help us get past those difficult moments. We’re acknowledging that thinking is hard and sometimes painful or frustrating, and that’s okay, and there are lots of things we can do to seek support. I’m assigning tutor visits as homework. I’m giving lots of chances to review each other’s drafts, to revise, to reflect on AI feedback before there’s a final draft due, and even then, I’m saying, “You can rewrite to raise the grade.” So this is an ongoing process where there’s support all along the way. We’re going to get stuck, but there are a million moves we can make to feel better and to ultimately push our own thinking further in the writing process. And you get credit for all of those things that you do. So I’m hoping that that’s really an affirming and a hopeful orientation to writing that carries a lot of empathy for the moments [LAUGHTER] when it’s hard and onerous, but that ultimately offers a lot of options for every step.

John: Many of the guests we’ve had on the podcast in the past have advocated alternative grading systems as a way of shifting the focus from grades to learning. Have you experimented with that? Or have you talked to many people who have tried this, and has it had an impact on improving student motivation in shifting the focus from just trying to maximize grades to maximize learning?

Anna: Yeah, I think it definitely does help. A proportion of my class is sort of ungraded credit/no credit for process assignments, and then I do have a portion that’s graded. I think it can help. I think you still have to have a certain minimum standard for passing the class, but I think it’s something I’d like to explore further, because I think it does remove a lot of that anxiety for students. The sense of inadequacy is huge in first-year composition classes and the sense of imposter syndrome, and “maybe I don’t belong here, and I should be using big, fancy words.” So, there’s so much sense of inadequacy and fear and imposter syndrome in first-year composition in community colleges where I teach. So you can say to students, “I want to hear your voice and you don’t have to use fancy vocabulary and all of that,” but it’s hard for that to really sink in. I think that ungrading can be part of just affirming whatever it is that students bring to class. But I think also sharing examples with students of student voice, and celebrating that, can be powerful as well, so that it really becomes the students’ space and more on their own terms, and I think ungrading can be part of that.

Rebecca: One of the ways that you’ve talked a little bit about doing this is through the PAIRR process. Can you talk a little bit about this process for our listeners?

Anna: Sure. So PAIRR stands for peer and AI review plus reflection. This was actually developed first at University of California – Davis by Carl Whithaus, Lisa Sperber, and Marit MacArthur, principally, but I was doing something very similar on a parallel track as a volunteer advisor for MyEssayFeedback, which is this not-for-profit app, and then I joined forces with the UC-Davis PAIRR researchers, and we’re now working together because it’s an approach essentially to bringing AI into what we already know about good writing pedagogy and building AI literacy in the process. So basically, you keep what’s good, including peer review and instructor feedback and students drafting, putting together their own draft, and then you bring AI in for feedback that stimulates further revision, and you encourage students to question that feedback and compare it to what they got from their peers, and think about how they feel about that, and what was their actual purpose with the writing. Did the AI feedback get it right, or did it misinterpret what they want to do? So you’re building this habit of skepticism of AI and also building some prompting skills, helping students get the most out of that feedback by pushing back in various ways and asking for more from the system. So that’s the core of the approach. But we also start out with some AI literacy readings, so that students have some basic concepts about what these systems are and AI literacy, including questions around the ethics of AI and how those ethics should influence our decisions about AI use. That’s a big piece of it as well, but really the goal is to support students’ sense of agency around their own words, their own thinking, and their choices around using AI. So it’s a really simple method, and I think, very complementary to existing pedagogical practices. So it’s kind of something that people who are against or afraid of AI can use, and that people who are excited about AI can use. I think of it as kind of a meeting place, this approach.

John: How have students responded to this approach, or the approach that you’ve talked about with MyEssayFeedback?

Anna: So the UC Davis researchers, they have a piece out in Computers and Composition. They found that students overwhelmingly preferred having both peer and AI feedback. They found them the most useful in combination. They didn’t want one or the other. Often the AI feedback reinforced the peer feedback. Sometimes it provided a complementary perspective. They also noted flaws in it, that it wasn’t always right, and so that’s overwhelmingly what I’ve seen as well in student response to MyEssayFeedback. I’ve used it for I think this is the fifth semester, and that’s what we’ve seen in other teachers’ observations and student testimonials on MyEssayFeedback, that it’s sort of like, “Oh, this is a way that AI can support my writing and not interfere and not get in the way. I’m still in a driver’s seat, but I’m getting help.” So I think it’s a really powerful paradigm for students that they appreciate. There’s the concrete help, but there’s also that paradigm shift around what AI can be for them. And we hear that again and again in student comments, and we’re still doing further research, so we’re still looking to improve the process, to improve the prompts that we use, the ways we request AI feedback. We’ve done a lot of testing on those prompts, and we shared them publicly as well. So I think we can keep improving that model and sharing it and inviting others to improve on it as well.

Rebecca: One of the things that I can imagine as a student that would be interesting is when you have peer feedback and you also have AI feedback, there’s sometimes conflicts in that feedback, but there’s also places where that feedback converges, and that happens when you have feedback from multiple people anyways, it’s not always the same, so sometimes that helps, maybe, prioritize what to work on or to focus on.

Anna: Yeah, I think sometimes students don’t know whether to trust the peer feedback or the AI feedback, so it is helpful to see where they overlap with each other. We also designed one of our prompts so that it would try to nudge students to speculate on what the human readers will feel or think or want, to kind of remind them that the point is the human readers’ response, ultimately, not so much does it meet the criteria or the grade, although that’s also part of the feedback, but what are your human readers going to want out of this? And that the AI can help them think about that and remind them of that framework, so it really can complement this human-oriented writing process.

John: We have a lot of faculty, though, who are still a bit reluctant to try anything related to AI, and they just like to bury their heads in the sand, or who are just giving up on a role for faculty in affecting the evolution of how we do this in education, because they feel that it’s being dictated to us from the big tech companies out there, and we don’t have any sort of say. How would you respond to those faculty who are either just ignoring AI or who are perhaps giving up any hope of being involved in how we use these tools?

Anna: Yeah, I would say “I hear you.” That makes sense. It’s a lot of cognitive load and labor to engage with it, to learn about it, to try out new strategies, to understand how people are using it. Those of us who are on social media a lot learning about AI and education often liken it to a fire hose coming at us. There’s constant change, and this is labor. I think it is possible, though, to support faculty, to compensate the labor to develop programs that make space for faculty to learn and to shape and reshape our pedagogy, our AI policies, our own practice in an emergent way, in a collaborative way, that is not dictated by the AI companies or by for profit ed tech. And there have been some great examples of this that I’ve been able to be part of. One program at College of the Siskiyous that has participants get a stipend. They attend meetings, they learn certain things, but they’re developing their own project that they’re later going to share and showcase that builds on what they’ve learned, but that is very much their own. And that’s the kind of community of practice model that has some learning, some outside instruction, but also a lot of faculty making decisions based on their own experience and practice. And I think that’s the kind of model that shows respect for faculty, time, energy, and labor that I feel good about saying, if we do this, then we really can ask faculty to engage, and that will feel very different from the AI company saying, “Look at this cool thing we built. You should be using it in X, Y and Z ways. So do a whole bunch more work.” It’s understandable that people would want to say no to that kind of ask, but I think it is possible to support faculty to engage on our own terms, and that’s what we need to do. I just think faculty empowerment around AI is really something I want to see more of. Those of us who are afraid of or hate AI, those of us who are excited about it, all of the faculty voices are needed to shape what is our society going to look like? What is learning going to look like? We should be the ones shaping that. So I want to see more involvement, more people listening to us, in terms of how these systems are shaped, how the learning management systems are shaped, where the money goes. And I think we don’t need to be intimidated. These systems are very straightforward to interact with. We have the expertise to work well with AI. We have the expertise to give guidance on how it should and shouldn’t be used, and what the systems should do and not do. So really, they need us. Society needs us. Let’s step up to that. Let’s claim that, a more powerful place in shaping our future with AI.

Rebecca: So we always wrap up by asking, what’s next?

Anna: Well, I want to improve on this AI orientation textbook that I’ve done and really share it, and I want to start making more student facing videos. I think one space where a lot of learning happens that both faculty and students haven’t yet tapped into as much as we could is social media, and so I do a lot of engagement with other faculty learning on social media, and I want to reach students on there as well, and be in dialog with students on platforms like YouTube and TikTok, because I think that this is a moment of great vulnerability, risk, possibility, overwhelm, change, and the only way to feel good [LAUGHTER] in those kinds of moments is to sort of turn toward each other, to support each other, and to keep talking through it, and to sort of make space for new practices to emerge. So that’s really where I get my energy and hope. And I think that my wish would be for more faculty and students to get to experience that with each other and find more spaces for that with each other.

John: Excellent. I’m not sure I’m ready to do any of the TikTok dances, but that would be a place to go to reach students. [LAUGHTER]

Anna: Right. And I’m all about like that vulnerable message to students of “I don’t have all the answers, and even I question sometimes whether I should use AI at all, given the ethical concerns, I don’t have the perfect prescription for ethical AI use, but, here’s what I’m learning, here’s what I’m doing now, and why and how you might critique that, but why I’m still doing it.” I think we can be honest and vulnerable with students, and it feels better to me when I do that. I think ultimately, as an educator, it is exciting that this is all changing so fast, and students want to talk about it, they want to learn about it, they want to develop their own opinions about it and responses to it. And that’s where we are, too, as teachers. So that’s the moment where we can really meet students in a really genuine way and be energized together. To be honest about this, I think that’s a good aspect about the moment. There are a lot of challenges, but that is a good thing about where we are today as teachers.

Rebecca: I really appreciate that idea of bringing people together to talk about the issues that are really bothering them or exciting them, or whatever emotion they’re [LAUGHTER] having about it, and that experts and the novices can be together in the same space and learn from each other.

Anna: Yes, AI keeps hijacking our department meetings in the English department. [LAUGHTER] Everybody wants to talk about it at every meeting, whether it’s on the agenda or not. So we need each other. We need those conversations. And there’s so much energy around it. We really can support each other and grow together given that great need.

John: And it’s hijacking pretty much every educational developer conference that I’ve been to in the last three years. [LAUGHTER]

Rebecca: Or podcasts for that matter, John.

Anna: Yeah. [LAUGHTER]

John: Well, thank you. It was great talking to you, and we’re looking forward to more conversations in the future.

Anna: Thank you for your wonderful questions and a wonderful conversation. I appreciate it.

Rebecca: Thank you.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing Assistance provided by Madison Lee.

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424. Teaching with AI

The rapid evolution of AI tools provides a challenge for educators exploring educational applications. In this episode, José Antonio Bowen joins us to discuss ways in which faculty and institutions can better prepare students for their future lives and careers in a world in which AI tools are ubiquitous.

José has a joint PhD in musicology and humanities, served for several years as President of Goucher College, and is the author and co-author of several books, including: Teaching Naked: How Moving Technology Out of Your College Classroom Will Improve Student Learning; Teaching Naked Techniques: A Practical Guide to Designing Better Classes; Teaching Change: How to Develop Independent Thinkers Using Relationships, Resilience, and Reflection, and a co-author, with C. Edward Watson, of the first and second editions of Teaching with AI: A Practical Guide to a New Era of Human Learning.

Show Notes

  • Bowen, J. A. (2012). Teaching naked: How moving technology out of your college classroom will improve student learning. John Wiley & Sons.
  • Bowen, J. A., & Watson, C. E. (2017). Teaching naked techniques: A practical guide to designing better classes. John Wiley & Sons.
  • Bowen, J. A. (2021). Teaching change: How to develop independent thinkers using relationships, resilience, and reflection. JHU Press.
  • Bowen, J. A., & Watson, C. E. (2024). Teaching with AI: A practical guide to a new era of human learning. JHU Press.
  • Bowen, J. A., & Watson, C. E. (2026). Teaching with AI: A practical guide to a new era of human learning. 2nd ed. JHU Press.
  • ChatGPT
  • Gemini
  • CoPilot
  • Claude
  • Hume AI
  • Sesame AI
  • Latimer
  • Mike Caufield (2025). “Deep Background GPT Released.” June 16 substack post.
  • Apertus – new Swiss AI platform
  • Storm (from Stanford)
  • weteachwithai.com (website accompanying Teaching with AI.)
  • Undermind
  • FutureHouse
  • AAC&U Institutes
  • Nano Banana AI
  • BoodleBox
  • Poe AI
  • Comet AI browser

Transcript

John: The rapid evolution of AI tools provides a challenge for educators exploring educational applications. In this episode, we discuss a resource that can support faculty in this process.

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John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

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Rebecca: Our guest today is José Antonio Bowen. He has a joint PhD in musicology and humanities, served for several years as President of Goucher College, and is the author and co-author of several books, including: Teaching Naked: How Moving Technology Out of Your College Classroom Will Improve Student Learning; Teaching Naked Techniques: A Practical Guide to Designing Better Classes; Teaching Change: How to Develop Independent Thinkers Using Relationships, Resilience, and Reflection, and a co-author, with C. Edward Watson, of the first and second editions of Teaching with AI: A Practical Guide to a New Era of Human Learning. Welcome Jose.

José: Thanks. Good to be here.

John: We’re looking forward to this conversation. Today’s teas are:… Are you drinking tea by any chance?

José: Coffee. Sorry.

John: That’s not uncommon, that and water are probably the most common teas.

José: I had tea this morning already, though, so…

John: Okay.

Rebecca: Alright, so you’re not anti tea. Alright. [LAUGHTER]

José: I lived in England for five years. How could I be anti tea? [LAUGHTER]

Rebecca: Right? [LAUGHTER] I have a Constant Comment today.

John: And I have a ginger peach green tea today.

Rebecca: So we’ve invited you here today to discuss the second edition of Teaching with AI. We ran a very successful faculty reading group on the first edition of this book in fall of 2024,
and since the first release of Teaching with AI in late April 2024 we’ve experienced extremely rapid change in the development of generative AI platforms and tools. What were some of the changes that prompted this relatively rapid release of a second edition of the book?

José: Wow. Yes. I mean, first of all, the technology has continued its march, and so we are seeing pretty steady improvement and diversification of kinds of tools and platforms. So we initially started a website with a list of tools. There was the big three and a few others, and now they’re probably 18 to 20 foundation models that are all over the world. When we wrote the first edition, there were not all of these cultural models. There were not Arabic and Chinese models and Spanish models, and there’s a new Swiss model this week, which speaks 1000 languages, we didn’t have Latimer, which is trained on black and brown sources. So there are more models, there’s a lot more tools, and every week seems like we get more of them. But it’s also the case that people’s attitudes have changed, student use has changed, faculty use and resistance have changed. But a lot of it was also that we just spent a lot of time visiting campuses. So I think each of us visited 100 campuses this year alone. So that’s a lot of ideas for assignments, things we hadn’t thought of, that “Oh, I’m using it to do this. I’m trying it to do that.” So we always intended this as a practical guide. There are lots of assignments, lots of assessments, and there’s a lot more of that now. There’s a whole new chapter on chatbots, because that wasn’t really an easy thing to do when the first edition came out, and now there’s hundreds of platforms that do that. And there’s also an extra chapter on literacy and ethics. We were initially a little scared to write a chapter on literacy. I mean, partly, we didn’t have time. I mean, it was just we were like, “We got to get this out.” And also, I think we didn’t know what it meant at the time. It was brand new. And now we have literacy, we have fluency. So this time, there’s enough, I think, research and evidence out there for us to say, “Okay, here’s how people are thinking about this.” And so there’s a whole new chapter on literacy, and there’s an extended chapter on sort of role play and feedback. That’s less about tools, although the voice tools have really… we didn’t have that in the first edition, the ability for voice and also emotional voice tools like Hume and Sesame that really do understand the tension in your voice and respond in an appropriate way that feels and sounds human. So role playing is a bigger thing. I could go on and on and on, but so much has changed. I thought we should do it as an annual, [LAUGHTER] but I don’t think I can do that. I think that’s too much work.

John: And it will probably stabilize to some extent, as most other tools have, at some point in their evolution.

José: We thought so, but the first three years, it certainly hasn’t yet. I’m with you. I sure hope so, because it’s impossible to keep up.

John: In your earlier Teaching Naked books, you focus on the importance of maximizing human connections in the classroom and using appropriate technology for student work mostly outside of the classroom. Do you still advocate a teaching naked approach in the current environment, with the ubiquitous availability of AI tools?

José: Yes, mostly, I think there are going to be places in the classroom where you have to bring AI in, the same way that you can’t teach digital literacy if you don’t have the tools in the classroom. I do think there are a whole class of assignments where I want students to do things in class and talk to each other. An example that I’ve borrowed from Mike Caulfield’s great work, or sort of altered, but used his tool, is that I have students come up with a list of controversial statements. What are some things that might be true, or does the Earth have two moons? And then the students now write a prompt, an 800-word prompt, to test this group of ideas. If I said, “Write an essay. write a prompt,” sounds a little bit more meaningful. I’d let them go do that at home. Go write your prompt, and then they come back. And so now everybody has a prompt, and now I say “So now get up with a partner and pick a controversy and test it with your two prompts. See who wrote the better prompt that actually gets underneath and does this thing well.” Then I show them Mike Caulfield’s Deep Background, and I said, “so his is better.” So I don’t show them the prompt. I just say, “Can you alter your prompt to get it to do this, to make a table, to divide the question into sub questions, to trace back the origins. What are the other things that you didn’t think of? Do you need a chart that with a scale of “Well, some statements might be true on a scale of one to five? This is definitely true, definitely not true. But what about the maybes?” So there’s a lot of active learning in class, but the tool is still with us in class, in the same way that I don’t think we should ban PowerPoint from the classroom, but I do think we should ban PowerPoint slides that have 400 words on them. Put a picture, that’s good, and six words that’s, I think six words still works as a maximum, and then write that. You use that as the way to kind of focus the conversation. “Oh, that’s what we’re talking about. We’ve kind of wandered off, but there’s the picture and the six words.” So I think there’s a place for some use of technology in the classroom, like I always did. But I still think that if you’re going to have students drive to campus, get parking and take the extra investment to be face to face, rather than be online, you have to give them something different, something they cannot get online, and that’s the smell and the feel and the touch and just the presence of other people and the ability to have those kinds of conversations and look how people are responding. I still think we should be using class for that. I do think that, as with the internet and now with AI, we might not need to meet three times a week for 50 minutes, especially if you’ve got over 50 or 60 students. If you’ve got hundreds of students, you do not need to meet together with 600 people. My suggestion is, let those 600 sessions… let that happen online, tape your videos, your lectures, whatever, but then meet with students in small groups of 15 or 20. So maybe that’s only once a week. Maybe I have my 600 students and I have my four TAs. So maybe what we do is we only meet in groups of 12 face to face once a week. We could radically rethink what we do with that face time, and that’s kind of what I proposed in Teaching Naked and so I still think we could do that. And now, instead of those lectures, maybe it’s an AI tutor that does the out-of-class work, but I certainly don’t need the AI tutor in class.

Rebecca: One of the main concerns that faculty raise about generative AI is the concern over academic integrity issues, so faculty sometimes opt or wish to ban the use of generative AI or focus their efforts on trying to detect its use. Why is this not a very useful strategy, and what should faculty maybe be thinking about instead?

José: There are two things. So the first is faculty should be concerned firstly, with learning, not with academic integrity, right? Learning. So if students are not learning, the problem is that they’re not learning, and I need to rethink how they should be learning. The second problem, though, is that the phrase “academic integrity” implies that we have a special integrity. It’s not like your integrity, our integrity, right? And the minute you say that, it’s like, “Well, then in four years, I can forget about it, right?” It’s only academic. So why not just talk about integrity? Why not talk about authorship and the importance of human contributions? And by the way, what we call cheating, business calls progress. So the first thing to realize is that if the product your human students produce is no better than AI. You’re not doing them any favors because they won’t get a job. Yes, there are things… we have to learn how to add, even though the calculator will do that, but there are other reasons for that, right? We’re really learning number sense. But in fact, most accountants are going to use a calculator. They’re not going to add up long columns of numbers. The learning to add is part of the process to get us to a kind of thinking that moves us along. So if we’re clear about, oh, this is the reason you’re learning to add when the student says, “ Well, no accountant is not going to use Excel spreadsheets or computer or calculator, why do I have to learn how to add?” Well, the answer when I was a kid was, so you get the right change when you buy a candy bar at the drugstore, [LAUGHTER] and now they don’t even make pennies. Or they used to say, “Well, the patient will die. If you can’t do long division, you won’t be able to give the right kind of meds.” No doctor anywhere does long division in the hallway on a piece of paper. They all use an app. Now it’s so you don’t put as many zeros on your taxes, right? You want to be able to estimate so that, oh, I put the numbers in wrong. This doesn’t look right. This is too many milligrams. So the ability to have number sense still matters, maybe matters more. The process of learning to do those things matters. Is that really an integrity issue, or is that a learning issue? I’d much rather frame this as a learning issue and demonstrate to students that this learning will be useful. At the same time, I want to raise standards in the same way that a spell checker raised standards. It did not make me a better speller, but it did make my spelling better, and that was sufficient. So now one in three fortune 500 companies require Grammarly for every communication in and outside of the company. So the requirement that you don’t have spelling mistakes is now pretty common, but I don’t grade spelling like I used to. So those 10 points that I used to give for spelling, I now put on the thesis and the thinking, I put it someplace else. So AI is going to make us do the same thing. We have to rethink what is essential for students to do, and how will I encourage them to do it and motivate students to do it? And how will I raise standards so in fact the end result is something that neither AI nor student alone could do, in the same way that my accountant could not do my 700 pages of taxes without a computer program. She simply couldn’t do it, but she does know how to add, I think. [LAUGHTER]

John: But she doesn’t need to.

José: We do talk numbers all the time, and she’s estimating things. That kind of thing happens all the time. I’m glad.

John: A related concern, though, is that perhaps students are using AI too much at the early levels of skill development, so that they’re not developing critical thinking skills and disciplinary expertise to be able to evaluate the output of generative AI. What are some strategies that we can use to help students develop these skills while working on learning how to use AI tools effectively?

José: The first part, is yes, they absolutely are overusing it. But back to your cheating question, instead of calling that cheating, maybe we should call that overreliance or misuse. Is it really fraud? Is it really intentional deception? Cheating is a very loaded term, and so a student might not necessarily know when they’re overusing it or how they’re undermining their own learning by over reliance or misuse. On a practical level, there are two things that you need to use AI well. One is expertise does matter, that being able to say, “Huh? I don’t think that’s right.” I often say that AI literacy is fundamentally two things. One is asking better questions, and the other is evaluating answers. The two things that we all do, the core of a liberal education has been those two things for 1000s of years, so we all already teach AI literacy, but how do I get expertise if I have this tool that pretends to have expertise? It’s kind of the same question as the calculator problem. And I know the calculator is not a perfect example, but it’s a good example of this. I still need to know how to add to know when the calculator might have made a mistake, and so even though the calculator can do the work, I need to have that experience. So the calculator challenged us in two ways. One is, do I still need to teach long division and addition of long, long lists of numbers? The answer to both of those is not much. I should probably do a little bit, but I don’t have to spend six months like I did just adding up long series of columns. So I have to change what I teach, but I also have to think about the sequence. When do I introduce the calculator? So the question for us is not, do I introduce AI? The question is, when and how and what are the skills that I still need to preserve? Spelling is another one of those. I’m able to write books with a spell checker, and if I had to look up words in a dictionary still like it was 1970, I would be much slower… if I was still using index cards for my bibliography… So, I no longer have to teach the card catalog. Are there skills that I can not teach in order to add the other one? Academics, we love addition, we hate subtraction. [LAUGHTER] We don’t like taking stuff away, but we’re going to have to take something away. But that’s a big question. How do I do that? But I do think we’ve been teaching digital literacy for a while, and we should add AI to that. So the question is, is this true? Although, again, Mike Caulfield’s version of this is, “is this what you think it is?” is a great better question. Is this what you think it is, especially now that you can’t tell because it has six fingers. There’s no more “Oh, it has six fingers. I’m going to show you how to identify AI pictures.” Well, that doesn’t work anymore. So understanding what’s the source? What are the kinds of claims? Do other people say this too? So that’s probably a separate skill from expertise, just finding out what’s true. And again, all of us are doing it on a daily basis, because the news is full of stuff that it’s like, “Is that really true?” And “What’s the source for that?” and “is that picture really real?” So we’re all having to do that. So that should be easy, but the expertise is the harder part. How do I get you to be an expert if the junior level jobs and internships are going away. And so I think that that is a primary question that we should be thinking about. And my rudimentary answer is we need a lot more talk about motivation, a lot more talk about process that “yes, you will have a calculator, but the process of learning to add and to multiply is valuable, and here is why.” And so here’s what we’re going to do. And then I promise you, we will introduce the calculator. It is coming, but you will not be as good a user of that if you don’t first do this. And my analogy to that, so I teach music, and so people wanted to learn how to play jazz pian, and so you would give them these timed tests. I’d say, “okay, E flat minor, what key are we in?” And they’d have to write that. And so those were, like, those old multiplication, you know, 60 seconds, and you’ve got to, like, scribble as fast as you can. “I want to play the piano, why am I…” I said because, if you have to think about what key E flat minor is in, and how many flats that is, the band is already… that time has to be immediately in your fingers. And so in order to do that, I’m going to give you what feels like really rote memorization. Five flats, by the way, is the answer. And unless you see E flat minor and immediately go three, two, and you have that hand ready to go, you can’t converse with me in jazz. So I had to explain to students why I was making them do these, what appeared to be really dumb, like rote third grade math tests, because that will allow you to speak with me. When you see a piece of paper and we’re playing in real time and it’s going, click, click, click, click, E flat minor, F minor, you can change key. You know how to do that because it’s automatic, and so I have to make it automatic for you. So notice how much time I spent convincing you that you had to know how many flats. So that explanation is not something we’re always good at. So that’s not a great answer, but I think it’s the right question.

Rebecca: What you’re talking about is reminding me of a conversation I had with my graduate student a couple weeks ago about changing disciplines and fields and learning the language of a discipline, and how much that actually allowed him to communicate about what he wanted to communicate about this whole time. And so he’s worked on a really great presentation about this [LAUGHTER] exact topic that kind of becoming fluent in the language of the discipline allows you to communicate things that you couldn’t communicate before.

José: And that you can do with AI. There’s a great tool from Stanford called Storm that actually has a little button that allows you to change the disciplinary focus. And so one of my assignments is,”Okay, so pick a topic, pick a question, and go to Storm and then look at it through three disciplinary lenses, and then write an essay about what is the difference? What did you learn? What are the differences in how anthropologists and historians think about this question? So I do think that’s an important piece. And again, it’s often not explicit. We should make it more explicit. In the same way we should make the critical thinking discussion more explicit. For years, we’ve just said, “Oh, they’re learning that. They’re learning critical thinking.” It’s like, “ah?” We did a great study four years ago, and the students were like, “Critical thinking, what’s that?” You just spent four years doing it. It’s like, “but you never use those words.” And so adding the words, “This is critical thinking,” turned out to really help students. So we’ve got to be more explicit about that disciplinary knowledge and why it’s useful and how we’re learning it. And I remember the light bulb in graduate school. I remember sitting in that talk when it was a guest speaker, and all my professors were there trying to outdo each other, and it’s so “Civilization and culture.” And the other guy said “Apollonian and Dionysian.” And it’s like, “Oh, they just want to use their own terms.” It’s like, “Oh, my God, they’re not really arguing. They’re just like, trying to see who’s top dog. And so I remember [LAUGHTER] that light bulb going, “Ah, please.” [LAUGHTER]

Rebecca: Can students use of AI tools help them reduce the equity gaps that students have faced in their prior learning experiences. This kind of goes back to John’s earlier question a little bit.

José: Yeah, so I think the answer is yes, but we have to be careful. So the first is that AI has bias. We have to make sure that everybody knows this, but it’s also the case that humans have bias, and if we were ignoring human bias, we weren’t doing anybody a favor either. So it turns out that AI bias is a little easier to fix if you know what you’re looking for. If I know that AI tells me I should be earning less money because I’m a woman, I need to tell it to respond to my resume like a man. [LAUGHTER] How much should a man make in this job? And then that’s a useful tool. So understanding the bias is even more important than it used to be, because now I can say, “Oh, well, how could I correct this?” My example is, if I said to you, “so rank the candidates for the new position of history professor that you’re searching for,” can you ignore the prestige of where they got their PhD? Uhhh, that’d be “no,” because you’re a human. It’s like, you know, don’t think about the cookies. You can’t not think about something I just mentioned. But in AI, I could say, just rank the candidates based solely on the quality of their teaching, review the faculty based solely on the quality of student feedback. I can do things as a check on equity. I can also do things like so I can make you a bot. So you didn’t take calculus that’s required for this course, or most people have had it. So I’ve created this bot that will help you figure out what you’re missing, or create a bot that will help me, I didn’t take biology before chemistry, and I could do individualized tools. And AI is pretty good at learning, “Well, you tend to make this mistake a lot. This is a grammatical mistake you always make, and so you made this in the last paper,” and one that people don’t think of, which is that most of us, well, all of us, because I do it too, and everybody does it. All of us provide students with way too much feedback at once. [LAUGHTER] Here are 37 things to fix about your paper. And the truth is, you can only do one, one at a time. After you fix that. I’ll give you something else. This hit me when I was trying to relearn tennis a few summers ago, and it was like, move your feet, hold the hand, the grip. It’s like, “Shut up. Just give me some more balls. One thing at a time, let me just put my feet right, and then… So I use AI to take my feedback and what is the most important one thing the students should fix for next time, and then save the rest, because I overdo it. And so I can tell the AI not to give the student too much feedback at once, and it can do it. And I have a… all of us, I think… have a really hard time doing that. So the potential for AI to close equity gaps, to help us see bias, to make more ethical decisions, is there, but it was not going to happen automatically. We have to be very, very careful and very aware of the kinds of things AI gets wrong and the kinds of biases that are built into it, in order to make sure that doesn’t happen. But as a check on human bias, it’s really a pretty amazing new tool that we didn’t used to have.

John: In terms of that issue of feedback, when students are given 20 or 30 different things to correct or to improve in a paper and assignment, they will often choose whatever is the easiest, which is not always what is the most important. And that idea of focusing on the most critical or most important type of feedback makes a lot of sense.

José: Well, I can give an example. So this is a real example from a PhD student who’s like, comes back to me and says, “Oh my god, I handed to my dissertation to my three readers, and I got three different types of feedback. My God, they don’t agree. Is this normal for you people?” Uh, yes, [LAUGHTER] you got contradictory feedback. [LAUGHTER] Congratulations. Welcome to the club. And she’s like, “Well, what do I do? One of them wants me to cut chapter two. The other one wants more. What do I do?” I said, “So, you put the whole thing into AI with the three letters, and you say, ‘so, make a table list all of the things they want me to fix, from easiest to fix to hardest to fix, and then list: Does everybody agree that should be fixed?’ Check, and then another column, if they disagree. What’s the easiest way to make them both a little happy?” Graduation. So easiest to fix, the hardest to fix. So fix the easy stuff first. That’s fine, but an AI here could keep you on track to make sure you actually prioritize. So yeah, here are the three easy things you should fix, because that spelling mistake was terrible. But here is the real issue, the one real thing you should focus on. And I would say students leave a conference with me, and I didn’t know this until AI, but now they go to the AI bot and they say, “Okay, what’s the one thing I really need to fix?” Oh, my God, I didn’t make that clear. And I know that because I can see them talking to my bot about the conversation [LAUGHTER] they had with me.

John: With all the changes that are occurring in terms of the development of AI, we’re beginning to see some shifts in labor markets that are not that well defined yet, but it’s very likely that many of the jobs that students were preparing for in the past may not be there or may not be there in the same way going forward, and jobs are very likely to be changing in the future. I think you’ve already partly addressed this, but how can we prepare students to thrive in a world where the nature of the tasks that they’ll be doing might be changing fairly dramatically in response to more widespread use of AI in many occupations.

José: So, you know, I’ve been saying this for a while, but the future has been unknown for a long time. And so first you said, “Look, your major has never mattered less. I don’t know what the jobs are going to be in five years.” In fact, when I wrote Teaching Naked, my daughter was still in college, and then she became a director of social media, which was not a joke I put in the book because it hadn’t happened yet, but there’s like, oh, wait a second, you could be a director… this didn’t exist five years ago, and now you can direct it. So this isn’t new, that new jobs are changing and being created all the time. The internet destroyed jobs and created lots of IT jobs, but it’s impossible to guess what those are going to be. So in some ways, that’s great news for those of us in the humanities and liberal arts, because it means that the thinking is going to matter. Good use of AI is creative thinking, asking better questions, evaluating answers. Computer Science, it’s hard to know which exactly to do. But that was also true 20 years ago, your degree in the iPhone I was not going to be useful forever, unless you could figure out they’re going to be new iPhones. And so adaptability, flexibility, has always mattered. It’s just it was slower, and now we’re seeing it’s happening more quickly. It is very hard to know what the job market is going to look like, whether it’s going to be we all work three day weeks, whether certain classes of jobs go away. Is AI really taking jobs? It’s clearly going to take some jobs, but the research says that every job is going to change. There’s at least one task. And so for faculty, my first thing is find out what you no longer need to do, whether it’s making a makeup test, grading, drafts of accreditation reports, assessment of student for accreditation, assessing student learning. There’s got to be something that you hate to do that AI could already do better now. My doctor no longer sits at the computer and types, my doctor now gives me eye contact, which is a little creepy, [LAUGHTER] but the evidence says that doctors who use AI transcribers to do the nurse’s notes, the insurance notes, whatever, that they have more cognitive load to focus on patients, and they’re happier, there’s less burnout. So this is a good thing, right? Because no doctor went to insurance school, they went to medical school, so insurance notes are not part of the job I love. So could AI help with that? And my doctor says the AI is now better, the first six months, he checked it and checked it, but it turned out he was looking stuff up, because the AI is better at it. So what could faculty do that AI is already better at? The department schedule, chairs? Because that’s what’s going to happen, or the bibliography, the literature review, keeping track of what are the latest trends. So that should give you time to do something else. And for faculty that will learn a bit about AI, because you’ve got a few hours back, but I think for students, the question is, how can you be constantly thinking about what else you could do, how your job is changing. I’ll give you one example. So in the old days, if I had a new product or I needed to know how to price my product, I would send a group of interns out to various cities to do focus groups. Go find 12 people at the supermarket and give them a taste, see if they would pay $4.99 for 12 ounces of this. Now I can do that with AI. You’re a 35 year old suburban soccer mom and you live in Oklahoma, and it can give you the synthetic personas to do marketing. So on the one hand you say, “Oh, I’m going to lose my job.” Well, no, because you’re going to go back to your boss and say, Okay, boss, so instead of doing 10 focus groups. I did 1000. I did them in every state, and I did them by year. I did them for every year between 30 and 40, and I did them for rural. And so that’s actually happening. So somebody is setting up synthetic personas to do marketing focus groups for their products, and they’re now able to do work of much higher quality than they were. So there’s still a job, but the job is now different than it was, and so I would classify that as creative thinking that’s raising standards but also how could I do this, not just more efficiently, how could I do this better? What do I want? I want to know how much to charge for my product. What could I do to get better data? And the answer is, “Oh, I could create 1000 synthetic AI personas to do this market research and go back to my boss with way more data than she was expecting.” Those are the assignments I like, too, right? So I might give students an assignment and say, “Okay, so you need to figure out a way to use AI to do something you couldn’t do by yourself. How do you make this better? What else could you now do that you couldn’t do before?” And that’s thinking, that’s the critical thinking, part. And so the process becomes important. It’s not just did you produce this thing, but how did you think about it? How did you make sure it was right? How did you validate? Those are going to be really important questions in the future. Did you validate? Did you make it better? We should be able to do that. We love that stuff.

Rebecca: One of the things that we’ve talked a bit about today is the rapid amount of change, change in what jobs look like, changes in tools. It’s hard to keep up…

José: Impossible.

Rebecca: Right. So what are some low effort and high impact AI practices that faculty might explore if they want to get started, but haven’t quite gotten there yet.

José: Well, the first thing I think you need to do is to try it in your own life. I actually think that just try an AI browser, try shopping, trying to see if AI could help you buy a new subwoofer, buy appropriate gifts for your kids or whatever. I have a really hard relative, Uncle Larry, plays the ukulele, and he likes whiskey, but I got him that last year. Give me 10 ideas for new gifts. Link me to the website. Shopping is really changing, so just finding a way to use it. So we have this website: weteachwithai.com. We have lots of models, lots of tools. Try a literature search with Undermind. That will change your life. Try FutureHouse, if you’ve not tried that, if you’re a STEM person. Try some of these tools. And start to think, “Aha.” Because if you try a disciplinary tool, right? We talked about disciplines earlier, you’re think, “Aha, this is going to change how experiments are run. This is going to change how literature search is done. This is going to change how verification is done.” And now you have the basis for rethinking your assignments. But the easiest thing for faculty to do is to take your syllabus, and if you’re nervous about putting your whole syllabus in, then start with your academic integrity policy, take your cheating policy, put it into AI and say, evaluate this as if you were… and then put the description of the students in your class, right, 18-year olds at Nebraska City College, whatever, put in the description of your students and then say what might be insensitive or unclear. How could I make this policy more motivating or more meaningful to and then describe your students. That works for every assignment, it works for your syllabus. In fact, one thing you can do is build a course profile. So I build a course profile. So I have 64 students. I have three who have this accommodation. I have this student who didn’t take calculus. I put in the details. Then maybe even I asked them what they like. I’ll come back to that. And then I say, so is there anything about this reading list or this assignment that is not going to feel inclusive to my students. I’m teaching engineering, and I didn’t used to have a lot of women. Now I have more women, but I’m using the same syllabus from 1975, could I change some of the examples so they might be more inclusive of a more international or gender diverse group of students? So get feedback from your students. It’s a focus group, essentially, right? Because you don’t know… you’ve been here too long. You like school so much, you’re still here. You’re weird, all of those things. So if I want to know how an 18-year old would respond to my assignment, I need to find an 18-year old. So those of us who had kids go through college found that very useful. It’s like, hey, what do you think of this? But once they graduate, it’s like, “Oh, my focus group is gone.” So now I could use AI to get feedback. So a course profile is a great way to do that, and that’s low-hanging fruit.

John: So you’ve suggested a few ways in which faculty can use AI to improve their teaching. Are there other things they can do to update their courses, to make them more inclusive, etc?

José: Yeah, I have a prompt on the weteachwithAI website that says. “So here’s my syllabus. I want to update my syllabus. I’m really skeptical about AI, but I know students are using AI. So what are some things I need to know? How do I update my policies? You give me 10 ideas for how to create new assignments that might prepare students for this new world. And by the way, you’re an expert.” Learning to prompt, right, recognizing that you have to tell it it’s an expert, is useful. So I think that’s a pretty easy thing to do. Another thing you could do is customize your assignments. Ask AI, “I need an idea for a 10-minute activity to break up this lecture. I need an idea for new assignments, or I have this assignment, but I have 50 students. I know they have different interests. Not all of them are majors, some of them are pre-med. Could you create different versions of this, one version of the problem set for pre-meds, one for this, one for that?” Because nobody really cares about Train A leaving the station and going 20 miles an hour faster than train…. Right? Nobody cares. So why not? The Republicans are registering 20 voters an hour and the Democrats are registering… It’s the same math, but the research says the students were going to do that problem more because it feels like a poli sci problem, or the wide receiver you’re covering runs 30 miles an hour and you’re only…, that feels more like a football problem. And so it turns out that matters. So you could even go to the extent of customizing every assignment for every student. Do a survey before class or the first day of class. So give me an alias. I don’t have to put your name in the AI, but give me an alias and then tell me what motivates you to do your best work. What do you want to be when you grow up? What are your favorite colors? What’s your favorite sports team? What are your favorite hobbies? And then I feed my generic assignment with that and the spreadsheet, I say, now create an individualized assignment with a unique link that you will then email to all of the students individually, that’s a customized version of this assignment. You can also do this in class. So an example: “So write a five page paper on Hamlet.” Okay, so students are like, “Oh, really?” So first what I want you to do is, I want you to tell the AI a bit about what really motivates you. Tell it about your passions for baseball or dancing or whatever it is, and then ask it, how could you customize this assignment so that you could write a paper about Hamlet that overlaps with what really motivates you? Could you write a paper on Hamlet and baseball or Hamlet, using analogies for whatever, and so AI is actually really good at that creative back and forth, because it hallucinates. It makes stuff up. It’s creative. So that’s the kind of way where I can get students to increase their motivation, more customization of their assignment, because they’re using an AI to create and again, I couldn’t do that at scale with 600 students, but with AI, I can customize 600 assignments just as fast as I can customize three.

Rebecca: You’re starting to hint at this, but if faculty implement some of the strategies you talked about today and are in your book, what types of changes would you expect to see in your classroom and in the student learning outcomes?

José: Well, I think that we have two problems. One is that we charge a lot of money for something that’s now out of date. If you’re still delivering content in a lecture, there are cheaper ways to do that. If you’re giving a credential, there are cheaper ways to get the credential. But if what we’re teaching is an experience of critical thinking, of being with other people, of learning about diversity, we should double down on that part, and we can outsource again, not unlike I talked about in Teaching Naked and the internet, but it didn’t quite happen with the internet, right? But we could outsource some of the content acquisition. They could do things outside of class to get first contact with content. And so I do think that classrooms will probably physically change. We do not need 600-seat lecture halls anymore. We need smaller classrooms. We may not need to meet as often with students, but we do want to meet them in smaller groups. So I think that’s going to change, but I think more face-to-face human reactions. If you have an asynchronous course that was mostly online or asynchronous, most of those students are within a few 100 miles. They are fairly local. I was a consultant for one state, and they were having this problem because the students, they’re spread out. And I said, “Well, why are you moving the students around? Move the faculty around. You have seven campuses in your state. The faculty could go to a different campus a day and meet with students, and then the rest of it is online already.” So we have to think differently about what our classrooms look like, what we do in those classrooms. But I think that’s going to be the harder change. Certainly, AI could do grading of papers. There’s a whole lot of caveats to that, but AI could do content delivery. Instead of having to read the chapter, work with my bot that’s interactive, an interactive textbook instead of a midterm. Maybe I have you talk to Einstein, and when Einstein thinks you know enough about thermodynamics, the chat bot puts a grade in the grade book. So those things I think could be automated if we do them carefully. The bit we can’t automate is the “I believe you can do it” piece. “I believe in you, and I’m a human. I believe in you. You can do this.” That bit we can’t outsource. And so the question is, how do we make sure we have time for that. My example is advising. So we think we’re advising. The student comes into our office for a 20-minute meeting, and we go, you don’t have a prerequisite, but you want to study abroad next spring, so you have to take this course now. Oh, you don’t like vampires, and you want to switch? Well, let’s find another sec, right? And we spend 20 minutes adjusting their schedule, and it’s like, “Oh, bye.” [LAUGHTER] And we never got to ask them, “How are ya? How do you feel? Do you have friends?” That’s the mentoring piece. And what we do often is we do administration stuff. Is your schedule right? Do you have the right prerequisite? I have to sign this. That’s administration. So if we separate administration from real mentoring, perhaps I now have a 20-minute mentoring conversation with you and say, “You know so AI is going to make sure you have the right prerequisites, and it’s going to adjust your schedule. And so I’m not going to do that. How are you?” Not everybody can do that, by the way. We have some faculty who are not good at relationships. You used to be able to skate by with, “I’m a content expert. I got my three Nobel Prizes. I’m just going to lecture.” I think that’s going to be harder. So I think that we’re going to see changes to everything about the campus, how it looks physically, how we divide up our time, what people do with students. And I do finally think the stand and deliver lecture is probably going to finally, maybe, go away. Maybe.

John: Turning just a little bit to institutional policies. Are there any sort of policies you’d recommend for institutions in terms of institutional policies with AI. Many campuses have introduced syllabus requirements, that there be an AI statement or something to that effect. Some campuses have gone a little bit further. Are there any guidelines that you’d suggest for campuses that would involve best practices, currently, in terms of AI use?

José: Yeah. So the good news is, the AAC&U has been doing this institute. This is, I think, the third year, and so we have 180 campuses, so we’re seeing a lot of what people are doing. So the first is, last year, everybody was worried about policy. So I would worry less about policy, because policy is changing. It’s not a bad idea to make sure everybody has a syllabus statement, but lots of people will just put “No AI,” and then they think they’re done. So I actually think the first place to start is with control. So I call this CLEAR. So C is for controls. Do I have a safe AI? Do I know which AI i can use? Can I put student data in? Can I not? Do I have to use CoPilot? Or if I’m not going to use CoPilot, what can I use? And what data can I put in there? Because people are confused and they’re told, just use CoPilot… But I want to use Nano Banana. Well, that’s okay, as long as you’re not putting student data in. So understanding what the controls are. L, is for learning. You’ve got to give them places to play. E is for examples. How are you using it in your course? And then A is for adoption. So we need some mechanism for Okay, let’s run a pilot. Let’s do that. Let’s try that. And then R is for review. Did this help or did it hurt? Oh, that was bad. But,there’s going to be failure. This is not all going to work, but in order to figure out what does work, just like anybody who builds… I think it’s Adidas who has a failure funeral when shoes don’t work, they acknowledge that not every pair of shoes is going to be a best seller, not everything we do is going to work. So we have to be able to experiment and try and forgive each other, and be able to have a climate where we know what’s safe. We have some examples, we’re all learning, and then we review to say, “Oh, we tried it twice, because you can’t try it once.” I mean, some things you can, but a lot of the times where we think, Oh, I tried this new thing in class and it didn’t work. So yeah, that’s because the first time you did it, you were crap at it. You’ve been lecturing for 20 years. And so you tried this new active learning thing, and it didn’t work because you didn’t introduce it right, you didn’t set it up right? You aren’t good at it. Try it again. [LAUGHTER] You’ll be better at it next time. And so I think that for teaching, we often think that teaching is just content. And I discovered when I started doing corporate work and I had to do the same 3-hour presentation 35 times in a row, [LAUGHTER] that it got better by the 34th or the 35th time, that actually practicing… I wasn’t changing the content. I was just getting better at the delivery and the management of the client, all those sorts of things. And that rarely happens in higher ed, where we do the same thing over and over again. We get feedback, and we try to make the activity and the learning better, and not just, “Oh, well, I’ll do that next year and try some new thing.” So I would worry a little less about policy and a little more about building the right culture where people are having the right conversations or experimenting. I do think we’re starting to have the curriculum conversation, which is, where does it go? I think people now recognize, okay, I can’t graduate students without any AI. That’s got to be someplace. So the first place to start is the senior seminar, because they’re about to go look for jobs, and so they should know how to do a practice mock interview with an AI before they go: give me a 30-minute mock interview in the format that Microsoft uses for my interview next Tuesday. Here’s the job description. That’s an easy thing to do with an AI and have students practice. But it’s also, read between the lines of this job description to tell me what they’re really looking for, so I can write a better cover letter. AI can read between the lines. So that’s cool. And also, how is the discipline changing? Think about it, you just spent four years as an accounting major, and then Excel spreadsheet gets introduced when you’re a junior, but the curriculum can’t catch up because, like, well, we don’t teach that, because it didn’t exist when you started. Four years from now, maybe. So the seniors’ capstone is the place to say, well, you know what, this new thing just happened. It’s called a spreadsheet, and I’m going to introduce you to the spreadsheet, because you’re an accountant. So that’s a good place to say, Okay, I still need to learn how to do all those other skills by hand, but before you graduate, and that means you’ve got at least one person in every department who is having to keep up with what is going to happen when our students graduate, and what are they no longer going to be asked to do, and what new thing will they be asked to do that we might need to prepare them for. So, senior seminar is a great thing to say, “Let’s put some AI there.” Probably at the freshman year too, about… again, I would redefine cheating. I would think about it as what’s misuse and what’s really fraud. Why does integrity matter? Why do credits matter? It’s the same thing with the bibliography. That’s not about academic integrity. You go to the movies and what do they do at the end? They show you the credits. Is that academic integrity? No, that’s the movies, right? But telling people who gets credit for this matters, and students get that, right? They really care about that. If you steal somebody else’s TikTok, that’s not academic integrity, but it matters. So there are probably other places you need to do this, but I don’t think you can have one policy for everybody. I am noticing that some universities are now having a policy that you can no longer ban the use of AI for specific things. In other words, you can ban AI, but you can’t ban it for feedback, or you can’t ban it for brainstorming. So they’re limiting, because students were going from all AI to no AI, and it’s like, “I’m confused.” So nobody can ban AI for everything, in the same way that you can’t ban a spell checker, right? We don’t do that anymore. We used to, we used to ban pencils and erasers too. We didn’t like the typewriter either. But we’ve had a long history of this. It’s going to change everything. Yep, and it did.

Rebecca: So going back to your idea of controls, rather than really thinking about policies, but giving some boundaries on things, you mentioned, identifying specific tools that maybe people are allowed to use, what kind of data they can put into such tools. Paid services often have more services or more capabilities, and so in the interest of equity, should institutions adopt some sort of standardized tool that everyone then has access to?

José: Ah… it’s a lot of money. So I do think that we need to think carefully about those issues. It may be the case. The problem is that Microsoft CoPilot is expensive and you buy one tool. So I think the first thing is for people to understand what happens to data when you put it in if you’re already using Microsoft on your computer, and you wrote your dissertation on Microsoft and you use iCloud, or you used Microsoft 365, OneDrive, or you used Google Docs to write your dissertation, it’s already in the AI. You have much bigger privacy concerns when you talk in your car. There are other things that we need to talk about for privacy. So I think the first step is helping people understand what does happen to your data when you put it in an AI and which AI. And by the way, there are these controls. If you open your settings, you can turn some things off… not sure I trust them… but you need to know that the default in Claude is off, but the default in ChatGPT is on, and that will probably change tomorrow. So you need to be checking your settings, and you need to be thinking about what kind of data is it ethical and legal to put in here? And do I want to do that? If I do that, then there are a lot of free tools I could use. So I think step one is not saying, “Do whatever you want, but only do it in this tool,” because then everybody’s an idiot. Much better to educate people, that’s what we do, and to say so let me educate you as to what the tools do and how they do it, and what are the parameters. And then you can make informed decisions about whether or not you want to use free Gemini twice a day, which is all [LAUGHTER] you can use in the best model, but at least you have that. And then you can switch to ChatGPT for your three times a day or et cetera. And we’re going to have this model, and you can always use this model for this kind of thing. But I think if you don’t also educate people as to how this works, then having one tool for everybody is just a waste of money, because not everybody’s going to use it. People are going to use their favorite tool anyway, and they’re going to use it poorly and with risk. So the first question, the way to reduce your liability is to educate, not to restrict. And so I would start with that, I like BoodleBox because I think the pay structure is reasonable. It’s a used-based model. It gives you access to lots of different models, it’s not just only ChatGPT or Gemini, because one of the things we now know is that Claude is better at Excel than CoPilot, and that it used to be the best writer, but now ChatGPT is a better writer, and Claude is a little verbose, and Gemini does other more…. So they are distinguishing themselves. They’re doing different things. And of course, we have all the millions of APIs that really do different things, and so I don’t think, for the moment, anybody can get by with just using one model. I mean, yeah, you should try making a picture in Gemini and writing text. That’s good. But we’re in the AskJeeves, AltaVista phase. [LAUGHTER] I don’t know where we’re going to end up. And so for the moment, it’s useful to try a couple of things if it doesn’t work in one try another, but to understand how data and privacy work.

John: And that reminds me of Dogpile, I think that was the name of it, where if you put a query in there, it would search all of the major tools, [LAUGHTER] which sounds like perhaps the best way of approaching this now.

José: Yeah, and there are tools that do that. POE will do that, but I also think that it’s not just about getting in it because you have to follow up. Iteration is a part of good usage. And so picking the right tool is important, but mostly following up. I was doing some shopping the other day, and I had a really odd problem. I have this old set of speakers and an amp from the 1990s so I needed a new piece of equipment. So I tried Gemini, ChatGPT. I was using Comet. I tried different things, and I didn’t know what the answer should be, but I got pretty good advice. But let me just try this over here. Let me try changing the prompt a little bit, so I think that sort of skill is going to be necessary for everybody to manage, in the same way that digital literacy, not everything you see on the internet is true.

Rebecca: What?

José: I know, I know. It’s hard.

John: Well, we always end with the question: “What’s next?” …which is an especially important question in addressing AI.

José: Oh gosh, yeah, nap would be the right thing. Well, here’s what I think. So I think what’s next is that we’re about to see lots of new competition from places you didn’t expect. This one. I do know, we’re going to see a $6,000 eMBA from an accredited university rolled out in the next couple of weeks, the whole degree. So it’s the professors have been turned into AI avatars, so it’s scalable. So it’s cheaper than your MBA [LAUGHTER] or even your eMBA, and they’re hoping to scale it. So we’re going to see degrees that are totally AI, that are going to be cheap and scalable. And this is something new, because traditionally in strategy, you can either be customized and expensive or low cost. Can’t be both. But AI allows you to be low cost and customized. So your new competition is an entirely new class of things. On the other end, we’re going to see boutique institutions. You’re going to see Harvard. They don’t have to change because they’re Harvard. So we have in-person classes. We do this, but they’re expensive. So the question is, for most of us, where in that spectrum are you going to be? Most of us are not Harvard, so we can’t charge what they charge, and then have boutique. So are we organic vegan boutique, or are we, you know, mass? So most of us are going to be neither extreme, but those two things are coming at us, and so the market is about to differentiate or spread. And so for most places, thinking about what kind of institution should we be? Where are we going to plant our flag? And it’s not whether or not we’re going to have AI for most of us, it’s how much and where. Harvard could ban AI and get away with it and give you a four-year degree with no AI because they’re selling a credential, but most of us cannot do that.

Rebecca: Well, thank you so much. This has been a great conversation. We appreciate you joining us today.

José: I just make stuff up as I go along. [LAUGHTER]

Rebecca: So does AI. [LAUGHTER]

José: And that’s why we’re both creative people [LAUGHTER], or creative entities, I guess.

Rebecca: Beings?

José: I don’t know. [LAUGHTER]

Rebecca: I don’t know. [LAUGHTER]

John: And by the time we release this, the second edition of your book will be out. In fact, my copy is arriving in my mail tomorrow. I’m looking forward to the second edition.

José: Yeah, December 2 is the drop date, so Amazon should switch over everything, it should switch to the second edition tomorrow, and there’s a lot more. We didn’t think it was going to be 40% new, but it is. We thought, oh, we’ll make some updates and we’ll fix this. It’s like, “Oh God, scrap that, whole new chapter.” So it’s a thicker book with a lot more than we thought it was going to be.

John: As I think has happened with AI in general, it’s a lot more than many people thought it was going to be when it first appeared.

José: There you go.

John: Thank you.

Rebecca: Thank you.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing assistance provided by Ryan Metzler.

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416. Teaching More Effectively with ChatGPT

The rapid evolution of generative AI tools has introduced an expanding set of educational applications. In this episode, Dan Levy and Angela Perez Albertos join us to discuss how these changes are affecting faculty and classrooms.

Dan is an economist and a senior lecturer in Public Policy at Harvard University where he teaches courses in quantitative methods, policy analysis, and program evaluation. Angela is a graduate of the MPA program in International Development at the Harvard Kennedy School, and is the U.S. Head of Strategy at Innovamat. Dan and Angela are the authors of the first, and now the second, editions of Teaching Effectively with ChatGPT.

Show Notes

Transcript

John: The rapid evolution of generative AI tools has introduced an expanding set of educational applications. In this episode, we discuss how these changes are affecting faculty and classrooms.

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John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

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John: Our guests today are Dan Levy and Angela Perez Albertos. Dan is an economist and a senior lecturer in Public Policy at Harvard University where he teaches courses in quantitative methods, policy analysis, and program evaluation. Angela is a graduate of the MPA program in International Development at the Harvard Kennedy School, and is the U.S. Head of Strategy at Innovamat. Dan and Angela are the authors of the first, and now the second, editions of Teaching Effectively with ChatGPT. Welcome back, Dan and Angela.

Angela: Thank you so much for having us.

Dan: Thank you very much.

Rebecca: Today’s teas are:… Dan. Are you drinking tea with us today?

Dan: Yes, Moroccan tea. My family is originally from Morocco, so I tend to drink this one.

Rebecca: Always a good flavor, for sure. How about you, Angela?

Angela: Yeah, this morning I’m drinking chai tea latte. This is my usual morning drink.

Rebecca: How about you, John?

John: I have a tea which actually has a French name, which I’m not going to try to pronounce, but the English translation is chimps’ tea. It’s a black tea with fig and honey and a few other things in it, and it’s really delicious. It was sent to us a while back by one of our listeners in France, which was really nice.

Rebecca: It’s always nice to be able to try new things. I have an Assam tea this morning from Pekoe Tea in Edinburgh.

John: Impressive. She got it there herself, mine came through the mail. [LAUGHTER] We’ve invited you here today to discuss the second edition of Teaching Effectively with ChatGPT. Since some of our listeners may not have heard our earlier discussion about the first edition of this book, can you provide an overview of the purpose of the book and who is the target audience.

Angela: Thank you so much, John. The main purpose of the book is to provide a guide for educators to understand how ChatGPT and other AI tools can be useful for more effective teaching and more effective learning. Now what our main approach is to provide plenty of examples that are practical, to really demystify what AI looks like and how it can look like in and outside the classroom for learning.

Rebecca: In Chapter Two, you describe three guiding pedagogical principles that you use throughout the book, and these principles we certainly talked about on the podcast before and are great advice to anyone involved in course design, whether or not they’re using generative AI. Can you tell us a little bit about these principles and why they’re so important?

Dan: Sure. So the three key pedagogic principles were meant to give the reader of the book an overview of what are the underpinnings of the pedagogic advice of the book. For those of you who listened to this podcast, you might not be surprised in hearing the first one is to be student centered. That sounds very obvious, but yet we spend a lot more time thinking about what we do in the classroom and less time about what our students are doing in the classroom. We spend more time thinking about what we will cover in a course, rather than what our students will uncover. And so being student centered just means paying very much attention, and the focus of the teaching enterprise is stimulating the learning of our students. So that’s kind of that principle. The second principle is to plan for active learning. I think the two of you in this show have said, many times, there’s ample research suggesting that people don’t learn by just listening to someone telling them stories they learn when they actively engage. And the third one is to begin with the end in mind, and this is a little bit of a different way of saying we use backward design. So the idea we start with, what do we want the students to be able to do or master at the end of every learning experience, and then we design backwards from there. And so the book, very much, has these three principles embedded. A lot of the activities that we propose for educators to do have to do with how they can create more active learning in the classroom, and so the three principles embody a lot of what the book is about.

John: Could you each give us an example or two of ways in which Chat GPT can be used to design new classes or redesign existing courses?

Angela: Absolutely. So I can get started with a very cool use that, Dan and I actually did together when we were redesigning one of the classes on the Bayes’ rule in a statistic class, and there was a specific question or activity that was based on COVID and a positive and negative test and the possibility of a false positive and false negative, and we felt that was already an outdated activity by the time that we were working together, and so we wanted to update it to new examples that felt more appropriate for the times. And we could not pin down what could an idea of a different theme or contextualized activity could be. And we asked ChatGPT to just share what could be like 10 potential topics or things that we could explore that would help the students understand how Bayes’ rules can be valuable in a real-world context. And it gave us an infinite amount of ideas that we would have never guessed just by brainstorming him and I together, and just to give like two specific examples that we liked. One was about art forgery and being able to identify fake art pieces, and the other one that we ended up using was about being able to detect AI plagiarism in student work. And so it’s incredibly helpful for anything that has to do with brainstorming contextualized tests and activities to context that are valuable for students.

Dan: Yeah, so similar to Angela, I can think of several examples in terms of designing course, but just to give a recent one, this is more about designing an assessment, similar to what Angela just mentioned. I created a project for a course… and I’m sure we’re going to talk in a little bit about projects… the syllabus has a lot of things, and there was a question that we had created last year that we wanted to change. And so I uploaded the question to ChatGPT, and I said, “Can you tell me what you perceive to be the learning goals of this question that I just gave you?” And I think it did an amazing job, better than I could have stated them myself. This was very helpful. I think part of it is because it had access to the goals of the course, but I think it was very helpful in helping me be better at interacting with ChatGPT in the refinement of this learning activity. So that’s just like one example. I realized both examples that Angela and I gave are in the realm of assessment, but it turns out to be a very important realm in terms of designing sort of a new session or new course, the book goes over to what I think, still today, is my favorite and maybe Angela’s favorite activity. This was kind of maybe the spark of the book. We were trying to think about an activity surrounding the Cuban Missile Crisis. And over two weeks, basically had a conversation with ChatGPT along the lines of, “help me design a better class session on this topic that I don’t know too much about.” After I saw that, I thought this has a potential to be an incredibly useful tool for teachers and learners, as Angela said.

Rebecca: Your first example seems like a really good reminder that we can use tools like this to help implement things like TILT (or transparency in learning and teaching), having it help us be more clear about what the course objectives or assignment objectives are, so that we can then convey that information to students.

Dan: Right. And you might have the reaction, but how can you not know? It’s not that you don’t know, it’s that maybe some of them are subconscious to you. I mean, one of the most useful things that a teacher can have is someone, or in this case, it’s not a person, but put a mirror to what you’re trying to do in the classroom. So that’s why having a colleague come and see you teach is so valuable, because you might do things and you might not realize why you’re doing them, and maybe having someone ask you or sort of give you ideas for why you’re doing them, it’s helpful. The book is based on the premise that AI can be an incredible assistant, but also an incredible thought partner. And I think both of those examples have those two roles being played quite intensively.

Rebecca: One of the other things that you certainly have examples of in the book is the way that ChatGPT can be used during class sessions. Can you give some examples of this?

Dan: Yes, we can give several examples. So, one of them, it’s an example that is described in the book. But our colleague, Mitch Weiss, who teaches at the business school, has a wonderful activity that is about discovering the capabilities of AI, and students use that in the classroom in a case called Storrowed. And so probably not the best use of time here to describe the full activity, but suffice it to say that students are put into two groups, and they’re kind of competing to be able to see who comes up with the best ideas, but they’re able to use AI for proposing some of their ideas. So that seemed like a very interesting one. The book also highlights two examples of using AI to summarize student input in the classroom, and I think that’s an incredibly powerful tool. I think a lot of educators have heard about the one minute paper, the way that it typically worked, at least until not too long ago, was you ask people to give you what was the main idea that they learned, or maybe the muddiest point, or whatever it is, at the end of class. This is incredibly valuable, but normally you would just get out of the classroom and then have to read and sort of think about how it works. And now you can do it in the classroom, live and understand what are the main themes that people are picking up. You can also do it with questions. And then a very exciting use that I experimented with a month ago is with group work. I don’t know how much you have been in situations where you assign students to work in groups, and then 10 minutes later, you’re trying to debrief. But debriefing is a little bit hard because you don’t really know what happened in the groups. You might circulate, but if you circulate, typically, the moment you approach a student group, the conversation changes, so you’re not really listening to what they’re describing. And so what I did is to build a simple custom GPT that did the following. So the students were put into groups, and they were asked to input their answers to the activity in a Google slide. So this was typical use, especially during COVID. You did breakout rooms, everyone fills in a slide. As soon as they’re done doing that, what custom GPT does is it produces four slides for the instructor. So this is in a matter of a minute. And so the first slide has the main themes. So of all the groups you had in the classroom here, the main themes that were emerging. The second slide has particularly novel or interesting ideas that emerge. But not only they tell you the idea, but it also tells you it was group three that came up with this idea. So you can use that in the debrief, “Hey, group three, I noticed you came up with this idea. Can you expand on this?” The third slide tells you about points of contrast or tension between what the groups did, and then the fourth slide gives you some ideas for how you could debrief. And so the fact that all of this is available within one minute of students finishing the group work, I found it to be incredibly empowering to lead a much better debrief discussion. So those are the kinds of uses that I think advance learning in the classroom in a way that it would be very hard to do without a technology like this.

John: You have a chapter discussing how ChatGPT can be used to either design or grade assessments, and there’s been a lot of discussion on this in various social media forums and also in some editorials and articles and so forth, in terms of whether generative AI should be used to create assessments and to grade student work, with some students being concerned that they’re not getting the direct feedback from their professors. Could you talk a little bit about what ways might be appropriate and how this may be helpful.

Dan: So, I would like to separate the question into two parts. One is about designing assessment, and the other one is about giving feedback in terms of assessment. I am convinced that for designing assessment, AI can be incredibly helpful without sacrificing the voice of the instructor or the learning objectives. And the two examples that we gave at the beginning, I think, are suggestive of how can they be used. It can convert you in a much more creative teacher, because you have a thought partner with which to bounce ideas. It can give you feedback on the assessment that you’re doing. My colleague from the business school, Mitch Weiss, again, he has this custom GPT that is like eliminate ambiguity in my exam questions. And so he gives the exam question to the AI tool and essentially “write it in this way so that there’s less ambiguity for students.” So you could imagine ways in which the design of assessments can be improved with the help of AI. I’m not saying we should farm out the design. It’s like, “okay, I’m teaching this class, and now you go, AI, create the assessment, and that’s it.” But I do think, in my mind, whatever drawbacks there are, weighing everything, I think the impact is positive. For providing feedback on assessment, I think you have decided this is a big topic, a lot of controversy about student expectations on assessment and so on. I am not going to tell you that I’m convinced about what the right answer is, but I would say, as we describe in the book, there’’re both benefits and risks associated with doing this. But what I would like to suggest is that what we know from the science of learning is that students benefit from both practice and timely feedback on that practice. So that we know. And so the question to me is, are our students getting enough opportunities to practice and timely feedback? And my sense is the majority of us educators feel that the answer to that question is, no matter how hard we work, it’s very hard to provide timely feedback to students of the sort and in the quantity that they need to really, really make a difference in terms of their skill development. And so what we end up is with assessment strategies that are the most feasible given all the constraints that we face. But I think we’re leaving some value on the table in terms of our students being able to practice. So I’m not suggesting that AI should do all the assessment work for us, but if it could work as a complement to the work that we already do, I think this could potentially be a useful thing. Just to be super concrete, maybe if you had five ways of assessing students in the semester, but you’re only able to grade two because of time constraints or whatever it is, maybe you can use, in a very transparent way, here’s AI to help you provide feedback on the assessment. Now, of course, this has to be done with a lot of care and thought, but we can’t do it by hiding from the students that we’re doing it. And also, I think we need to explain to the students why we’re doing it, and if we’re just saying to ChatGPT, “just give them feedback without our input,” then our students, very justifiably will say, “Well, what are you doing? So I can do that.” So I think there is thought that needs to go into how AI is used to be able to do it. Just to close, we have here the Kennedy School, a course taught by Shard Goel, Teddy Svoronos, and myself, and we have our Teaching Fellow, Calvin Isley. He basically helped us design a system by which the assignments would have a preliminary assessment done by AI, and then our teaching team would take into account that and use it as input for grading the assignments. Students were able to opt in to this pilot. And what I think happened is the students who opted in got more detailed feedback than they would have gotten in a system where we’re just counting on human labor. So, in sum, I’m not saying AI should do everything in providing feedback, but I do think there’s a role.

John: And humans get tired. And if we’re providing feedback on many students, the quality of that feedback may very much vary over time in a way that it wouldn’t if you’re working with an AI tool for assistance and at least providing a starting point.

Dan: For sure. I mean, one of the things we say in the book is that AI has like, two very important virtues: it’s available 24/7, and it has infinite patience, and I don’t know many human beings for whom we can say that.

Rebecca: One of the things that you just highlighted was student practice. And you have a number of chapters in your book about how ChatGPT can help students learn. Can you give us some examples in which ChatGPT can support student learning?

Angela: Absolutely, these were one of the chapters that I was more excited about writing, because when we find many of the books that are there is that they focus a lot on the teaching side of things. But there’s so much that as educators, we can do to just teach students to use ChatGPT in a way that is beneficial for learning. And just to give a couple of examples, the first one I’ll give is in continuation with this idea of practice, we have an example in the book of one student that was studying medicine, and she was learning and preparing for an exam, and as she was finalizing her preparation, she wanted more activities to practice, because she had already completed all of the activities that her teacher provided. And so she just asked ChatGPT, “Can you create more questions? These are some of the questions that I received. Can you generate more questions and then don’t give me the answer? I will give you the answer, and then you will let me know if it’s correct or not.” So it’s just an extension of this practice that, again, it’s one of the things that for educators, it’s hard to provide enough practice opportunities for students because it means generating more questions and more questions that cannot be used for formal assessments. So that’s one possible example. The other possible example, it’s the personalization of learning, and this, again, it’s one of the big moments that for Dan and I, it just sparked our interest even more for what AI could do for learning. And the specific moment was related to time when in one of Dan’s classes, he encouraged his students to learn about risk management strategies before the class itself, and then he asked. The students to share what the conversation with ChatGPT had been like. In addition to that, he nudges students to ask ChatGPT about risk management in a certain way. So for example, sharing what context they had about risk management strategies, etc. And we basically saw very different conversations depending on the student. You had, students that were much more familiar with risk management strategies and the kind of explanations that they received were much more technical, much more detailed, less intuitive for somebody that would be just exposed to this concept. Well, for some students, it was the complete opposite. It was a much more intuitive, just like high level conceptual explanation with lots of examples that could be relatable. And this is something that would be very hard for an educator to replicate, a specific one-on-one explanation that would fit the needs of every single student. So many multiple uses for students that can really help improve learning.

John: In the spring of 2025 we had a reading group on campus that read your book. And just as a little bit of an aside, one of the things that happened is, right at the very beginning, people suggested that people would experiment with something, try it, and then bring it back and report at the next reading group meeting. And that worked really well. It’s a great book for a reading group. But one of the things that surprised people a little bit who had experimented with ChatGPT was the ability to customize it to meet your individual needs. Now some people weren’t able to do that very well if they were using it in a wide variety of applications, but now, with the new edition, you’re able to talk about projects in ChatGPT. Could you talk a little bit about how you can customize ChatGPT in general, and also how you can use projects for a similar sort of purpose?

Angela: Absolutely. So to put it simple, because sometimes I feel it can be hard to get lost in all of these, like features and new releases of ChatGPT. The idea here is that you can customize ChatGPT so that it’s more responsive to your needs or your specific context. And to put this into an example, my specific context, for example, may be I teach an economics course to first grade university students. But the reality is that I don’t use ChatGPT only for my classes. I may use ChatGPT also for other uses. And so what openAI has rolled out in this past year is the possibility to customize the responses of ChatGPT at different levels. The first level is to have instructions that apply to any chat that you open in ChatGPT, and that’s what system instructions do in chat GPT. For example, you can keep this to a very general level, like, “I’m a woman, I’m 30 years old. I do this as a profession. I’m from this country, etc, etc” things that may be relevant. For cases where you may use ChatGPT frequently, but it doesn’t apply to any use of ChatGPT that you do, you may want to choose projects instead, where you may have a project for a specific course that you teach. You may have a project for something that you do frequently with ChatGPT. Perhaps you were talking about travel planning before, like, perhaps you travel plan a lot, and you want to have a project specifically where you can provide instructions around the kind of travel that you like to do and the kind of places that you want to go to and the kind of things that you want to do when you go on traveling, and so that’s what projects allows you to do. It allows you to have instructions that you don’t need to be repeating every time you open up a new chat, but then don’t apply to all of your chats within ChatGPT.

Dan: The only thing I would add to what Angela just said is that not only can you add instructions which are very helpful in customizing, but you can add files, and those files can be incredibly helpful. So, for example, for each of my courses right now, I have not only instructions about teaching philosophy, goals of the course and so on, but also the syllabus and relevant material that I think has helped, as Angela said, the advice that you get from ChatGPT be much more customizable. Back in the old days, and old days here means 2023, when you were writing one chat and then you would begin another chat, it was like two completely independent conversations, and now I think AI is much better at understanding through not just the customizations that we just talked about now, but through the use of memory and so on, it’s much better understanding who you are as a user. I think one aspect of the progress in LLMs, ChatGPT in particular, but I think it’s true for all of them that we have picked up in the second edition of the book, is increased personalization and customization that is now available to a user to be able to get more customized and specific advice from their LLM. And the last thing I want to say is that everything that Angela said is very, very, very, very easy to do. You don’t need to code. You don’t need anything. If you know how to prompt ChatGPT, you can create a project, and it’s as simple as you put in instructions, you put in files, and then you create the chats within that project, that’s all you need to do.

Rebecca: You’ve hinted at some ways of taking advantage of projects. Can you give one or two specific examples of where this could benefit student learning?

Dan: So I think the easiest way Angela mentioned is you can create a project for each of your courses. Once you do that, then a lot of the help that you asked ChatGPT would be useful for creating activities for students, helping you design assessments and so on. So that’s one use of projects. If you want to create something that students consume directly, then you would move from projects to custom GPTs. Custom GPTs can be shared with students and they can interact with it. In the first iteration of custom GPTs that were most popular were the tutor bots, so where you might ask students to interact with a tutor bot, for example, to learn some skills in whatever subject that it is that you teach, but where the instructor was playing kind of a design role, was adding materials and so on. I think those tutor bots continue to be helpful. One of the things I think, I suspect many of us have discovered is that if the tutor bot is not part of the natural workflow of students, then it becomes a little bit harder for them to benefit from it. So if students are using ChatGPT as their AI tool or any other AI tool, it’s going to be hard to move them to the custom GPT or to the tutor bot, unless you sort of have very directed task at them doing that. But there are many, many other ways in which you can use custom GPTs with students. And before we were talking about giving students an opportunity to practice, but imagine you were teaching negotiation, for example, you could imagine, and there are some available out there, bots that allow students to practice negotiations or public presentations or so many other skills. So if you teach any skill that you think benefits from repeated practice and feedback on that practice, I think the potential of AI to help you and your students in that task is immense, and custom GPTs are a way to make this happen. And again, the book is called Teaching Effectively with ChatGPT, so we put our stake in the ground with selecting one LLM, partly because it was most popular sort of tool. But if you’re using some other LLM, like Claude or Gemini, the equivalence of everything we have talked about exists. So Claude has projects. You can build custom GPTs in Gemini, through what they’re calling GEMs and so on. So I think a lot of these companies are all building and competing with each other in this space.

John: And in addition to providing those tutor bots, you can also design them to help you with course design, things that will generate things in the TILT framework, for example, or make suggestions for adding more active learning activities. Pretty much any of the things that you talk about in your book, if it’s going to be done repeatedly, could be turned into a custom GPT. And your book was really good at providing them, that was a really popular chapter with the reading group.

Dan: Thank you. I just want to say one thing. So since the first edition of the book, projects became a thing, and I would say, I think building custom GPTs is relatively easy, but if you’re only going to use that tool for yourself, then building projects is an even easier way to accomplish the goal. Obviously, custom GPTs have the advantage that they’re shareable, so you can share not just with students, but with colleagues and so on. But if you’re getting started and are feeling somewhat intimidated with how do I do all this stuff, projects is a very, very natural step up from your ad hoc use of ChatGPT.

Rebecca: There’s still a lot of faculty who maybe would prefer to ban student use of AI or focus their efforts on trying to detect student AI use. Are there any ways of reliably detecting AI use?

Dan: So I’m not a computer scientist, but the ones I speak with give me pause that we will ever be able to really do this at a level of reliability that suggests that we can take action on this. My general sense is that this is a lost battle in the battle of an instructor against students. I think students always win, is my sense. If this is the battle that we’re fielding, we’re not going to win. I think we have to do differently. But just to give you a sense of for why I think it’s so hard, is that now AI is so embedded in everything that we do, that I think it’s going to be very hard. Back in the old days, again, 2023, you could ask students, okay, go to ChatGPT and then have a conversation and then give me the transcript. But now it’s embedded in the things that we do. It’s auto complete things in Word and so on. And more importantly is that you could, as a student or as a human being, produce an essay by yourself. You created all these ideas, and then you give it to ChatGPT and say, “Please improve the clarity of my writing.” That’s all you’re asking it. It will add a few em-dashes and a few words, and then everyone will say, “this was written by ChatGPT.” And you might decide, as an instructor, particularly if you teach writing, that even the use that I just described should not be allowed. But I’m going to suggest that at least for some subjects, that use is fine. And if that use is fine, an AI detector is going to say that this was created by AI. I think we’re in trouble.

John: Since the first edition of your book, openAI has released ChatGPT versions 4 and 5 more recently. You mentioned projects, but what are some of the other changes that have been added to ChatGPT since then?

Angela: Great question. So projects, as you very well mentioned, is one of the main features that was released. There’s another very interesting one, which has to do with the models that are available within ChatGPT. So before ChatGPT 5, there were multiple models available, as most people in the audience would know, where there was a basic default model, which was valuable for most of the tasks. And then there was a deep research model that is valuable for in-depth, multi-step, more complex tasks that require time to think and process before being able to generate an output. So this can be useful, for example, to summarize academic research, to create classes or provide feedback on longer documents, anything that is more complex than the average task. Now, what used to happen is that experienced users knew about this model, and they would change the model that they wanted depending on the task that they were wanting to do, but non-experienced users would just default to the basic model and would not be using the most out of the capabilities of ChatGPT and so with the release of ChatGPT 5, one of the key developments was that ChatGPT 5 automatically selects the model that best fits the task at hand. And so this is a great improvement in terms of being able to really put at the service of users all of the capabilities that ChatGPT has. One of the important things is that when it was first released, there was a big controversy, because some people claimed or complained about the fact that ChatGPT was not able to always optimize for the best model, and they wanted to retain that agency. And since then, openAI has backtracked and still provides everybody the ability to be able to choose the specific model that they want to use.

Dan: We’ve already alluded to this, but to me, a big development in the last year or so is the ability to customize and personalize your experience, not just through projects, through memory. and other things that we go into in the book. In fact, the chapter that we used to call Creating Custom GPTs, we’ve now broadened the title of that chapter to include many other ways in which you could customize and personalize your experience and that of your students through the use of this feature. So that, to me, feels like obviously ChatGPT 5 is better than ChatGPT 4, but back then, now there are these reasoning models that are automatically selected and so on, is useful. But the thing that you will notice more as a user, I think, is the potential for customization. And obviously the reasoning models can be very, very helpful for more complex tasks, deep research, which Angela briefly mentioned, is another important advancement for much more complex tasks.

Rebecca: As we’ve noted in this conversation and also in your book, generative AI platforms change quickly. [LAUGHTER] And when we talked to you in the first edition, you mentioned having a companion website that you would continue to update. Are these updates something that are going to continue with this edition?

Angela: Absolutely, that’s one of the main advantages of the companion side, the ability to be able to update it. As we know this is a fast evolving space, and that we felt that was a good format to be able to digest information. But one of the main inconveniences is that it’s not life, and so users and readers and listeners can always go back to the companion side for more up-to-date information.

Dan: And the companion side has all the prompts that were used in the book, including, for those educators that shared with us their prompts behind their custom GPTs, they’re also there. The site is freely available, and then it also has a lot of the examples from educators.

Rebecca: And we’ll make sure we have that link in our show notes.

John: And people found it really valuable when we’re doing the reading group to have all those examples there so that they could modify them for their own needs. We always end by asking: “What’s next?”

Dan: Well, I want to say something that’s next that is, thanks to Angela. So I think we are maybe one or two weeks away. Hopefully by the time you publish this show, it will be out. We have a edition of the book in Spanish that Angela gets all the credit for making it happen. So we hope that means that more readers around the world will be able to use some of the insights from the book. If you’re a listener and want the book to be available in some other language, let us know, and we’ll try to see if we can maybe collaborate with you and make it happen.

John: And Angela, what’s next for you?

Angela: Getting on a plane, flying back home for the weekend, and getting that book ready.

John: Well, thank you. It’s been great talking to both of you.

Angela: Thank you so much for having us. It’s always a pleasure to come and have tea with both of you and to talk a bit about AI developments in teaching and learning. So always a pleasure. And thank you so much. Hopefully we’ll see you again sometime soon.

Rebecca: Safe travels.

Angela: Thank you so much.

Dan: Thank you.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing Assistance provided by Madison Lee.

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