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
- Vee, A. (2017). Coding literacy: How computer programming is changing writing. Mit Press.
- Laquintano, T., Schnitzler, C., & Vee, A. (2023). Introduction to teaching with text generation technologies. TextGenEd: Teaching with Text Generation Technologies.The WAC Clearinghouse.
- Bruff, D. (2009). Teaching with classroom response systems: Creating active learning environments. John Wiley & Sons.
- Bruff, D. (2019). Intentional tech: Principles to guide the use of educational technology in college teaching. West Virginia University Press.
- Computation & Writing: Intersections between computational tech & textual writing – Annette Vee’s substack
- Rhetorica: Navigating how AI impacts education, culture, and society – Marc Watkins’ blog
- Agile Teaching – Derek Bruff’s blog
- Vee, Annette; Marc Watkins; and Derek Bruff (2026, forthcoming). The Norton Guide to AI-Aware Teaching. Norton.
- Lunsford, A. A., Brody, M., Ede, L. S., Moss, B. J., Papper, C. C., & Walters, K. (2023). Everyone’s an Author with Readings. 4th ed. WW Norton.
- AI & How We Teach Writing – Annette Vee’s substack for Norton
- Derek Bruff (2026). Teaching Math with AI with Amy Langville, Chloe Lewis, Lew Ludwig, and Kathryn Pedings-Behling. Intentional Teaching Podcast. Episode 88. April 21. (the podcast that John referred to)
- Adam Bridgeman, Danny Liu, and Ruth Weeks (2024). Program level assessment design and the two-lane approach – University of Sydney. September 12.
- AI hallucination cases database
- TILTHigherEd
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.
[MUSIC]
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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