431. A Curated AI Framework

Students can use AI tools to support or short-circuit their learning. In this episode, Tim Curry joins us to discuss how he uses AI tools to support students with diverse prior knowledge in a carefully curated framework.

Tim is a lecturer in the Department of Health Sciences at Northern Arizona University. He is developing AI applications for autism research and tribal health service while building Northern Arizona University’s Applied Health Analytics program. He’s also working on the completion of his PhD degree in health equity technology.

Show Notes

Transcript

John: Students can use AI tools to support or short-circuit their learning. In this episode, we examine how one instructor uses AI tools to support students with diverse prior knowledge in a carefully curated framework.

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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 Tim Curry. Tim is a lecturer in the Department of Health Sciences at Northern Arizona University. He is developing AI applications for autism research and tribal health service while building Northern Arizona University’s Applied Health Analytics program. He’s also working on the completion of his PhD degree in health equity technology. Welcome Tim.

Tim: Hey, John, it’s great to be here.

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

Tim: Yeah, I am, actually. I’ve got one here that is this really nice licorice root spearmint mixture.

Rebecca: That’s an interesting combination. I don’t know that I’ve heard of that.

John: And I have just a plain old oolong tea today.

Rebecca: A nice choice. I’ve swung back to old habits, and I have an English afternoon tea.

John: Must have been a tough day, huh? [LAUGHTER] Today, you’ve been doing some interesting work in which you’re planning to have students work through a learning portfolio constructed for each course module in a health science course in which they’ll complete a structured sequence of interactions with a chatbot tutor that you’ve created. Before we discuss the details of this course, can you tell us a little bit about the course? How large is it? What’s the course level? And who are the students that generally would be taking this course?

Tim: Yeah, absolutely. So this course that we’re focused on today is HS 300 which is our human diseases course. It’s quite an interesting course. A typical class is usually about 40 students, usually juniors, although I do get sophomores and seniors in there. And why I say it’s an interesting class is even though it lives within our department’s program of public health I would say, in the average term, I only have about half the class filled with public health majors. The rest of them are a mixture between students who want to go pre med, so nursing students or med school, DPT, biomedical engineering, and then also a smattering of psychology majors. So it’s quite a diverse group I’ve got in this class, to be very honest, with a range of different backgrounds.

Rebecca: Can you talk a little bit about your general approach to how you’re planning to address the class? A little bit about the portfolio and the structure overall?

Tim: Yeah, do the objectives of this class are for students, broadly speaking, I won’t get into the full curriculum guide here, but broadly speaking, is for them to better understand not only a wide range of human diseases across all of our systems, but also the application of that knowledge to both the clinical side, so think more like Western care and also the public health side. So we have this interesting mixture here. In terms of the portfolios, the reason I chose to start with this class with this concept was, as you can probably imagine, with the varied degrees I have in there, I have students walking into this class with everything from having completed anatomy and physiology I and II and maybe a pathophysiology course, all the way to students who haven’t seen biology since high school, because they just haven’t reached that point yet. That makes it a challenge. It’s a little hard to talk about congestive heart failure if they don’t know the anatomy of the heart. So that is the reason I chose this approach, is because, and I know we’ll get into this, really is focused around meeting the student where they’re at in their current process of learning, which I think is really the best approach, especially in a course like this, where background knowledge is so critical.

John: Could you walk us through the steps that students would take through in a typical module? And I believe each one of these modules is for one week?

Tim: Yes, that is right. So, the way I prefer to build my Canvas shells, and it’s the same in person or online for the general layout, is that I start off each week, beginning in week two, where they have to do the content first before we actually dive into the meat of the week. So the first thing that they have is they are given a portfolio to work from. And the way that these actually works is that they are handed a file, and this file is the thing that I have produced for each week, and it starts off with some core basics about that particular body system, and then we progress into the diseases, and then progress into more application of their work. And they’re going to take that file and not only work to fill out all of the spots that I have included in there, but they’re going to take that file and give it to this AI bot that I have built out, which is going to act as their own personalized Socratic form of a tutor to actually help them work through the entire file. So that’s the general layout with the portfolio. Once they actually go and turn that in, that actually unlocks the rest of the week’s work. So for an in-person class, this means they got to do the portfolio before they show up to class. For an online class, it means they have to do it before they can engage with the more advanced application we would be doing that week.

Rebecca: Can you talk a little bit about why you chose this approach and how this counters how a textbook is designed for a typical student?

Tim: Yeah, absolutely. I know I alluded to this briefly earlier, but when we have a course like this where in, effectively, I’ll call it 14 weeks, we’re going through every body system and covering well north of 100 different ways that we get sick, ill, and die.

Rebecca: Sounds really exciting, [LAUGHTER] all the ways you can die.[LAUGHTER]

Tim: That’s actually the way I kind of go and bring it in, was like, we’re going to go through so many things, and it’s going to convince you that nobody is normal. There’s just no possible way. But students actually really like it, and I hope not too many of them are freaked out by the time they [LAUGHTER] leave my course. So the reasons for this, I found that with a class like this, we really kind of have two options. The first one is, is that it falls into this very, what I’ll say is, traditional, classic style of a lecture class where we show up and we have to effectively spend the first day raising the bar for everybody so that day two, we can actually do some application work where we can challenge their knowledge. That is tough, because with the gambit of not only backgrounds, but also just experience levels in this class, I can tell you that any given week, if I were to do that, I’d be boring, probably about a third to half the class, because they already know about the systems, they’ve been through the courses, they work in the field, whatever it may be. So that is kind of the downside of that approach, and I’ll be honest, the textbook we had been using for this, which I think is a fantastic textbook, if… here’s the big if… if you have some anatomy and physiology under your belt. If you don’t, it’s a lot to swallow, like it is a thick textbook. It’s phenomenal, and it doesn’t adapt. It’s a textbook. So that was really the core reason why I went down this path, was I’ve been working with artificial intelligence chatbots since 2015 which was pre all of what we have now, and back then, it was a lot of work. [LAUGHTER] It was tough work to actually get them to where we wanted them to be. So I’ve been going and doing this for quite some time, and ever since openAI kind of broke the teaching world in November of 2022 it’s been really fun to start to begin to play and see where are these capacities going. And suddenly we’re at this place where we can use this tool that our students can access for free to give them this individualized approach. So my hope is going into this term, that what this will do is truly meet the student where they’re at, if they’re that student who has just one prior biology I course, no anatomy, and physiology, it’s going to spend more time going through the anatomy that they need to know to be able to comprehend the diseases and the processes that we actually care about in this class.

John: Now I have to say this, if the students do not have any anatomy and physiology under their belts, they’re probably a bot already. [LAUGHTER] But moving on, one of the things I noted in looking through some of the modules that you’ve created is that you put in suggested times. And this is an approach that I know Betsy Barre has suggested in terms of a couple of variants of student workload calculators that she’s created. And we’ll include a link to our podcast with her about that. Could you talk a little bit about why you’ve included that and how you came up with the estimates of how long students are likely to take with each of these activities?

Tim: Yeah, absolutely. So I am a big fan of providing estimates or ranges with the students. Personally, whenever I build a course or rebuild a course, I use the Wake Forest workload calculator that they have on their website. I love it. I think it’s fantastic as a way to just get a general scoping. And this also comes from… I have to think here… I believe at this point it’ll change after 5:30pm today, but at this point, I’ve taught 184 sections of courses, and this has been consistent feedback I’ve had from the students I’ve taught, is it’s really helpful. When I look at the list of things to do, if there are like numbers beside it, that even if they’re not 100% right, they give me a way to kind of calibrate my brain. And so I have some idea about how to schedule out my week. I don’t know about you, John, but our students here, just because Flagstaff, if they’re residential, it’s just an expensive place to live. They’re not only full time as a student, they’re working two or three jobs on top of that. So being able to tell, like, where should I spend my time, what should I do first, is really important to them. So the estimates in this I kind of went and derived a couple ways. The first way was I went through and tried to turn my beginner mindset back on. It’s a little bit harder when you spend a lot of time in the field, but I tried to go backwards and work through this and get a feeling for how many questions do I need to ask if I’m just that average student who maybe has done anatomy and physiology I but hasn’t gone further, which is kind of the bar I generally see as an average in this class, and then I will fully admit, I have some unpaid help, called family members, [LAUGHTER] who are more than willing to go and jump in and just like dive into it and go and tell me what worked, what didn’t work. They’re all individuals who don’t have the background, so they represent that one extreme on the far end of their really having to learn everything. So that’s the way I got this rough estimate for what I’m starting with this term. And then one of the things I’ll do is, this term, as they work through the portfolios, I am actually asking for them to report back as they go and turn into each one about approximately how much time did you spend on each one, and so that I can better calibrate this tool as I move forward. And so we’re on a grand adventure.

Rebecca: I had some activities that I did that with students, and had them kind of mark their time and track their time, and I think it was just as interesting for me as it was for them to understand how they spent their time, or where they might spend more time than they thought. I can also see how, having times broken apart, even if they’re not 100% accurate for each individual student, it does give you the idea that you’re going to spend more time on this activity than this activity. This is maybe more important, and therefore I want you to spend more time on it. And it’s a way of conveying some of that prioritization that needs to happen, that students have a difficulty doing if they don’t have expertise in the subject.

Tim: Yeah, and I’ll say as well, I know, because I have these students in class every term, and I love them, but there will be some of them that will spend way too much time on one part, it’s the kid who’s clearly gone into med school, will do great as a physician, or whatever they go and pick, and hopefully this helps them keep on track where they’re not spending five hours in the first bit that should be 15 minutes, and then they panic about the rest of the thing.

John: Yeah, those relative weights are important just in terms of showing how much priority students should place on each.

Tim: Absolutely.

John: We know that active learning approaches, as you’re planning for this course, tend to result in increased learning by students and longer-term knowledge retention and more transfer ability. Yet, when students are asked about how much they feel they’ve learned when they’ve been using active learning, they generally report that they learn more in lecture courses because they feel that they’re not getting as much because they’re struggling with things and any gaps in their knowledge is becoming transparent, while if they’re sitting in a lecture class, they can sit there and nod and smile and everything makes a lot of sense. How are you going to sell active learning to the students?

Tim: That is great. And to be frank, you bring up an item here that is one of my favorite, just bizarre oddities, that I get to explain to my classes. So I have a couple approaches on this one, the first one is on day one, which, as we record, this is a couple hours away, actually, this is one of the first things I actually go and bring up, is to explain, this is not a lecture class. We’re not doing PowerPoint slide decks the whole class. Here’s why. And I explain all these points about the fact that active learning actually does make you learn more and, more importantly, helps you develop these skills that you’ll actually need to make yourself career ready. And that’s really important, and it feels uncomfortable, because you’re going to get it exposed, you’re going to learn what you know you know and what you don’t know that you don’t know [LAUGHTER] and that can be frustrating. It can be anxiety inducing. So I think the first bit is just calling that out, bringing it up, and just acknowledging it from the start. So I think that is part one. Part two is I have built in, and this is an item I actually learned from some fellow faculty at a conference about a year and a half ago. I actually have required work at the end of each week, which is a very short self-reflection that they have to do. And it’s 10 questions, and it includes things around this area, about like, preparation for class, what’d you learn? What you feel like you had a hard time with? …all of that, and I have seen that with just even holding them, of course, the same, that inclusion, the feedback has been phenomenal in that they really enjoy being effectively forced to pause after they’re done with everything and really think about what did they do this week, and then how do I improve for next week? And then I do think the third and final bit, which I realize I might be uncorking a can of worms here on you, John and Rebecca, but I use ungrading for all of the courses I teach, and in fact, for this particular course, I use a contract grading style. And so I do think that that is one major component, is utilizing that style that supports this active learning, where they know that they’re in this effectively safe space to work in, where mistakes are expected, and I’m not as worried about the mistakes. I’m more worried about the mistake I see you make 15 times in a row and not learn. So being able to get that feedback and learn in real time is really the key. And when they feel safe about their grade, I found that they’re much more willing to take risks.

Rebecca: That’s a lot to unpack on that first day.

Tim: Yeah it Is. It’s a big first day. And really, I’ll be honest, it’s the first week. It’s not really the first day, because as much as I would love to dive into how do we diagnose disease on the first day, they haven’t done anything yet.[LAUGHTER] So we can’t do that yet. And so it’s really the first week, I should say.

Rebecca: Can you talk a little bit about the Gemini Gem that you created, that students are interacting with, maybe a little bit more about what their process looks like, how you’re going to coach students around hallucinations, what’s it allowed to do when it’s interacting with students, and what other kind of constraints and guardrails you have in place?

Tim: Absolutely. So Gemini Gems, just for those folks who may not know, this is effectively a, let’s say it’s a tailored version of Gemini that we can instruct to behave in certain ways, or we can give certain knowledge background to that allows us to make a customized chatbot without having to go through the hassle of fully training your own chatbot. So this particular Gem was a bit of a journey over the last approximately three months, where I abstractly played with a range of different models, and for my own work and myself, I work around not only the Gemini bots, but also ChatGPT and Claude, just being like our big three and occasionally Llama, but I don’t do a lot in there. So the creation of the Gem really came about when I went and sat down and said, Okay, if I were able to spend time with every single student, one on one, how would I approach tutoring them? Because that would be our dream. They would learn so much, and there’s just no way, unless I can clone myself a bunch of times. There’s just no way. And I came to kind of three broad, I’ll say, pedagogical points I wanted the tutor to do. The first one being Socratic style. So I don’t want it to just go and lecture. All of the students I’ve taught for years and years now have been very clear. They don’t like that. They want to be involved. So Socratic style. So we’re going to ask a question. I might give you a little bit of a knowledge point, and then we’re going to expand on that, but I’m going to ask you to really use your brain and engage. Point two is that we want to assess prior knowledge and learning. So does Rebecca know about cardiac anatomy and valves, or do we need to kind of back it up and maybe go and cover the basics. So when we dive into a new portfolio, that’s really the first thing it does is to go and ask you a question related to where we’re about to dive into. So again, the heart is my jam. I’ll be very honest. That’s what I do a lot of work in. And so if we’re talking about cardiac disease this week, we’re going to assess, do you actually know about the heart. How much do you know? Is this enough for us to actually talk about disease or not? And if it’s not, let’s back it up and start there, and then go for it. And then the final one is that idea of the zone of proximal development, that idea that ideally we want to be asking a question that’s they’re getting about 80% correct, and so they’re encouraged, but not overconfident, because if they’re getting 100% correct, they think they know this and they really know about half of that. So those were the three bits I began with, and from there, I built out this prompt. The things it is allowed to do, it’s allowed to act in these three ways, and it’s allowed quite a bit of freedom to be in this encouraging form of a tutor that supports work and tells the student when they’re wrong. Because I think all three of us have probably engaged with a bot, and I know that Gemini can be one of the more challenging ones, where it cheers you on, even though it’s blatantly wrong. [LAUGHTER] We don’t want that, so it is encouraged to constructively criticize, I’ll say, when the student is wrong and help them correct that. What it’s not allowed to do, though, is also really important. There’s a whole component to my system prompt that gives it a rule set of what it can’t do. It’s not allowed to just fill out the file for them. It’s not allowed to write their answer for them either. It can help critique their work that they’re going and putting in to kind of help them improve it, but it’s not allowed to just go and do that for them. Those are the guardrails in place, broadly, in this one. And then, for this particular class, I think you also asked about things like hallucinations and background knowledge. I’ll start with the latter. So background knowledge, given the fact that we’ve been studying human disease for quite some years at this point, to go and say it briefly, like at least 100, I found that when I played with this by giving it some RAG files, so some files it can pull from, it actually slowed the bot down and didn’t improve the answers. Because realistically, when we’re talking about human problems, sites like Mayo Clinic, Medscape, all these free sites are already included in the build of the AI, so it already has all that. So what it really needs is more fine tuning to make sure it’s focused on, which, again, is a guardrail, focused on accepted knowledge in this realm from reputable sources, versus trying to give it a bunch of specific files that it then has to go through and search and find what it’s looking for. Now, I will say on the flip side, I have a different course that’s exercise considerations for special populations. So this is a little bit more of my background, exercise physiology wise. How do we do activity with a cardiac patient or a cancer patient or a child who has autism spectrum level three. And in this one, I did provide files because I found that especially with a topic like autism, there’s just so much out there that it can inadvertently get onto the wrong things, that it was better to give it position statements from major fields and bodies to go and pull from to make sure it stays locked in on where I want it to go. So that’s effectively the building of this bot. I have found that I can generally use the baseline system prompt around how this bot behaves and then add in some of the details I want specific to a course to adapt it fairly quickly. Finally, hallucinations. And so I got to ask, what’s like a favorite for you that you’ve experienced around a bot hallucinating a fun thing for you.

Rebecca: I don’t know if it’s fun, but something I’ve seen a lot is hallucinated sources and students claiming that they really do exist. [LAUGHTER]

Tim: My favorite was… now, this was back in 2023, so it has improved, I will say that… But in class, asked it for a weight training plan for a adult who was 85, had never worked out in their life and just wanted to play with their grandkids, which was a example form of a client for that class. And it gave them Olympic lifts on day one. [LAUGHTER] It was like, we’re doing clean and power jerks, like, what are you doing? No, absolutely not. So hallucinations, as either or both of you may have seen, openAI released a white paper at the end of last year where they supported that currently, mathematically, hallucinations are going to happen, period. It’s not a thing that can be fully wiped out, and that’s just the reality we’re in. So what I actually do is they work through the portfolio, then their task two behind that is, by the end of the week, we are going covering that working content, I actually give them my key, and they’re in charge of actually reading through this key and going saying, were there any points where my answers weren’t quite correct? And I actually view that as a valuable skill, because we’re working on going and teaching them that hallucinations exist. Bias exists in these things as of right now. They always will. Hopefully, maybe there’s a way to wipe that out in the future. Right now, it doesn’t seem like there really is. So we’re going to not only use it for learning, but then we’re going to check ourselves against a known source to see where might it have led you astray. And I’ll be honest, that is one of the big unknowns, because, yeah, I’ve done some testing within a small group, but once we roll it out in this semester, it’s going to be north of 100 students who are going to go and do this. We’re going to see what is the actual rate of students having incorrect answers. If it becomes too great, then we have some tuning we need to do on the spot.

John: I hope that you’ve asked the bot to ignore anything coming from the CDC since January of 2025. But going back to the issue of assessing student work. You mentioned you’re using ungrading, but yet you still do have to assign grades, typically at midterm and at the end, but you do have students reflecting each week on what they’ve learned and their progress. So how are you connecting their assessments into those grades? And how often are you doing that?

Tim: Yeah, there’s a couple things I do, so starting with the smaller items in class, let’s pretend like we’re about to walk into week two. So you’ve done the week two portfolio. The moment I go and hand that in, I can immediately get to the key, which is the goal is they can go and get to it right away and see, were there any problems there? And now we’re in class. The first thing we do every week, day one in class is we start, they get into their groups that I pre assign. And to be very honest, until week six, it’s just random. I want them to work with a bunch of people in that class. So they get into these preset groups, and the first thing we do is a tRAT or team readiness assessment test. Now I’ll kind of pause here, because I know it’s a mixture here, John, I’m going seeing a nod and a smile. I feel like you’ve used this. Is that right?

John: I haven’t used it myself, but I do know how it’s used in team-based learning.

Tim: And Rebecca, is that a thing you’re aware of, or would you like me to kind of walk through like, what is a tRAT?

Rebecca: I think for our audience, we need to walk through.

Tim: Awesome. So a tRAT is probably one of my favorite tools for in-person classes, because I effectively get to trick my students into taking a quiz in class that they like taking, which is quite an experience. So this is a multiple choice quiz that you can use these cards that are pre-keyed. I will admit that I’ve actually moved to running a Google script on this. And the idea is basically they work as a small group. So I do groups of three or four, no larger than four in my classes, and what they have to do is, as a group, they go and decide, hey, for question one, we think the right choice is B, and they go and click on it, and if it’s wrong, it will go and turn red and say, try again. So they have to reason their way through and every time I’m wrong. I’m reducing the amount of points I could earn for that one. So because I use four choices in mine, if I get it right on the first thing we choose, it’s worth four points. If I don’t, it goes down to two and then one and then zero, because if you clicked on all four, you truly have no idea what was right or wrong there. Now for my classes, for this term, because I want them focused on their knowledge, and what do they know, and what do they not know? All that they have to do is report what they got, and if they’re there in class and do it, they get marked as all done with that. With that, I know I’ve heard feedback from other faculty being like, “Well, if that grade doesn’t go in the book, don’t they just not try?” And I actually go and say, like, “No, that’s actually the opposite of what I see is, they know how they did, they don’t like not doing well [LAUGHTER] in the class, especially if they spent all this time engaging.” So what they do is, I see from effectively week 2 to week 14, the grades just keep increasing, week over week over week. And what I actually do with these is, instead of just regurgitating what they just learned through the portfolio, we actually take one step more advanced, and I have 15 questions organized, usually around anywhere between four to six major conditions we’re going to cover. And we ask questions that are more advanced and more case application work, where if you don’t understand the background, you’re not going to get this right. And because it’s forcing them to do peer-to-peer engagement and reason through what is right or wrong, it requires them to really assess their own work and knowledge. So that’s a bit of it is, in class that goes and gives me a timestamp, because every single attempt I see live… that goes back to a Google sheet. So I see everything, I can see where we are right on the first attempt, where are we not. Where might we need to refresh on day two, and then the really big bulk of it is around that what happens after the tRAT? And what actually occurs in my class after that is we’ll spend that last hour of that first day and then an hour fifteen on our day two doing some quite advanced application work. So as an example, one week when we talk about pulmonary conditions, they go straight from the tRAT into learning what a SOAP note is. So, for those who don’t know, this is a way that we write a note in more of the clinical medical field. So they learn about this way of writing notes, and then they, as a team, actually work with a bot that I will assign them that acts as a patient, and they have to produce a SOAP note for that that they then have to present to the class on day two. So it’s all, can they pull out the right information? I f they think it’s problem A, they think it’s pneumonia, what other questions do you need to ask about that? And that’s our weekly work. And all of this at the really end of the term leads up to the culminating project in our class, which is three parts. For this class, this is where I’m really assessing them on what do they know? And they can revise and improve. They pick a disease that exists today. So as much as smallpox is fascinating, it doesn’t exist, except for in labs and hopefully never out in our globe again. And so they pick a thing that they find intriguing. The first bit is they have to do research and write me a historical overview of this disease and how it has impacted our health over time, including, how has it affected our clinical past, how has it affected our public health past, our policies, all of that stuff. So it’s this really overview, big scale idea. Number two, they go and take what they just went and wrote there and turn it into fact sheets. Now a fact sheet, I guarantee both of you have seen before, because if you’ve been into a physician, they have those things like: “Get the flu shot, now. Here’s why.” That’s a fact sheet. So they have to write one about their thing that they picked for a general adult, one for a physician, and one for a person who might be in Congress and might be making policy. So it requires them to take this knowledge and turn it and go and say, “What does this general adult need to know? What does maybe a physician need? What does a policy maker need?” And then the last of it is, and we’re ramping up into this, is they’ll actually give a presentation in week 15 of our term, where they have to propose a potential public health program that would help within a certain group of people for this thing and improve their health, which requires them to really thoroughly not only understand the disease, the processes behind but also the populations affected. How might we reach those groups? How do we help them, like, what are they lacking? And then, how would we actually go and reach them? And for this group, that’s usually their first time they’ve ever done this, so that’s a big ask, but they have the knowledge they can do it successfully on that. So really, a lot of it is around the projects, but everything builds up to those projects.

Rebecca: So it sounds like, with all the project-based learning that you have in here, you’ve certainly been thinking a bit about student use of AI and its potentials for cognitive offloading and academic integrity that we know come up in many conversations around AI. Can you talk a little bit more about the reasons why you chose some things and how that might relate to academic integrity issues?

Tim: Absolutely. So I think this is the core of a conversation we could have for hours on here. I have students every term who it’s very clear that a paper and assignment was written by AI. I don’t think that’s going away anytime soon. And [LAUGHTER]] the reality as well is there was, and I would have to go find it again. But there’s been quite a bit of work that’s shown that the majority of employers are expecting them to walk out of a college with some generative AI skills. So we need to be going and teaching this. And part of that is we need exposure. So for myself, I think there’s a couple parts to this question. The cognitive offloading is an item that, again, I do address in week one or week two, just kind of, depending on how week one rolls about just having kind of a very open discussion at the start of a class for about five minutes. About these are the risks if you, for example, try to acquire foundational knowledge and you just offload that to the AI writing the assignment for me. These are the risks that are run, and here’s why I don’t want you to do that. This bot that I am getting you to work with is specifically designed to force you to learn and think and then actively engage in this to help prevent that. So that’s the first bit. The second bit as well is we do actually go and talk about throughout a class like this, we go into a little bit, and I will fold this in maybe the first five minutes, kind of threaded throughout the term, we go and talk about more of this ethical or philosophical component with AI usage, because it goes beyond just things like power and water that is used for going and training these bots. And we do go and talk about that, but we’re beyond that here, and we go and talk about things like, “Well, what is the purpose of learning? When do we use these as an enhancement versus using it in a way that’s going to go and detriment your learning and your skill development? How do we avoid that?” And I’ll be honest, this is not the first term I’ve gone and spoken about these. I’ve done this for a couple terms now. And the feedback I’ve heard, which I suspect both of you will have heard if you have gone and done this before, is that students actually really appreciate it, because a lot of our kids, especially right now, they were in high school during the rapid pandemic lockdown era, and then maybe hybrid, and then maybe [LAUGHTER] back into lockdown. And what I’ve heard from them is they were threatened, and they were told by a lot of the folks in high school, and then moving into being freshman and sophomore, “If you use this, I’ll fail you out of the class, you’ll get expelled.” And so they’re afraid to admit any type of use. And nobody’s talked with them about, how do we develop this as a tool for your toolkit versus replacing your brain. And once we actually get into that, then we can actually explore this region in a much more effective way. So I think we need to have an open talk about this, and then as we build out parts of the class providing the guidance they need. So for example, for the paper that we just spoke about, that historical paper, they’re allowed to use an AI, and I have this built right into that. I’m fine with them if they want to go and use it for brainstorming, for helping them outline the paper, feedback on their own work, feedback on the paper. And I want to hear their voice. I want to hear their writing. So it’s not allowed to write the paper for them. I want them to write it. So I think it’s both the education and then being very clear about what you want them to do or not do, and then why.

John: And given the nature of the assignment, they’re already seeing how AI can be useful in assisting them in various ways that can carry on into their future careers. And the ungrading aspect of it also takes a lot of that pressure of grades off, allowing them to focus on learning. So, those are approaches I’ve recommended to many people trying to do these things. If we can show students how AI can be useful, and if maybe we shift their focus from getting the highest grade to learning the most, it can have an impact.

Tim: Yeah, well, and that, and I’ll go throw out a third one here, building them a box and allowing them the freedom to explore in that box. So I’ve told them what parts they need in the paper, but I’m allowing them to pick a disease that they actually want to research out, and that is a huge help, because when they want to do the work, I’ve had some phenomenal work done where I read it, and I’m like, man, you’re a couple reviews away from being able to publish this as like an undergraduate, like this is phenomenal.

John: That can really tap into intrinsic motivation. I know when I give students freedom in the choice of their projects or topics, they get so much more engaged than if they’re given a list of things they have to do.

Tim: Yeah, totally.

Rebecca: There’s a lot of steps week to week. There’s a lot of things that students need to do. In my experience, people don’t always read all the instructions [LAUGHTER] and maybe don’t follow through in the ways that we’re expecting them to. How are you hoping to prevent some of this? Especially because there’s a lot of steps in “this unlocks that” that can prevent someone from making progress during the week.

Tim: Yeah. So first one is, I have, unsurprisingly here, I’ll say I do have very clear instructions on like, what to do each week. And as you just said, some students just don’t read that. And the first thing that they do is, “oh my goodness, I can’t see the week,” email the professor. So the first bit is, I have learned how much instruction is enough versus how much is too much, because if it’s just pages and pages, they just won’t read it. So how to be succinct there? Part two is, for example, for the first portfolio, we’re going to actually start that in class. So that’s our day. We’re going to actually spend about half the class, and we’re going to sit there and work on it in class. So I’m available right then and there, and I make sure that anytime we have something new happening in a course, and this is the exact same in person or in an online course, is that I, at the start of class, I’m very, very clear both of the days about this is exactly what you need to do. And yes, that will not catch 100%. I still get a student or two out of 40 who are like, “Oh my goodness, it won’t open up. What am I doing wrong?” And I just have to go, remember to turn in your portfolio and that will unlock over the weekend they’re like, “Oh yeah, I recall that now.” So that has been kind of the path I found that works well, is to introduce complex things in class, including the projects, allow some free time where I can help them immediately, and then follow up with regular reminders of what to do.

John: Northern Arizona University, as Oswego does, has a site license for the use of Gemini. Would this work as well if all students didn’t have the same equitable access to a platform?

Tim: Yeah, that’s a big question. And in fact, I’ll say to throw a complexity here, we not only have the Gemini, we’re a Google, and we also have Copilot. I have no idea why, but we do. [LAUGHTER] So, we have lots of fun stuff. Without that, I think that even if you were to use the Gemini free version, that still gives you sufficient usage and space to be able to do a thing like this. The big asterisks I throw on there is making sure that we’re 100% transparent with our students about the fact of, “Hey, we don’t have a license. We’re going to do this. We’re going to do it through the Gemini bot, because the context window… for those of you who maybe that’s a new phrase, that’s basically the memory like in quotes for the bot… is gigantic, and the usage terms for the free tier are quite large enough to do this task. That anything you put into this bot, they could use it to train so don’t put anything that’s personal information into this bot. And I think that is a very good thing that we just need to teach in general, because there are a lot of students I teach who, one, they haven’t been told that they actually have this Gemini license for Ed that we pay for and that it’s all private. They’re going and using the ChatGPT free version, and they have no idea that they’re training on their data, unless they go to, like, flip off like a switch. And so that’s the first one, is we need to go and explain that, because there are students who, once they go and learn that are like, “oh my goodness, I put some very private stuff into here. I had no idea.” And then also thinking about the use in the class, for this class, for this purpose, we’re not asking them to reflect on anything. It’s straight disease and some biology learning. There’s nothing there that should put any personal information at risk. So I think it is 100% doable with the free license if I don’t have it. I think you just have to pick your bot wisely, because I’m a Claude person. I love Claude, and their free tier is very restrictive on the amount of use you get each day, so that’s not a good pick for this.

Rebecca: So we mentioned at the top some of the other ways that you’re using AI in your research. Can you talk a little bit about what that work looks like?

Tim: Yeah, I’ll try to be brief here. So I am a researcher on what’s called the ASD3 project, which is a joint project between the Oregon Health and Science U. and our institution. We have a PI here that I work under, and under that I work on the machine learning and deep learning, I’ll say, arm of the project. This is a multi-million dollar piece of work that looks at better understanding the autism experience for children, specifically around the disparities in the diagnosis and access of care. So a lot of data work. We work on several big code sets, so my work is mainly around using these advanced models to better understand where are these present, and how do we address them? So that’s that work. On the tribal health part of it. This is actually what I am planning on doing for my own doctoral research, is around trying to expand the availability of, specifically, heart care. So the cardiac rehab that I am trained in working on to individuals in a tribal space. The reason for this is that, based on some research we have, not only is heart health a major health burden within this space, but the relative risk of death from a heart-related problem in American Indians is somewhere around 53% higher than the average white adult’s, which is shockingly high. It’s the highest of any group. So, within this space, we also know that rehab is a thing that if we can get people into it, it dramatically drops the rate of a first event being like a heart attack, or if they’ve had their first one, their next event. But the problem is this, this is usually done in a hospital. And if you look at Navajo Nation, this is an area the size of West Virginia, they only have 13 hospitals total in that entire space. It’s very difficult to get to this, especially if you’re working full time, especially if you happen to be a farmer or you ranch or do both. So it can just be non feasible. So my work is focused around developing the foundational knowledge that I’ll take forward after I’m done, into the actual building of a generative AI that can supply this as a service within this space, and that is based on a very engaged approach, where we are working with folks from this space to help drive what does this AI look like? How does it engage? What do we do with cultural adaptation work? Because this is not my lived experience. It’s where I have knowledge, but it’s not my experience. It’s a different form of, a culture, a background, than what I am. So I am working on working with the tribes to be able to better drive what does this AI look like that we eventually build? How does it engage? And so that we’re not trying to walk in and propose a thing that doesn’t actually work because we don’t know what needs to be done yet. And so that’s, briefly speaking, the forms of AI that I do research on.

John: Some very valuable areas of research.

Tim: It’s exciting and frustrating at the same time. [LAUGHTER]

John: So we always end with the question, what’s next?

Tim: Yeah, in two hours, I’m going to go and see how this learning portfolio lands. That’s really what’s next for me is in two hours, we’re gonna dive into this for the first time for the entire class, and it’s no longer a trial. It is on the ground running. And yeah, I would love to do like a follow up, maybe in the summer or the fall, and we’ll actually have some figures and data and student feedback on how did the portfolios go? How did they change the class?

John: That would be great, and we’re looking forward to that conversation, so we’ll keep in touch.

Tim: Yeah, for sure. And I would just like to say, I do appreciate the time to chat, because I’m very excited about what we could do with AI in our space,

Rebecca: And it sounds like you’re finding ways to use it in all your spaces.

Tim: Absolutely.

Rebecca: So thanks for joining us. We appreciate your time and expertise.

Tim: And thank you, both of you.

John: 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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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.

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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 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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427. Open Exchanges in College Classrooms

Ideally, college classrooms provide students with a comfortable but challenging environment in which diverse ideas and viewpoints are openly exchanged; the reality they experience, though, is often quite different. In this episode, David Laibson joins us to discuss how Harvard University is attempting to identify and address barriers to this ideal.

David is the Robert I. Goldman Professor of Economics and a Faculty Dean of Lowell House. He has published dozens of heavily cited articles on a wide range of topics, including behavioral economics, self-regulation, behavior change, household finance, and aging. David is a Research Associate in the Aging, Asset Pricing, and Economic Fluctuations Working Group at the National Bureau of Economic Research, member of the National Bureau of Economic Research, and serves on numerous advisory boards. He has received Harvard’s Phi Beta Kappa award and a Harvard College Professorship in recognition of his high quality teaching. David is also a co-author of popular textbooks on introductory economics and  a co-editor of the Handbook of Behavioral Economics.

Show Notes

Transcript

John: Ideally, college classrooms provide students with a comfortable but challenging environment in which diverse ideas and viewpoints are openly exchanged; the reality they experience, though, is often quite different. In this episode, we discuss how one institution is attempting to identify and address barriers to this ideal.

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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 David Laibson. David is the Robert I. Goldman Professor of Economics and a Faculty Dean of Lowell House. He has published dozens of heavily cited articles on a wide range of topics, including behavioral economics, self-regulation, behavior change, household finance, and aging. David is a Research Associate in the Aging, Asset Pricing, and Economic Fluctuations Working Group at the National Bureau of Economic Research, member of the National Bureau of Economic Research, and serves on numerous advisory boards. He has received Harvard’s Phi Beta Kappa award and a Harvard College Professorship in recognition of his high quality teaching. David is also a co-author of popular textbooks on introductory economics and a co-editor of the Handbook of Behavioral Economics. It’s really an impressive list of things there. Welcome David.

David: Thank you. Glad to be here.

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

David: Well, I don’t actually have my tea cup with me right now, but I do have my preferred tea on hand [LAUGHTER] because I knew you would ask. So my favorite tea right now is a Korean matcha called Osulloc, and it comes in a very handy format where I can basically take my matcha tea bag, drop it in my water and have my lovely cup of tea, but I don’t honestly have one right now. [LAUGHTER] So, I thought you were gonna ask about what I like to drink, as opposed to what I’m drinking.

John: And I am drinking a Twinings oolong tea today.

Rebecca: Oh, nice. Must be the end of the semester is getting a little intense with the black teas, huh?

John: It is. [LAUGHTER] I think this is my third or fourth cup today. [LAUGHTER]

Rebecca: I have a decaf Irish breakfast this afternoon.

John: That’s the difference being in the administration. [LAUGHTER]

Rebecca: I have about a week, have about a week. [LAUGHTER]

David: Well, good luck reaching the endline.

John: So, we’ve invited you here today to discuss your work as co-chair of the Classroom Social Compact Committee, which we had read about in an October 6 article in The New York Times. Can you tell us a little bit about this committee?

David: Sure, the committee did most of its work in 2024 and issued its findings report in 2025, early in the year. Maya Jasanoff, another faculty member here at Harvard, is the co-chair of the committee, and our report was both a description of the state of affairs here at Harvard and a set of recommendations for addressing many of the problems that we uncovered or documented. The committee was charged by the Faculty of Arts and Sciences Dean, Hopi Hoekstra, and the charge, I won’t read the whole thing, but it included describing the nature and purpose of the faculty of arts and science classroom and developing practical recommendations for engendering a vibrant learning environment in the classroom.

Rebecca: Can you talk a little bit about some of the primary issues that were raised in the report?

David: So, there’s a long standing recognition that the classrooms here have a aspirational role to play, that perhaps we’re falling short on on many dimensions, and let me review some of them that were discussed and documented in the course of writing our report. So, one thing that we’re concerned about is the degree to which students come to the classroom not completely comfortable sharing what they think when they raise their hand, if they raise their hand. There’s a fair degree of self-censorship, an extent to which people, for various reasons, may not feel comfortable speaking up. It may be that they’re shy and don’t want to say something that might be wrong or that they don’t want to say something that may be viewed critically by other students. And so one aspect of the classroom that is problematic is the degree to which people are not comfortable bringing authentic voices into the classroom setting. Another significant issue is the degree to which our students have, in some cases, focused energies outside the classroom, to the extent that it actually undermines the academic center of their lives here. Now I don’t want to imply that that’s the fault of our students. I think in many cases, that’s a reality that our faculty are not creating classroom experiences that are sufficiently stimulating and therefore engaging our students’ interest. And so I think the un-centering of the classroom, which has been occurring for decades here, is a two-sided phenomenon. On the one hand, it’s students that are focused elsewhere. On the other hand, it’s faculty that may not be creating the most stimulating and exciting environment intellectually in the classroom. And I think we’ve all agreed, unfortunately, that that’s okay, and this report is part of reversing that consensus and reasserting the principles that I think we would agree with, but haven’t always been our practice here at Harvard.

John: And I think what you’re describing is something that we’ve seen at pretty much all institutions, to some extent. But one of the things that really impressed me about the report was the extent to which you gathered data and the ways in which you gathered data, because that could be a useful model for other institutions as well. Could you talk a little bit about what types of data you gathered and the process of assembling that?

David: So we did a year of data gathering, and as you point out, it was pretty aggressive and extensive. We wanted to make sure that we really had our finger on the pulse of the many different communities and the many different stakeholders, so we held well over 30 group listening sessions, and then I would say, hundreds of one-on-one listening sessions, where you sit down with a student and have a one- hour lunch and get a full download from them in the most confidential setting, where it’s just a one-on-one conversation, as opposed to someone volunteering an opinion with 40 other people in the room, which can be a little daunting. We also consulted with colleagues and consulted with many different committees that sit here in the Faculty of Arts and Sciences that oversee our curriculum. So the Educational Policy Committee, the Graduate Policy Committee, the General Education Committee, a place called The Bok center, that is a sort of mentorship for teachers unit, and we talked with department chairs. We talked with divisional deans. So we really went out there and gathered a lot of data. In addition, we looked at 11 different surveys that were fielded either by us or by other groups inside Harvard, measuring undergraduate opinions, graduate opinions, faculty opinions, alumni opinions, and that is the maximally confidential space for people to give answers, because a lot of these surveys are done without any ability to identify the respondent other than knowing that they are, in fact, a Harvard affiliate, because they got into the survey using a Harvard ID. So we looked at five undergraduate surveys, three graduate student surveys, one faculty survey and two alumni surveys, and by that point, we had a lot of data that would enable us to, I think, have a fairly precise understanding of at least what the community thought and what they were willing to share with us in these different formats.

Rebecca: So, you mentioned the self-censorship issue that bubbled up across all of these different audiences that you surveyed and collected data from. What are some of the consequences in terms of student intellectual development?

David: So there’s a lot of adverse consequences. One is the obvious problem that students don’t feel, in many cases, that they belong, because their perspective is not welcome, and that, I think, undermines their ability to thrive. They’re also not developing the critical skill of learning how to speak publicly. If they’re constantly suppressing their voice in these public settings, and the classroom setting, then that’s a skill that isn’t being nurtured. I think perhaps one of the most important problems with a classroom where people don’t feel that they can say what they really think is that there’s this false impression of consensus when in fact there are enormously divergent viewpoints. They’re just invisible because most people that are speaking are speaking in a relatively narrow domain with respect to maybe the most sensitive issues. So when you have a classroom that is a place of self-censorship, and I want to emphasize many classrooms don’t have that problem. But when you have a classroom where people are self- censoring, you get a false impression of homogeneity. You get a set of skills that are not being developed. You get many students feeling that they’re just there on a performative basis, and it undermines the whole intellectual enterprise. We’re here to learn and really compare ideas while all that’s out the window when you have the impression, and again, this is only in some classrooms, that there’s a kind of acceptable viewpoint, and anything that sits outside that viewpoint you better keep quiet about. But I want to emphasize, a lot of people think that the way the law is laid down is through faculty and graduate student teaching fellows. Indeed, the students told us that mostly they were concerned about what their fellow students thought. So, if anyone has the impression [LAUGHTER] that this is a sort of monoculture problem that is driven by or primarily by instructors, I want to, right from the start, clarify that overwhelmingly, the students said that in many cases, it was fellow students and their judgments and what would happen after class in the dining halls that they were most concerned about with respect to saying something that might be unpopular in class, and not the bad grade they might get. Indeed, I was quite happy to hear that the faculty were the people that the students were the least concerned about. So I think there’s more concern about some of the graduate students that are grading their papers in a big class, but especially there’s concern about their fellow students. Now that’s not to say that the faculty don’t have any role to play in changing this equilibrium. Definitely, the faculty need to change norms and need to lead with their voice this new approach to a classroom where more perspectives are sought out and encouraged and welcomed, but it is, I think, important to emphasize that it’s not just the faculty that are contributing to self- censorship, it is fear of what other students are going to say in the dining hall after class.

John: One of the more striking statistics there was that in one of the surveys, 33% of Harvard’s college students reported that they felt free to express personal feelings and belief, as you’ve noted, a lot of students are self-censoring. However, there were some other statistics based on individual course evaluations that suggested a much lower rate. How was that explained in the report, that discrepancy between the reports from seniors and the individual course summaries.

David: Yeah. So just to highlight the tension, if you ask people about their overall academic experience, only a third say they were comfortable speaking openly, but when you ask them about the individual course that they just took, so ask them about one course, and you get about 90% saying that they’re comfortable speaking openly. So clearly, what we’re learning here is that this is not a problem that people are experiencing everywhere, all the time. It’s a problem they’re experiencing in some places. So when you ask people, as they’re leaving after four years, what was it like? Did you feel the need to self-censor? They say, “Yeah, sometimes.” But when you ask them about the class they took last semester on 19th century history of Brazil, they say, “No, actually, in that class, I did not feel the need to self-censor.” So, the distinction, I think that’s important to highlight, is that it’s not a problem that’s occurring all the time for our students. It’s a problem that’s occurring frequently enough that it’s a real concern, but not so frequently that it’s affecting every curricular experience. And I think that speaks to a couple issues. First, the problem is somewhat contained. But secondly, there probably is a degree of intellectual self- segregation that is contributing to the sense of, “Well, it’s not a problem in my class.” Sometimes I ask students, “Well, what happened in your class? What was the curriculum?” And they’ll describe a relatively homogeneous student body in that class. And then I’m not surprised that if the classes has advertised itself as having a particular viewpoint, and the students get that message early on, and the students that want that viewpoint stay, and the students that don’t want that viewpoint move on, you can easily see how people might say, “Yeah, in that class that I took, I had no trouble saying what was on my mind.” But then one realizes that in parentheses, that’s because the class ended up attracting a relatively homogeneous set of perspectives in the student body. And so I do think all of these considerations make it challenging to know exactly how deep the problem is. I definitely think we can over-exaggerate it if we just cite the 33% number of people that feel comfortable speaking their mind. But I think we can also kind of over minimize it [LAUGHTER] by saying if you look at individual courses, 90% of the students say that in the particular course they are evaluating right now, they feel free to speak their mind. We don’t know how much of that is a kind of self- segregation into curricular environments that they feel comfortable in.

Rebecca: What are some strategies, maybe both structural or also within a class, to work against this?

David: Well, I think you start with awareness as an instructor, I think instructors don’t realize the degree to which their students feel pressure from other students to accept a kind of student-endorsed viewpoint. And I think faculty are trying very hard to have real conversations in their classrooms, and are often working to prevent their own viewpoint from becoming, in some sense, the only acceptable viewpoint. They’re not necessarily jumping out on day one and saying, “This is what I think,” they’re creating space for student perspectives. They’re working hard on that front, but they don’t realize that it’s not just that students are trying to glom on to what the faculty member thinks, it’s what the other students think that is a real issue. And so I think faculty need to be explicit in saying, “Hey, in this classroom environment, we really encourage diverse perspectives, and I’m going to be thoughtful in drawing them out.” And so I’ll give you examples of how we do that in my class. You probably have both have the experience of that wonderful student who is always quick to get their hand up right away, and if we always call the students who get their hands up in the first five seconds when we ask a question, or when we open it up for questions, we tend to get a relatively homogeneous set of answers because it’s the same students that we’re privileging. They’re the students who get their hands up the quickest. And if we call them, once there are a few hands up, well, we’re going to over sample the same students. And so one thing that we do in my classes, we, by that, I mean me and my co instructors… we try to let the question settle and not pick someone immediately. And I have something called the 10-second rule, where once I ask a question, I’ll count to 10 in my head, quietly, and only then, when I’ve allowed 10 seconds to pass, do I finally start picking from the hands that are up. And at that point there’s a lot of hands up, and I have my pick of students, and I can go for students that have not been contributing so far in that lecture, or so far, even in that semester. So I love the ability to bring in this far more diverse set of student voices by just giving them time to get a question together. And I think there’s lots of ways that faculty can launch a more diverse conversation by not allowing a small set of voices to become the dominant voices, and by looking for opinions that lean a different way than the first opinion you got. So you can ask a question, and some student says, “Well, I think A,” and you can ask, “Well, who thinks not A?” And then you can draw in the range of opinions, rather than allowing the first student to say, “I think A” and then the second student to say, “I think A too,” and then the third student “I think A three,” and everyone thinks a. And that’s pretty much the herding effect you don’t want. And you can be very intentional in creating an anti-herding effect by only asking for questions or perspectives that create the heterogeneity that you’re trying to engender, rather than allowing students to herd on the first answer that comes out of the room. So I think there’s a range of strategies, and they had to be launched on day one to get the equilibrium right in the classroom. Students need to hear the message early on and hear it repeatedly, that this is a place where we’re looking to hear from different perspectives, and we’re not trying to all agree on something. I am very disappointed when I hear [LAUGHTER] faculty telling me about how much agreement there is in their classroom and how excited they are by that. I think when everyone’s agreeing on something, it’s probably a mundane thing, or they’re observing false agreement. There’s enormous disagreement among our students, and if you know how to draw it out, you’ll get it. And if you get everyone to agree together that something is true, well, I ask you, what does everyone agree on that isn’t something that’s utterly trivial.

John: One of the things I’ve done in my classes is I do a lot of polling, but also I have a Catchbox, one of those little cubes with a microphone in it. And when I’m asking questions where there is likely to get a lot of diversity, I’ll wander up to the back of the room because that’s where the students are most likely to sit who are least likely to volunteer and either hand it to them or toss it down the aisle a bit, because the same students always sit up front with their hands raised all the time, and students in the back, at least in my class, are much less likely to ever raise their hand, no matter how many other people have done so. I think one of the things you recommend perhaps using the Bok Center to have people work on alternative teaching strategies to draw out some of these things.

David: Yeah, we do some of the things that you just described. We also sometimes kind of insist on questions from the back of the room. And we also have something called a pre-lecture activity where we get everyone to anonymously answer polls or play games or do something that we’ve created before the lecture. And it’s amazing how much variation you get on these pre- lecture activities. And then everyone in class is astounded when we show them the histograms of responses, and they realize, “Wow, I thought everyone agreed with me and now I realize half the class has a totally different viewpoint.” And it’s not just now a hand or two, they’re seeing data that represents the entire distribution of people in the class, and everyone says that’s the thing they remember the most from the semester, seeing their own data and realizing how different their own perspective is from that of everyone else in the class. And it’s an eye opener.

Rebecca: One of the other things you mentioned earlier was the self selection into classes. Do you have some strategies to encourage folks to explore more things? [LAUGHTER]

David: Yeah, so certainly that’s advice that I give all my students. I’m not sure that that’s [LAUGHTER] the best way to get that advice to be accepted, just sharing it. I certainly take the view that if you are taking a class that’s just going to tell you what you already believe about the world, you’re less likely to be learning than by taking a class that might challenge your preconceptions. That said, I think one thing that instructors should do, in my view… of course, they have academic freedom, so they’re free to decide whether they want to do this… but I would encourage it as good pedagogy, is to offer a range of perspectives in a class and to, themselves, offer the alternative view on an issue, as opposed to the view that is the maybe main thrust of the course. Indeed, I do this myself, I’ll give students the range of evidence on an issue, and I’ll say some scholars think this, and here’s why they think this, and some scholars think this, and here’s why they think this, and here’s the body of evidence that we have, and you can see all the different empirical estimates that have been generated in scholarship in the last 15 years, and so we’re going to be very candid with you about the limitations of what we know and how scholarship has yielded different answers, rather than picking the study that I most like, that is most aligned with my personal viewpoint and teaching it that way. I think good pedagogy goes hand in hand with acknowledging the range of viewpoints that exist in the scholarly community and being forthright that there isn’t a single viewpoint that happens to match my own that is the right approach. And we even have a lecture in the introductory economics class on epistemics, where we talk about the limits of our knowledge and all the ways in which our own views have evolved as we’ve professionally been exposed to more data, seen new research, changed our minds, and so we try to model for our students this concept that there isn’t a set of truths carved on stone that we’re going to hand them. And I would recommend to colleagues that they teach their classes that way. And I think students should also think about learning that way. If they think that they have already discovered the truths carved in stone at age 19, [LAUGHTER] well, they’re in for a big surprise about how the world works. They’re never going to find those truths carved in stone, and they certainly don’t have them already at age 19. So I think we need to message that more in our classrooms.

John: One of the things you’ve mentioned is that students don’t always feel free to express their opinions, but there was also in the report a discussion of student motivation, student engagement, and the relative emphasis that students put on the classes versus the social life and extramural activities. In the report, there’s a number of recommendations, one of which you’ve already discussed in terms of changing how we teach in the classroom. But what are some of the other recommendations of the committee relating to these two issues in terms of getting more students engaged in class and putting more of an emphasis on class, as well as perhaps being more free to express their thoughts in class.

David: I don’t want to create too strong an impression of certitude here we are saying in the report that recentering the classroom, recentering academics, is an important goal going forward, and indeed, we’re already making progress this year where I’m seeing tangible changes in this current academic year, which is really giving me a lot of encouragement That said, the extracurricular life is rich and serves many purposes. So we’re not trying to convey the message that all of that should be shunted aside and people should focus exclusively on their academic lives. I think it’s about finding the right balance, and for some students they’re already well balanced, and for other students, maybe they’re unbalanced with a search for courses that ask very little of them. They’re called gems here at Harvard, g-e-m, and that means low work, high grade, and so maybe not so many gems should be offered, and maybe students shouldn’t seek out a course that is called a gem, and if people feel that they’re so busy out of class extracurricularly and they’re searching for gems, that that’s a sign that maybe the balance hasn’t quite been found for that student. So I think that we should, as a community, ask ourselves, “Well, what courses are famous for having a low lift of work and a very high grade distribution?” and then sort of ask, “Is that the sort of course that we want to be offering?” And that means that for that kind of curriculum, maybe we ask more of our students. Maybe the readings are more demanding, maybe the grading is tougher. So I think that there’s some suggestion to the community of instructors that we might think about raising our standards. And there, we’re thinking about what we ask our students to do out of class, what we ask our students to do even in class. So there’s the cell phone that’s inevitably taking the student’s attention away from what’s happening in the classroom and then 75 minutes have gone by, and you ask the student as they’re leaving the room about what just happened, and because they were parallel processing through the whole thing, they actually didn’t get very much out of it, and 75 minutes were spent kind of scrolling some social media app, and the student might think they got a lot out of that 75 minutes, but in fact, it’s very hard to do two things mentally at the same time. So I think we need to create more stimulating classrooms, more demanding classrooms with respect to assignments, we need to not create a classroom where there are so many A’s that the student can essentially only distinguish themselves by joining the consulting club outside of class and putting all their energy into that. So I think that there’s a lot to be done by instructors in creating a more exciting classroom, a classroom where excellence is acknowledged with higher grades, and it’s not that the norm is an A. I think students might also ask themselves, is the best use of their four years here to focus on things that are happening outside of our classes rather than inside our classes. And if the answer from the students is, well, actually, it’s better outside the class, then I think the faculty need to double down on making the in-classroom experience better. So a lot for us to think about as teachers, and our obligation to create an exciting curriculum that makes students thrilled to come to class. That should really happen. I mean, students should not come to class grudgingly. They should come to class with excitement because something interesting is going to happen, because there’ll be a conversation that will be memorable, because they’ll have an insight that they’ll take with them for the rest of their lives. I mean, classrooms are some of the most remarkable, extraordinary experiences one can have in a lifetime. And if it’s not that, then it’s on us faculty to up our game and create these experiences that are so memorable, that are so transformative, that are so important in the development of our students, that students are actually eager to be there and are not going only because attendance is being taken. Though I might take attendance if that’s a problem too, but for sure, the goal is a classroom that is a wondrous experience, that is the highlight of one’s life, that is sad as you leave it behind and graduate from college. Graduating from college should be, on one part, an exciting moment to move on with your life, and on the other hand, a very sad moment when you’re leaving behind this incredible opportunity to sample intellectually four courses that are life changing every semester. Well, let’s create those courses together.

Rebecca: I think a life changing course, seems like some faculty might have some imposter syndrome [LAUGHTER] around being able to achieve that. Can you talk a little bit more about some of the strategies in the class to make that engaging experience where students maybe don’t want to interact with their phones and they want to fully participate in class.

David: Well, I think students, by and large, do want to participate in class. I think the phone is sort of like a drug and everyone says, “Well, I don’t want to be a heroin addict in five years. I’m going to quit between now and then.” And of course, it’s very hard to quit the addiction. I think phones have a lot of those addictive properties. You enter the classroom thinking, “Gosh, I really need to focus.” I mean, this is my own experience as a student. There weren’t cell phones, but there were plenty of things that one could let one’s mind wander to. So I always enter the classroom intending to take good notes and really focus and not get distracted and learn intensively, and often I found myself drifting off. So we’re all easily distracted, and the cell phone is easy distraction on steroids. So I think students want to learn, and I think we need to create environments that facilitate that learning and make it easy. There’s so many classes that I’ve attended where literally, I was on the edge of my seat through the whole thing and excited because there was something important being said, some conversation with students, among students, between students and faculty, that kept me engaged. And I think students want that. I don’t think they are eager to spend these four years blowing off their courses. I think that is something that happens as a unintended consequence of all the other things they’re doing, like the conversation with a roommate that starts at 11pm and doesn’t end till 3am and after four hours of debating every single philosophical issue under the sun, you finally go to bed, and then, for your 9 am class, you’re very sleepy, and you get to the 9 am class having not gotten enough sleep, and you’re barely awake. That student was excited to be debating philosophy at two o’clock in the morning, and unfortunately, got to class in a drowsy state because of it. They’re not intellectually unengaged, but they’re still not going to really thrive in that classroom because of the sleeping that they didn’t get. So my sense is that students want to learn, and we just need to help create environments where it’s conducive to learning, where being in class isn’t going to put you to sleep, it’s actually going to wake you up, because so many exciting things are happening with respect to what is being said, debates that are taking place in real time, opportunities to ask questions, moments where we’re all on the edge of our seat because a student is actually pushing back on a faculty member on some issue and there’s a interesting dialog happening, maybe students also interacting with each other on some issue that is being discussed in the class, and so there’s a conversation, there’s issues that are surprises, there’s intellectual turns that were unexpected, there’s data that’s surprising, there’s ideas that are engaging. I mean, these are all aligned with what our students want. And I think the problem is often they come to class, and actually it’s a pretty boring lecture, and that’s not their mistake, that’s ours.

John: And along the lines of mobile devices, one of the things recommended in the policy is that mobile devices be either banned or discouraged, except in the cases of documented disabilities or when they’re being used actively for some component to the class. And in your class, the article mentioned that you had a device- free zone, but many people chose not to sit there. How can we encourage students, perhaps, to not use mobile devices?

David: I want to be clear, the policy in our class is no cell phones anywhere, so that’s a universal ban across the entire classroom, but we do allow students to use laptops and to use iPads if they want to. Though we also have a iPad and laptop free zone, I struggle with the question of laptop and iPad use, because for many students, they, I think, legitimately view it as an aid for learning, just a regular academic experience, I like to take my notes on an iPad, for example, as opposed to any need to use an iPad. So, I’m open to students doing that. I do warn them that it’s a slippery slope. Once you have the laptop open, you’re one click away from all the distractions and temptations of the internet, and that will really undermine your teaching. And what we do in our class is we actually hand out photocopies of the slides so they can commit to keep their laptop closed by simply using our photocopies and taking notes with a pen on our photocopies. And most of our students do that. That said, I think there are students that want the hybrid experience, and probably some who are taking advantage of being in a laptop allowed zone or iPad allowed zone to occasionally check out and check into their email or everything else that’s happening. So I’m torn about exactly how to handle the classroom environment. I’m open to professors that are going to do a complete ban. I think that’s fine. Obviously there’s accommodations, and we, I think, need to be mindful of that, but a complete ban outside of accommodations. But I’m also open to saying, “Hey, these young people need to learn how to develop this skill of living in a distracting world, and maybe part of developing that skill is realizing that after 75 minutes in the classroom, they have gotten nothing out of a lecture because they found themselves doing one thing after another on their laptop.” And so there is an upside to the distraction, which is a little bit of growing self awareness, which emerges as people begin to discover that we think pretty serially and cannot think consciously on parallel streams, and consequently that skill set of learning how to operate in this distracting world is developed in a room that allows them to use these electronic devices, though I think the cell phone is just so obviously destructive that I’m happy to nix that.

Rebecca: The policies that completely ban all devices except for accommodations… are we kind of skirting or dancing around issues of privacy when it comes to students’ disability status and things like that.

David: Yeah, so that’s a real issue. I mean, I think I’m still comfortable with having that policy, but I think you make a good point.

Rebecca: So we certainly encourage folks to read the full report, to dig into the details, but we always wrap up by asking, what’s next?

David: You don’t change the momentum of a university overnight. So I think writing a report is a great step, and now there’s lots and lots of work to be done to both evaluate the merits of the arguments in that report, and to the extent the arguments have merit, to change our culture, and some of those changes come with legislation, which indeed, Harvard has passed. There’s new language in the undergraduate handbook, which is in the report and mentions a lot of the things that I’ve discussed, including other things that I haven’t discussed, like Chatham House rules in class to give people more comfort speaking authentically. Chatham House rules, as I think your listeners know, is a policy of non-attribution so people can say things in class, experiment with new ideas without being afraid that it’s going to show up on a social media post five minutes after class ends with their name attached to it. So legislation, policy making, culture change, rules that align with the messages that I’ve said, like, how much do we allow pass/fai… Harvard is restricting the degree to which students can take pass/fail courses, not eliminating it, but reducing the freedom to do that as frequently as students did so in the past… finding ways to implement and enforce policies that ban electronic devices in the classroom or at least limit their usage, figuring out where the sweet spot is, and all of that, changing the norm so students get comfortable with difficult conversations, not just in the classroom but also in the dining halls, creating a classroom culture where we’re excited to talk with people who disagree with us, rather than horrified to interact with someone who doesn’t share our viewpoints. I’m seeing all these things actually happening in real time. There’s more and more willingness, just in the last year, for people to say what they think, and we have a lot of work to do. It’s not that culture change happens overnight, but we’re, I think, broadly, moving in that direction, even though there’s individual instances where I’m quite unhappy and still concerned about the freedom of speech on our campuses. So it’s sort of two steps forward, one step backward. But we didn’t get here overnight, and we’re not going to get out of the place we are in overnight. Let’s keep working on these issues and thinking about how we make our academic environments healthy and productive and engaged, and how we create classrooms that people are super excited to have the opportunity to enter and sad when the 32 courses are over and they’re off to the rest of their lives. College should be an extraordinary privilege and an academically delightful one, and getting back to that place, if we were ever there, is going to take some work.

John: Well, all the things that you’ve discussed, I think, pretty much all colleges are facing and it’s nice to see the actions that you’re taking. And I think a lot of institutions could benefit from exploring these approaches.

David: Thank you.

John: And we will include a link to the report in the show notes, as well as a link to the New York Times article that provoked our interest in this topic.

Rebecca: Yeah.

David: In the New York Times article, I will say one thing, it puts a fair bit of emphasis on the students. I don’t think that’s the right balance. I think we need to put a lot of emphasis on what the faculty are or are not doing to create a stimulating classroom environment that will, in this dance between the student and the instructor, create a combined commitment to all the things that we’ve discussed, and just saying the students need to be more academically inclined, I think, is the wrong message, and the New York Times article seemed to have a little bit of that tone.

Rebecca: I appreciate your emphasis on actionable items, and as members of our institutions, we can only control the actions that we can take. [LAUGHTER]

David: That’s right, we’re going to make all of this better by creating classrooms that are exciting to be in.

John: And at least move in that direction as much as we can.

David: Yeah, I’m pretty optimistic, actually. I think there are some people that are just brilliant, charismatic teachers, and it’s impossible to copy and paste that. But there’s also a bunch of low-hanging fruit that doesn’t involve being a charismatic instructor, that can create the classroom environment that’s exciting to be in. I actually think that we can create experiences that are very meaningful for our students by sort of working down a long list of check boxes for classrooms that have the right ingredients for engaged learning and active learning.

John: Well, thank you. We very much appreciate you joining us.

David: Thank you. Great to meet the two of 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 Madison Lee.

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423. Using AI for Project-Based Learning

Students are motivated to learn when they have autonomy and see the purpose in what they are learning. In this episode, Tara Chklovski joins us to discuss a curriculum in which students use AI tools to solve challenging problems in their communities. Tara is the founder and CEO of Technovation, a nonprofit developer of a curriculum used in over 160 countries and reaching over 400,000 students, to prepare young women for careers in technology.

Transcript

John: Students are motivated to learn when they have autonomy and see the purpose in what they are learning. In this episode, we examine a curriculum in which students use AI tools to solve challenging problems in their communities.

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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 Tara Chklovski, the founder and CEO of Technovation, a nonprofit developer of a curriculum used in over 160 countries and reaching over 400,000 students, to prepare young women for careers in technology. The current focus of Technovation is on teaching students to use AI to solve real-world problems in their communities. Welcome, Tara.

Tara: Thank you, Rebecca, thank you for having me.

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

Tara: Yes, I am. [LAUGHTER]

John: What kind of tea are you drinking?

Tara: It’s Costco’s green tea. I love it.

John: Very good.

Rebecca: Excellent. I have a Twinings English breakfast that I actually got when I was in London.

John: I have Twinings English breakfast that I got delivered to my house by Amazon. [LAUGHTER]

Rebecca: Actually, I made a whole pot this time, John, like it’s the good stuff. So we’ve invited you here today to discuss Technovation. But before we start with that, can you tell us a little bit about your journey to this position.

Tara: Yeah, I grew up in India, and I always wanted to be an aerospace engineer, and I came to the U.S. to do my Masters, and then my PhD. And it was just interesting to me that I did an internship in India at Hindustan Aeronautics Limited, and I was one of the only women on the research and development team, and then when I came to the US, and I was doing an internship at a drone company, I was also one of the only women on the research and development team. And it was just very surprising to me that the most powerful country in the world had a similar kind of talent makeup and distribution as a very low-income country. India was much lower income than it is now, and it made me kind of think about it, and it was just surprising to me to run into people in the U.S. where, especially women, thought it was okay to say, “I’m just so bad at math,” and that is almost like such a shameful thing to say in India, because it’s kind of saying like, “Oh, I’m not intelligent or I’m not a good person.” And it was just kind of interesting to me to see the culture of even people in a wealthy country just assuming that certain paths are not open to certain people based on their gender or socio-economic status, and in the richest, most innovative country in the world, that’s also the most entrepreneurial, that was just such a shock to me that that is okay for us to say. And so I stepped out of my PhD program and actually started a nonprofit 20 years ago, and the goal was to bring cutting-edge technologies and an innovator’s mindset to groups that are not typically thought of as innovators, and that was the sort of path. And we were funded by the U.S. Navy in the early years, and we were very, very data driven, trying to figure out what type of model makes the most impact on students and their mindset as a innovator. And one model really rose to the front that had a durable impact on students’ identity as a problem solver, and that was a three-month long experience where you work in a team, find a problem in your community, and actually build a technology solution and present it to your community. And that experience is unforgettable. And 10 years after that experience, students would choose STEM careers because of that very, very formative, collaborative learning experience. And when we saw that, it was like, “Okay, this is it, and we need to scale this up.” And we brought it to 120 countries, and it has run primarily as an after-school program, and now we have adapted a new version of the same curriculum, but making it easier for educators to use in the classroom. And that’s the AI in Action curriculum.

John: Can you tell us about this program?

Tara: Yeah, as I was sort of mapping out the journey, it is really our Technovation curriculum and our Technovation model of supporting students to go out into their community, talk to nonprofit organizations, actually any organization, find out what are some big problems that are impacting people, and then developing a technology solution and testing it with those users. So we started doing AI nine years ago, before anybody was talking about AI. Nvidia and Google and the Patrick J McGowan foundation were the three sort of leaders who recognized what was coming and wanted to make sure that there was a lot of education support built in when AI really became mainstream. And guess what? It took about nine years for it to become what it is today. And so we are entering the space with a lot of evidence-based curriculum, a lot of incredible stories of students actually using and building AI solutions for good. And so, as I had mentioned, this curriculum has run primarily in after school, and so we adapted it to be a shorter version that educators can adapt for in school, and you go through the same sequence of how do you find a problem that’s a meaningful problem, impacts a lot of people, and you need to have no prior technical background. How do you actually build an AI prototype? How do you build your own data set? How do you train a model? How do you make sure that it is not going to hurt anyone? And then actually test it with users, iterate, and then present it to the broader community on kind of like a demo day.

Rebecca: You mentioned that it was after school, now you’ve shortened it so it can be embedded into curriculum at schools. How do educators come about the program and actually bring it to their classrooms?

Tara: Yeah, so it’s completely free. Just go to technovation.org, and you can go onto the curriculum. And so there are two tracks. Basically, one is a competition track, and that is primarily for girls, and so that has typically been what we’ve been running after school. And then we have the AI in Action track, which is for educators, for use in the classroom, again, completely free, and you can access it from our website. You can also sign up for our educator guide that will provide you. Again, you know, you need no prior AI background, and the exciting thing is, we want you to become part of our global educator community and get connected to other communities of practice, learn from one another, and especially for students, its super inspiring to see what other students around the world are using and building to improve the world.

Rebecca: Can you talk a little bit about the competition track, why the focus has been historically on young women?

Tara: Yeah, we don’t have, like, I started like, the problem statement. I think every one of the 190 countries legally discriminates against women. They have a very big ladder to climb. And I just think that a country really loses out on innovation when you just have one type of person that is in the leadership role or in the sort of innovator’s seat. And, I think, if a country is really to make progress, you want everybody to have these kind of future-ready skills and these future-ready mindsets. And so I don’t see this as from an equity lens, I really see it as this is what a country needs to innovate and to stay thriving. So that, especially in the age of AI, where so many of your traditional white-collar prestigious jobs are being automated, there’s a real question of like, what are the big problems that are left for humans to solve? And these are these very complex human-related problems where so many people don’t have access to healthcare, education, there’s hunger across the US. And so who’s solving these problems? These are very, very difficult problems, and we are not empowering students to tackle these very complex problems. And so I think that this kind of future-ready skilling and mindset is really important to bring, especially to groups that have not been part of that.

John: Can you tell us a little bit more about the competition track? How is it structured?

Tara: Yeah, absolutely. So there’s just so much evidence to show that having a deadline motivates humans to try doing big things in a short amount of time. And so, yeah, it is a challenge and the challenge is find a problem in your community with a team. And so we invite girls to form teams sometime around now, anyone can go onto our website and register and start basically going through all the steps. As an educator, you can think about if you want to invite or do a club, or an after school club, or bring some students together, and then have the students self select into teams… anywhere between one to five… and then the most exciting part is for them to find a problem that they all care about, that they want to solve. And this is the highest learning opportunity, because they have to sort of think about their own value systems, who they are as people, what do they care about, and then they have to compromise as a team. And so the collaboration that happens there is very, very deep, and the conflicts that happen and the negotiation that happens. Once you register on the Technovation platform, we try to connect every team with mentors from industry, depending on your zip code or whether you’re virtual, and so educators are always connected to more help, so that the teams are not on their own. And then the team of girls and mentors walk through the 12-week curriculum. There are three subtracts divided by age. So beginners, eight to 12, junior is 13 to 15, senior is 16 to 18. And now we have an advanced accelerator track for 19 to 24. And so the curriculum is appropriately scaffolded for these age groups. And then, yeah, you spend 12 weeks actually prototyping, testing your product with users, and then doing that final demo day. And depending on how effective your solution is and how big your problem is, you may make it to the different rankings, so quarter final, final, and then we have the big world summit, where we apply the finalists from around the world. Last year we had it at Nvidia, and that experience is absolutely life changing for these girls, because they make friends with other innovators from around the world, and they stay in touch with them, and that’s their cohort of fellow inventors. So yeah, that’s the whole journey. This past year, we had about 33,000 girls compete.

Rebecca: That’s wonderful. I’m thinking, as an educator, it sounds really enticing and really exciting, but also maybe a little overwhelming to like, take a group of students on a journey like this. You mentioned that maybe the educators don’t necessarily have to have a background in AI, but what are some things that they would need to be prepared to do to lead a project? And is there training for the educators?

Tara: Absolutely, there’s a training for educators, and you can sign up, and now is the time, and you get connected with my incredible team of colleagues who absolutely love to support educators on this professional development journey and this journey of sort of bringing a sense of purpose to their students. It’s so, so fulfilling, because it’s not like your average class project, because you’re actually going out into the community. You have students listen to people and hear about their problems and then frame a solution. And students are very self motivated, because when you sort of promise somebody else that I’m going to make a solution for you, the students have an increased sense of accountability and responsibility, which grades can never do. And so you really unlock this deep sense of purpose and a problem solver’s identity in students, and that’s very fulfilling for educators. So Rebecca, you’re absolutely right. It feels overwhelming, but I think the thing to remember is like, there’s no right or wrong, because even a very lightweight experience for students is very powerful, and like with FIRST Robotics or any kind of competition framework that lasts for a long period of time, every year you do it, you get more students, you understand things a bit better, things become familiar, and you just get better at facilitating. But you’re not alone, and I think if you keep in mind that you don’t need to be the expert, but you can basically use AI to solve so many of these questions that you may have, it’s not going to be that overwhelming, because if there’s something that you don’t know, you just say, “I don’t know, let’s go find out.” And that part is just fun. I find that it’s so exhilarating to work with young people, because they’re so unafraid, like I’m afraid to try new things and break things, they just go bang at the keys, break things, and nothing happens. And it’s very inspiring to me, and that kind of reverse mentoring is something I’m like, “Yeah, I’m a facilitator, but I’m learning so much here too.”

John: You mentioned that the AI in Action curriculum was free. Who bears the costs of this? I know you’re a nonprofit organization.

Tara: Yeah, that’s a great question, John. So technology companies and our corporate partners who are really interested in building a talent pipeline, a talent network, because if we are to increase resilience in the United States, there is a real question of, where are the people coming from? And so the industry is very, very interested in addressing the skilling problem. AI skilling is a very important part of the full AI supply chain, and that’s what they have been supporting, the development the build out of the curriculum, as well as engaging their employees as mentors.

Rebecca: I want to pick up on something you were just talking about, which is there’s a lot of opportunity for educators to learn from students about lack of fear. A lot of college students, for example, are more comfortable with AI than maybe some of the faculty. Is this also true in elementary and secondary education, and if we’re going to venture into an AI space that we might be unfamiliar with, what are some basic things to get started?

Tara: I honestly think the Technovation curriculum and experience is a very powerful one, because it actually unlocks and opens up the black box of how a large language model works. I’ll give you one example of what a student builds, because it’ll help you immediately understand what it means to build a dedicated AI solution for a problem. So a girl was looking to clean a local lake. This was in India, and bringing volunteers together, and she wanted to sort of give some data back to the volunteers about how the lake was getting better, and she didn’t have access to a water sensor and her idea was she would train an AI model to detect the sounds of birds, and more birds would come as the water became cleaner. And so when she was looking online for an AI model that would automatically detect bird song, she only came across North American models. And so she built her own data set of Indian birds so that it could actually recognize the Indian bird songs. And these are examples of students around the world building new data sets that don’t exist currently for problems that are not being addressed by the AI models and solutions out there. And so educators can really be on the forefront of that if they were to partner with young people and students. And that’s where the innovation lies, and the only thing holding us back is fear. And I think we’re all super busy, and so the other thing holding us back is that we probably feel too overwhelmed and uncomfortable to make time and to prioritize this. But here’s the thing like, this is unlike previous technologies. This is unlike the internet. This is unlike mobile. This is unlike a lot of the other kind of technology waves, because it’s never threatened what humans did so closely. And the kinds of conversations we are having today are very different from what we used to have before, because sort of what it means to be human is being questioned when an AI, sort of chatbot responds sympathetically to you, and what if you didn’t know it was an AI chatbot, like, what if your friend was not a true friend and responded in a similar sort of robotic way? It still makes us feel the same way, right? And so that is a very scary piece, because that means that I don’t know what it means to be uniquely human. And so I think that over the next five years, people, especially our age, adults, if we are not fully grasping our hands around this crazy, scary thing, we’ll just get more and more buried by our own fears. And so the most we can do is to really embrace it and experiment and young people do it the best. So I think the Technovation model of adults co-learning with young people is such a powerful and a fun way. Otherwise, online learning is so boring. [LAUGHTER]

Rebecca: One thing that you’re highlighting is having a goal, [LAUGHTER] or like a problem that you’re interested in and connected to and that you want to solve. And I think that’s always a motivating way to pick up some new skills.

Tara: Absolutely, and I think grounding it in your community, whatever your community would be, it could be physical, geographical, online.

John: Now, I know at the college level, students are often racing a bit ahead of their professors in terms of adopting AI tools, and in terms of that willingness, as you said, students are much more willing to try things and not be too worried about the risks of things breaking, but I know a lot of college professors are a little reluctant to try it. Are you seeing the same sort of things in K-12 education? The response of many school districts initially was to try to ban the use of any AI, but that broke down pretty quickly, but are you still seeing that same sort of resistance to introducing AI tools in K-12 education?

Tara: Absolutely, you can see it everywhere. It’s the same kind of percentage of adoption, fear, hesitation, almost in every workplace, and especially so in the education space, because it takes so many years to build curriculum, build evaluation methodologies, set those systems in place, align teacher… like all the incentives and performance around that. And so I think we’re going to have this period of intense chaos where the evaluation methods have not caught up with the tools that students are using. And so I think the place is really to have open conversations with your students, because this is such an interesting time. You can bury your head in the sand and say, “This is above my pay grade,” or “This is not my job,” or you can really engage your students in some fascinating questions about how they are using AI tools to do their homework at home, and how do they see their brains changing as they are using AI for a lot of their assignments. And I think that will be very, very interesting. I don’t know if schools can quickly adapt and ask these questions, of like, what are the skills that are important to teach? Because I think we’re all grappling with that question. I think when people talk about empathy, being uniquely human, I was just kind of saying, I don’t know what that means when a chatbot can mimic empathy much better than even a human could. So I think that this is a time for parents and educators to really open their minds and actually ask young people and their children, no matter how young, because guess what? They’re all using AI. What do you think is happening in your brain? I have two daughters, and my older one was saying that she can feel her brain going soft in some places, but being exercised in different ways. And so that’s very interesting. Like, I totally feel my brain going soft in some ways and stretching in other ways. And so, yeah, what is happening here?

John: People who already have become experts or have developed some expertise in an area are able to critically evaluate the output of AI systems. But there’s a lot of concerns that beginners might do some cognitive offloading and not develop those critical thinking skills needed to critically evaluate the output. And we know those hallucinations do occur because of the nature of these models. What can teachers do, at all levels, to help develop the critical thinking skills needed to be able to critically evaluate the output?

Tara: I think that’s such a great question, John. And I’ll come back to really encouraging students to tackle the big problems, because complex systems thinking is not a linear problem, like where you follow a bunch of steps. I’ll give you an example. A very common problem that students try to tackle is hunger, especially in the United States. And the problem is so complex, because many people in the middle class families see the amount of food being wasted in restaurants, at your home, and so you immediately think, “Oh, what if it was a better food distribution method that would bring this food that’s being wasted to the people that are hungry?” And many people will just stop at that solution, and that is not a solution. The hard part is, really, this is perishable, these are perishable items. They’re dependent on preferences. It’s dependent on where you are, and the cost of logistics and transportations are enormous. And how would you do that at scale? Everything can be done for one little thing, but you’ll break down when you do it at scale. And so that’s a complex system, and that kind of mental mapping and problem solving is completely different from the little project that you would outline to a student. And so those are the kinds of skills that are really the ones that are what we need. And I think teachers have… there is time in the classroom. And if you align a lot of, I think, reading, literacy, math standards, into a rich project like this, I think you are preparing students very well for the future. And then they’re completely understanding they’re using AI at every step of the game, at every step of that way, and they’re seeing exactly what you were mentioning because they’re so invested in the end outcome that they’re checking and they’re like, “Wait, that doesn’t line up.” And so they’re motivated, they’re invested in their learning journey. I see students short cutting when they don’t care. We’ve traditionally sidestepped that question when students ask, “why should I care about this? Why does this matter to me?” And we typically say, “This is important for your brain, this is important for your future.” And I think we can’t hide behind that for much longer.

Rebecca: You’ve mentioned that the competition track at least is 12 weeks and I know the one for the AI in Action is a little bit shorter, but there’s a structure to it, because it goes week by week. Can you talk a little bit about what that structure looks like?

Tara: Yeah, absolutely. And so the very first part is problem identification, kind of like what we were talking about, and not very broadly, like, “Oh, I’m going to solve hunger,” but really narrow it down into what exactly is the problem, and then how can AI technologies actually address it? And so if you were to go back to our hunger example, it could literally be, how do we use AI to predict supply and demand? And how could we make the distribution of unused food as efficient as possible with minimal use of gas and energy and things like that? And so that problem identification, a meaningful problem, takes a few weeks, and we provide a lot of exercises, worksheets, for students to be able to do this, and for educators to facilitate that. The next phase would be prototyping, where you actually prototype on paper, and then you go out into the community and get feedback from actual people on would this actually help you? Where are you getting stuck? And then kind of building out the user interface for that. Third stage would be to actually start to code it, and this is where they’re learning whether they’re going to build it as a mobile app, or have it as a website or whatever. And then you start to think about, once it’s actually working, you test it again with people, and then you actually think about, how can you bring it to a lot of people? And so there’s a marketing element to it. There’s a business element to it. You look at competitors, you look at how much money is needed to actually scale it. And so that’s where that entrepreneurial learning happens a lot. And then the last phase is again, after iterating your solution a bunch of times, you actually develop a pitch video or a pitch where you tell your story and you practice explaining your solution to a larger audience so that more people adopt it and give you feedback. So that’s the full spectrum, and it’s a spectrum of starting a startup. I think for educators, and Rebecca, you were talking about like it’s a very real thing, like when you’re busy on your day to day, it feels overwhelming to try something new, to learn something new, and to restructure your life to make room for that. And I think that, as I was saying, that this is a very unusual time, and you may think that, “Oh, I’ll be fine,” but the option is, there’s a very real risk of jobs being completely eradicated. I think educators are not going anywhere, because we learn best from humans, but you can really be a superstar if you learn how to leverage these tools to support you, rather than have that be pushed down on you when you’re not ready. And so I think taking ownership of your own learning journey and your career and your professional development, I think it’s a very exciting sort of juncture for adults today where you can choose to really be a world class superstar because you have the world class superstar tools at your disposal, and the only thing holding you back is your mindset.

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

Tara: Yeah, I think for us, it’s really about trying to support and get educators to help their students get ready to solve these big problems. Because I sort of see an extreme case where corporations are making so much money with AI automation, maybe, like, I don’t know whether universal basic income is a real thing, but what if it is? Then the big problems that corporations will not be solving are the ones that still cause human suffering, healthcare for all, high quality education for all, no hunger, and those are not incentivized by industry. And so whose job is it to solve those? It’s our job. And I think that really encouraging students to have that sense of empathy and empathize with another human’s suffering and to try to do something about it. I think that’s the core of what makes us human. And so that’s why we want to bring this and work with educators to bring this to many, many more students.

John: So if people would like to find out more about the program, we’ll include links to your website so that people can learn about the AI in Action Program and the other programs that you run. And thank you for all of your work on this.

Tara: Absolutely. Thank you for having me.

Rebecca: Yeah, thank you for sharing this exciting work. And it’s even better that it’s free.

Tara: Yes, absolutely!

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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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422. Negative Examples

To understand what a concept is, we must also understand what it is not. In this episode, Bill Goffe joins us to discuss his study of the effect of the use of negative examples on student learning outcomes.

Bill is a Teaching Professor in the Economics Department at Penn State.  He had previously been one of our colleagues here at SUNY Oswego. Bill is very well known in the profession for his Resources for Economists on the Internet, which was one of the very first internet guides available for economists, and it’s now hosted and sponsored by the American Economic Association. He has served as a member of the American Economic Association’s Committee on Economic Education, the Secretary-Treasurer for the Society of Computational Economics, an Associate Editor for Computational Economics and the online section of the Journal of Economic Education. Bill is currently co-chair of the Liberal Arts Teaching Group, a faculty learning community at Penn State.

Show Notes

Transcript

John: To understand what a concept is, we must also understand what it is not. In this episode, we discuss a study of the effect of the use of negative examples on student learning outcomes.

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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 Bill Goffe. Bill is a Teaching Professor in the Economics Department at Penn State. He had previously been one of our colleagues here at SUNY Oswego. Bill is very well known in the profession for his Resources for Economists on the Internet, which was one of the very first internet guides available for economists, and it’s now hosted and sponsored by the American Economic Association. He has served as a member of the American Economic Association’s Committee on Economic Education, the Secretary-Treasurer for the Society of Computational Economics, an Associate Editor for Computational Economics and the online section of the Journal of Economic Education. Bill is currently co-chair of the Liberal Arts Teaching Group, a faculty learning community at Penn State. You can also find Bill on many listservs and forums devoted to teaching and learning. Welcome back, Bill.

Bill: Thanks, John. Appreciate that.

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

Bill: I am. I’m drinking Twinings green tea, decaf.

Rebecca: Nice, good choice. How about you, John?

John: I am drinking a peppermint tea from the same vendor.

Rebecca: Nice. I have hot cinnamon spice today because my daughter requested a pot of it. So we’re sharing a pot of hot cinnamon spice this afternoon.

John: Very nice as we approach the holiday season here when we’re recording. So we’ve invited you here today to discuss your recent working paper with Austin Boyle entitled “Negative Examples in Lecture Improve Student Learning. First, can you tell us what you mean by negative examples?

Bill: Sure. So you’re describing a concept to someone, let’s say a young child, and describing what a dog is, and you talk about different types of dogs: Dalmatians, Pomeranians, whatever. And unless you describe how a cat is not a dog, they’re probably not going to have a great idea of what a dog actually is. And this might be the case, say, for a second language learner, where you might have different terms for different types of mammals, also related to something called contrasting cases, where experts are very good at delineating different types of phenomena or different types of facts, if you will, say a physician or variation theory used in math. So these are all ways of getting students a rich understanding of something.

Rebecca: Can you talk a little bit about what prompted your interest in negative examples?

Bill: Sure. So I’ve used clickers to teach for a long, long time. You currently use iClicker Cloud. Love it. And I tried to write a paper on just in general, using clickers to teach economics, and the paper didn’t really work very well. There’s reasons it’s never been written, and the reason was I use clickers in many different ways, so a little bit more focused approach to having a paper. And a few years ago, I came across the book How to Teach Economics by Yousuf Hamid. He was a high school teacher in Great Britain, and I had to read that. Well, to be honest, I read it during graduation. You have to go to graduation every year, but, you know, it always kind of work. And he talked a lot about negative examples. And I thought, “Oh, that’s pretty interesting.” And it also followed work by Kirschner, Sweller, and Clark on the idea of direct instruction lowercase “d” and “i”, and I thought, “Well, that’d be an interesting to try, and it’s fairly focused,” and that’s the genesis of the paper, and pulled Austin into it as well.

John: We’ll include citations to those resources in the show notes. So, many people, when they’re teaching, tend to use primarily positive examples. What’s the problem with just using positive examples of a concept? I think you’ve already addressed that a little bit.

Bill: One example, the original work on this, at least formal original work… the idea goes back a long, long time… it was by Engelmann and Carnine, a book on direct instruction. And they give an example of a glem, a G- L-E,-M. Could you define that just with one example? Well, you could not. And they give an example of a pencil. Well, is it wood, or is the pencil horizontal, whatever? But it helps you focus in better on an idea when you understand what it’s not. For example, in economics, we talk about capital quite a bit. Unless you tell students, it’s not stock, they probably won’t pick up on that. Or you give examples of money, how money is very specific to economics. To economists, they’re probably going to pick up on it’s not some general sense of wealth.

John: And I can affirm that the concept of capital is one tht students have always had a lot of trouble about because the way we use it in economics differs from how it’s used in everyday use. And just for our listeners, could you explain the difference between the everyday use of capital and the way we use it in economics?

Rebecca: I’m an everyday listener.

Bill: Great. We’ll see if this works. So, to an economist, capital is something owned by business, manufactured and used to make things, so, a commercial mowing machine, a commercial oven, a jet liner, construction equipment. In everyday usage, it might also include financial capital, stocks, for example, might be considered capital. I would add one thing economists really haven’t done, the Bureau of Economic Analysis actually redefined capital recently, includes intellectual capital, and I just don’t teach that. It would be confusing, but there’s actually about as much intellectual capital these days as physical capital in the United States. But maybe in an intermediate course. I would do that.

Rebecca: Seems like an easy thing to get confused. Can you talk a little bit about how you pick which concepts to use negative examples for? I can imagine that some concepts are more ripe for this strategy than others.

Bill: Yes, we wanted something fairly limited, not a long concept of how to manipulate a graph or something, that could be a further work, but just a definitional sort of thing, but the students have misconceptions about this, as we described there with capital. And so we picked three different ones, money, capital, and technology. Technology in everyday use, our phones, for economists, it’s basically efficiency in the production process. You know, it is rather unfortunate, there’s what, 50,000 words in the English language, and we use words that are used in other ways just to make it more complicated. And I joke to students, I wish I could use different words, but that would hinder their further education, I fear..

John: And this has been a problem for economists for well over a century in those very same issues, and it would be nice if we didn’t have to do that, because it introduces that confusion right when students are just starting to learn the vocabulary of economics, and the way I talk to them about I say, “Hey, part of this is you’re just learning the language of economics, which may look similar to how we use these words in everyday use, but it’s a bit different, and you need to focus on these things because of the ways in which we’re using the term.”

Bill: I would add that I’ve seen some STEM education research papers. They teach concepts first and jargon second, and they find that actually improves learning because you’re trying to remember the terms and they’re using the terms, it overloads your working memory. And there you teach jargon later, they find improvements.

Rebecca: We do that sometimes in design, where we separate the technical skill from the concept, because learning them all at the same time can be complicated. I was thinking of a term that I run into all the time where there’s confusion, where this strategy might work well, which is digital accessibility, where people confuse the idea of meeting these standards to help people with disabilities gain access to content and material with the idea of “just available,” like it’s on the internet.

Bill: I learned that distinction just recently myself. I would add on choosing these terms, we found even after instruction, students got it right between 60 and 71% of the time. So we seem to have picked a nice set of terms and definitions to use.

John: Which is actually much better than they would tend to if you had only used positive examples. And we’ll talk about those results a bit in just a minute.

Rebecca: Can you tell us a little bit about your study? How you had it set up? How many students were involved?

Bill: Yes, we had about 1200 students. I teach large sections, about three sections every semester, about 340 students that did this over two semesters, basically just repeated things, get more power for our tests, and did a so-called crossover study. Crossover study, have your participants that are both controls and the intervention side of things. We did a 3×3 one, which is a little bit hard to describe on a podcast, where you can’t visualize things, but there’s a fairly famous one for ultra processed diets. You might have heard of by Hall and company in 2019. They put people into hospitals, they control directly what they ate, and one group got ultra processed foods for two weeks, the other group got more wholesome foods, I guess you could say, and then they switched who got the wholesome foods, who got the ultra processed foods. So thus you could deal with the fact that people might have responded differently, that different foods have different effects on people. And we did the same thing here in our study, except we had three concepts and three sections and three teaching methods.

John: So what were the three teaching methods that you used in the study?

Bill: So we just use a positive example, where I would list a half dozen examples of capital or money, or not quite of money, because there are quite six examples of that. Then with lecture, we had both positive and negative examples, where these things are money, this is not. This is capital. This is not. And a third type of teaching method was with a cliicker question, where I gave a student’s list of things. Some are positive, some are negative examples, and they just choosed which was which.

Rebecca: So what did you find? [LAUGHTER]

Bill: We found that including negative examples improved learning in the following class by about 21 percentage points. Let’s say the class got 50% correct just with positive examples, the class that got positive and negative examples got a 71% correct. So it is very statistically significant, it was robust with lot different statistical methods we used. So we’re pretty pleased with that, it was a clean result. Sample size helps, and I think probably doing it just with precise definitions, not long drawn out concepts or something like that.

John: Based on these results, would you suggest that perhaps we should all focus a bit more on using negative examples as well as positive examples in our teaching?

Bill: Yes, most certainly it’s an easy intervention. For example, you might know Jim Lang’s book Small Teaching, where he talks about easy interventions you can do, and this one’s easy, as long as you remember to do it. But you’re making up your notes, you think of things that aren’t whatever you’re talking about. You’ve probably seen those on exams, overall. Interesting result we had too that the clicker questions, the active learning, was no different than the lecture. So we’d like to do further work on that at some point, most active learning studies have been semester long, active learning not very well defined, and we didn’t find any difference here, so presumably, at some point, active learning make a difference be interesting to find out what that difference might actually be.

John: That’s an interesting result, and that’s probably worth some follow up.

Bill: I think partly on if you do see similar results with the flipped class and active learning and such, people don’t really have a framework on what they actually ought to be doing before class and what active learning should look like. I’ve got another paper on this, I mentioned, that uses so-called deliberate practice, That’s the 10,000 hour rule. It’s really not 10,000 hours, but there’s some key parts of active learning people don’t do that much. One is to focus on sub skills, for us in economics, things like change in demand versus change in quantity demanded. We generally don’t teach that specifically, is my thinking. And then also, you want to make connections between things. How are supply curves and demand curves separate? How are they similar? Because we remember things in terms of frameworks or schemas. The negative examples help develop schemas. You understand what things are not, kind of triangulating, if you will. And good active learning does the same thing, but if they’re just filling in a worksheet with numbers, they plug into the formula, that’s active learning, I suppose, but not much learning there. So we ought to focus more on exactly what active learning is doing for people.

Rebecca: Following your study, what are some different ways that you’ve incorporated this practice into your own teaching practice?

Bill: So I definitely try to do the negative examples, capturing, oftentimes what I see on these exams and when I’m thinking of a question, I always try to have it connected to something else. Oftentimes one concept’s related to another one, and we make that connection explicitly in clicker questions so that, “Oh, if you remember this, this is a variation of that.” For example, how is consumer price index similar and different than the GDP deflator would be one thing we do, and why are they fairly similar? To understand better how those things work. Oftentimes, I explain things based upon what they’ve done before. Another thing I do is we have very nice technology at Penn State, I have a main screen in the classroom. I have a side screen. Side screens, I put up an outline includes both the outline for the day and our bigger topics. So I’m constantly referring to that outline saying “what we’re doing here is a variation of this, is different than that,” and so forth. And if I do surveys as students, they find that very useful. A better study would be to not have the outline there someday and see how learning goes. I just now thought of that as an idea to do a study on.

John: We know that students come in with a weaker set of connections than faculty, and sometimes we assume that they’re able to make these connections and see the relationships, but they haven’t yet developed that skill, and sometimes it’s generally believed that that type of scaffolding, by putting up an outline of the main concepts you’re trying to get across, can help. But I don’t know if I’ve ever seen any tests of that, certainly not in economics, but I’m not sure if I’ve seen it in other areas either.

Bill: I have not. I can’t say I’ve looked very hard. I would add, to do my literature searches. Now I oftentimes start with ChatGPT, but that’s another whole podcast, [LAUGHTER] I suspect.

John: Yes, and there’s some other good tools out there, too. There’s research rabbit and other things that will actually, even in some cases, will provide more accurate results. But ChatGPT is getting a lot better all the time for these things.

Rebecca: Other than in the test results, have students responded? Or have you asked students about using the negative questions and how that impacts their learning?

Bill: I have not actually, that’s a good point. That’d be another thing to add. It’d be interesting see if they actually can pick up on that. Oftentimes, they’re not that great on understanding their own learning. Something we’ve all seen as instructors, someone thought they did much better on the exam than they actually did, which is something you want to avoid. That’s a tough place to be in and you have to be sympathetic as instructor when that occurs.

John: Now, one thing to keep Rebecca happy, we should also ask a little bit about the methodology that you used. In the study, you used both pooled data as well as panel data, and you also used ordinary least squares and binary dependent variable models. So could you just talk a little bit about why you used multiple approaches and how consistent were the results across those.

Bill: Yeah, the results are very consistent. So sometimes you don’t quite know what econometric approach you take, you can argue this is better, that’s better. And another approach, and it’s pretty common these days, I think it’s by far the most common, is you take multiple approaches and see if the results are robust, to see if you get similar results. Indeed, we did. So it’s a very happy occurrence when that occurs.

John: So were you surprised at the magnitude and strength of these results? I know when I read the paper, I was surprised at just how large that effect was, because it’s very rare to see effect sizes that large in a study of alternative teaching approaches.

Bill: I don’t think I had many preconceptions here. That’s odd. I certainly was pleased, but I can’t say I was quite surprised. It certainly makes me want to take this approach again with where we do the crossover studies, you can do many things with that. For example, we’re told we should teach about things students find interesting. Well, is that really true? I did an example of that this semester where Penn State students, they do a large fundraising project called Dance Marathon, or THON, and last year it raised $17 million, about half the student body is involved. And so I do a question about real prices, and the question was, did THON actually raise more money when you correct for inflation, you know, something they’re invested in a lot, and informally looking at the data, that didn’t seem to have any impact on that segment, on calculating real prices, I need to do more work on that, but I hope more people take this methodological approach, the crossover studies, where you could study a lot of things, the outlines, is it interesting or not, and so forth?

John: Because with the crossover study, you’re ruling out the possibility that there was just some systematic difference between the sections or between the other conditions, right?

Bill: Yes, exactly. We actually found there were differences between the sections. Some did much better on the questions than others, but by effectively pooling these different interventions and controls together, we can adjust for that. We can correct for that. I don’t see it that much in economics. John, have you seen it very much, the crossover studies?

John: I haven’t seen very many, but one that stands out, and I think it was done by economists, was back when the initial studies came out that suggested that when people took notes by hand, they remembered more than when they used a computer. I do remember a study, and I’ll include a citation to this in the show notes. What they did is they had all the students use mobile devices for note taking for a portion of the semester, they broke up the group into two halves, and half of them, for the first half of the semester, took all the notes using mobile devices, and the other half took notes by hand. And in the second half of the semester, they switched, and what they found was there was no difference between how students did when they took notes by hand and how those same students did when they took notes on the computer. However, those students who regularly chose to take notes on a computer, on average, did less well regardless of whether they were taking notes by hand or on a computer, which suggests that the students who choose to take notes by hand may be perhaps a little bit more focused on the material no matter how they’re taking the notes, and those students who may bring in mobile devices to use, perhaps may do it because they prefer having the availability of distractions. So it’s not quite the same thing, but it’s similar in that you’re controlling for the individual effects and separating it out so that you are getting a better comparison, correcting for any self selection in the people who are involved, which is really, I think, what crossover studies are trying to remove, the effect of any systematic selection between the groups.

Bill: I would add, I’ve surveyed my students, and generally my students aren’t performing quite as wel do choose to use laptops, and higher performing students do not, so consistent with your more serious study, for sure.

John: And another thing I’ve been noticing a lot is that for quite a while, I’ve been walking around quite a bit, but I’ve also noted that the students in the front third or so of the class tend to be very actively engaged in answering questions, and they tend to do really well, and they tend to ask more questions. But when I’m in the back of the room, I see a lot of laptops where there perhaps may be a video playing, there may be some online shopping going on, or there may be some sort of video game that they’re working on on the mobile device and their laptop or one or the other. And I think we often do see that sort of sorting in terms of the way in which students choose to work. I’m not sure how I could get rid of that effect, other than moving them all up to the front.

Rebecca: it does seem like at least the multi taskers, in quotes, are at least removing themselves, and because they’re probably distracting to other people, they’re just moving themselves to the back so they’re not a distraction to the people who are trying to focus.

John: Except they’re surrounded by other people, and there may be a bit of a contagion effect going on back there.

Bill: I’ve seen papers on the contagion effect, yes. There was a Carl Wieman paper. They did look at seating in large classrooms and found a very definite effect. They even switched seats halfway through the semester to bring the people in the back up to the front, and they still did worse.

John: Some of our students are just much more interested in the material and much more engaged than others, and there’s just so much we can do with that.

Bill: It’s tough, but I would be a little bit sympathetic to students. It’s been a long time since I’ve had to sit through a typical undergraduate today, and you know, you’re three, maybe four courses a day, a lot of sitting. That would be tough, a challenge. There’s a book called My Freshman Year, and there’s a field anthropologist who went to college for a year to understand her students better, and she found it was harder than she thought.

John: And she found some dramatic differences, too, in the way in which students connected with each other from when she had been a student, that they had much tighter networks with the people they knew before they came to college, because it’s less costly to connect with them over social media and so forth.

Bill: Yeah, friendships have changed with the rise of social media.

John: It used to be costly. I remember having to bring all these quarters to put in pay phones back when I was in school.

Rebecca: You mean, in those 1900s phones that my daughter refers to, John?

John: Yes, I think that would be the case.

Bill: What’s a quarter?

Rebecca: Yeah. [LAUGHTER] Well, I think we still know what quarters are. We just don’t know what pennies are anymore. [LAUGHTER]

Bill: Well, that could be, yes. [LAUGHTER]

John: Are there any other things you’d like to share about this study?

Bill: Well, I think that’s pretty much it. I think I would emphasize this little bit more about, again, developing student schemas. But we talked about that earlier today, this broad network, and it’s through connections, and this helps them make connections more focused on whatever the topic might be at hand. So I think negative examples could be used for teaching most anything.

Rebecca: Including our colleagues when we’re doing professional development and all kinds of other contexts.

Bill: Yes. [LAUGHTER]

John: And it’s easy for us to lose focus, because we already can distinguish these things more easily, and students have not yet developed that capability.

Bill: The curse of knowledge really reigns here, where you know it well. It’s hard to understand how other people don’t. I was doing a think aloud exercise the end of last semester where I interviewed students half the semester in how they’d interpret different things. And I spent a lot of time on aggregate supply, aggregate demand model in my course, standard staple things course, asked about certain phenomena, no one actually used the model. I was only interviewing four students, so, I mean, it’s not much power there, but it was kind of crushing, but it gave me insights on how they think about things.

John: In an earlier podcast with Doug McKee, he talked about having students come in and just work through the problems verbally, so he could see what sort of process they were using, and by seeing that, it was easier to help guide them in a more productive direction. Because if we don’t really understand how students are processing things, it’s hard to help them understand things more deeply.

Rebecca: Yeah, we use that talk-aloud method and design a lot too around troubleshooting, because troubleshooting strategies require, especially around technical skills, you need to think about problems in particular kinds of ways to be able to solve them correctly, or to be able to solve them at all, [LAUGHTER] and doing that kind of talk aloud strategy works really well for figuring out where something went awry in the troubleshooting.

Bill: And experts think a lot differently than students, I would suspect. You would never think someone thought that way.

John: And fMRI studies confirm that different areas of the brain are activated for experts and for novices when trying to solve a problem. So it’s easier when you become an expert, but we have to help students develop the skills to get there. While we’ve got you here, could you also tell us a little bit about how you teach classes so large with so many students, every semester.

Bill: I use Perusall as a way to have them read ahead. They come into class, they have some sense of things and then a lot of clicker questions, typically, as long as it’s not something new. For example, I was doing long-run growth. I do that a fair amount, and the book doesn’t really talk much about the institutions and the role of markets in that. So that was a day or two of me talking. I’m going to be doing the business cycle starting tomorrow, and that’s a fair amount of me talking. But other than that, it’s them pretty much asking questions, or me asking questions. Probably on Thursday, we’ll get to calculate employment rates like read about that in Perusall. I’ll ask them, “Who in this group of people is unemployed?” Then I’ll show them some data, what we compute from that data. So it’s a lot of using ideas. I do a lot of peer instruction where they first do votes on their own, and then I say, “Okay, there’s a lot of uncertainty here. Please convince your neighbors.” And I use an app by iClicker, so I can see their votes on my phone, and then they do a revote, and they usually improve 20, maybe 30 points, then I provide further instruction on the topic at hand. And there’s a couple papers I’ve seen. Let’s see Michelle Smith at Cornell, a computer science paper. They found that if you have students revote, that helps them; if you have a further explanation from the instructor that helps them; if you both, it helps them even more, and it’s probably enriching their schemas, they’re seeing several explanations that reinforce the connections between things. Also I do a series of quizzes, rather than midterms. I do seven quizzes. We’re very fortunate at Penn State, we have a testing center, so I send all my students there for testing. So I run the quizzes for two days, after the quiz they do a second stage of the quiz. In class they start, then they answer at home using any resource, and finally I release the answers, and then they reflect on and describe what they found most difficult on that quiz. So we do a two-week cycle. There’s a lot of feedback, even though the course is quite large, with about 1000 students a semester.

John: So having that testing center gives you a bit more security in the assessments than many of us have when we give quizzes online. Many of the assessments I use are done online, and I used to feel a whole lot better about them before we had ChatGPT and other AI tools appear, especially with some of the browser extensions, which will directly answer things, so you’re able to avoid some of that, which is really nice.

Bill: I really feel very fortunate, yes. I would add, it’s a good thing that the AI tools came after Covid, rather than before.

Rebecca: So one of the things that you mentioned when you’re describing your class was Perusall. Can you describe what that is for people that don’t know?

Bill: Oh, yes. An e-text goes inside perusal, and then students mark it up with comments, questions, and responses to other people’s questions. So it has them thinking bit about the text. It grades them automatically. They need to start early so there’s a little bit of spacing there. They need to make sure they document across the assignment, and they’re also rewarded for opening up multiple times, not just the evening before the exam. And it’s fairly straightforward. Most students get full credit on each assignment. So it’s a bit of encouragement there to read ahead. I used to use so called JITs for just-in-time teaching assignments where I’d ask some questions, including what they found confusing, but I like Perusall better. It’s less grading work, and also, I think it covers a little bit more topics or not just looking at the answers to my JIT questions.

John: Now, do you break them up into groups when they’re using Perusall?

Bill: Yes, Perusall actually does that. When I look at the marked up text from 1000 students, the page does not load basically, or has difficulty loading. And you can also download, I’ve not done this, you could download all their comments, and you can run through a chatbot or whatever to get a sense of what they have trouble with, or what they find confusing. I think textbook authors would find that really, really useful. You could say your textbook was designed with the input from 1000s of students, and so your students can read this more carefully. I’ve not found an author willing to do that, but I think that would be a big win for someone.

John: It could be, and from a marketing standpoint, that could be really helpful. When iClicker announced that they had AI tools being built in, I was really hoping that they would integrate that into the text responses, because right now, it’s just generating questions, which is not bad, and it doesn’t do a really bad job of that, but it would be so nice when you got maybe 1000 or more responses coming in, or a few 1000 responses, if it could very quickly generate a summary of the responses to free-response questions. That seems like something that wouldn’t be all that hard to do, and it would be a nice extension.

Bill: I agree. I think there’s some polling technologies that do that. For some reason iClicker doesn’t. I wonder if there’s a legal issue or some other constraint. I would love to see that as well so I’m not just asking five different possibilities.

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

Bill: So I want to do more work on active learning, the ideas from deliberate practice. Does it make a difference if students have good sub skills. And does it make a difference if you teach by making connections between different topics? There’s many ways active learning are implemented. And it would be cool to do a study where you look at active learning in many different implementations, but some get those interventions, some do not, in a crossover study, of course. So that’s what I would like to work on next a bit.

John: That sounds like a very productive area of research that could really be helpful for all disciplines.

Bill: Yes, well thank you. That’s the idea, I think, a bit about marketing of papers, you know, who would find it interesting, and so forth.

Rebecca: Well, thank you so much for joining us, and we look forward to your future research.

Bill: Thank you so much. It was great fun.

John: It’s great talking to you, Bill, always. I’ve learned a lot from you over the years, and it’s great to still be able to see your work out there in various places.

Bill: Great. Okay. Thanks so much, John. It’s great to connect up again.

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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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410. A Summer to Learn

Imagine exploring alternative teaching methods in a classroom focused on learning and not grades. In this episode, Russell Marcus joins us to discuss how this occurs in a two-week philosophy summer program. He is the Christian A. Johnson Excellence in Teaching Professor of Philosophy at Hamilton College. Russell specializes in the philosophy of mathematics and the pedagogy of philosophy. He is a Past-President of the American Association of Philosophy Teachers. Since 2018, Russell has been running a philosophy summer camp at Hamilton College designed to help faculty develop more effective teaching practices while also benefiting students.

Show Notes

Transcript

John: Imagine exploring alternative teaching methods in a classroom focused on learning and not grades. In this episode, we discuss how this occurs in a two-week philosophy summer program.

[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 guest today is Russell Marcus. He is the Christian A. Johnson Excellence in Teaching Professor of Philosophy at Hamilton College. Russell specializes in the philosophy of mathematics and the pedagogy of philosophy. He is a Past-President of the American Association of Philosophy Teachers. Since 2018, Russell has been running a philosophy summer camp at Hamilton College designed to help faculty develop more effective teaching practices while also benefiting students. Welcome Russell.

Russell: Thanks for having me.

John: We’re very happy to talk to you today. Our teas today are:… Russell, are you drinking any tea?

Russell: I have peppermint tea. I don’t do caffeine, so I’m all on herbal tea, and I have a box of peppermint tea in my office… constantly.

Rebecca: You can’t go wrong with mint. I have English afternoon today, relying back on an old favorite, John.

John: And I am trying to cut back on caffeine, but not today. [LAUGHTER] It’s a busy time getting ready for workshops next week, so I am drinking a black currant green tea from the Republic of Tea.

Rebecca: Nice.

Russell: Lovely.

Rebecca: So we’ve invited you here today to discuss the Hamilton College summer program in philosophy, which we read about in the July 21st, 2025, article in the Chronicle of Higher Ed. Can you tell us how you became interested in philosophy pedagogy?

Russell: Sure. So I started my career after college as a math teacher. I was a high school math teacher, and then I did that in New York City for a couple of years, and I worked also in a high school in Costa Rica. I taught there, and what I discovered in my first years of teaching was a real love of curriculum design. I taught something like 24 different classes in my five years between college and graduate school. After about four or five years, I started feeling both the pull of philosophy. I wanted to go back to graduate school, and I knew that I wanted to. I wasn’t quite ready for it when I came out of college. I also saw a kind of waning ability for me to be creative as a high school teacher. My first love in philosophy was mathematics, but I found the community in philosophy of mathematics to be kind of disappointing and frustrating as I started to develop as a scholar. And in 2008, I went to a meeting, this biennial workshop conference of the American Association of Philosophy Teachers, and there I found my people. So, after I got tenure, which was like 2016, I really turned my attention more fully to the scholarship of teaching and learning. I was kind of looking to teach some topics that students found naturally interesting, which was not the case with philosophy of mathematics. I kind of had to convince students that those topics are interesting, but when I turned more to teaching… I started teaching a philosophy of education class… I was connecting with students where they were a little more closely.

John: My experience was somewhat similar. I started teaching, and I was disappointed, though, with how much students were learning. So I started reading more about what other people were doing, and attending sessions at conferences related to pedagogy. And those are really great things for people to start getting involved in, and I’m still very much involved in all of that. As noted before, you began the summer camp at Hamilton. Could you tell us a little bit about how that came about?

Russell: Well, I’m going to start by telling you that we have kind of a running joke at the HCSPiP (That’s our acronym. It’s a terrible acronym, but anyway, we’ve embraced it.) It’s a summer program, not a summer camp. And so every time people call it a camp, I wince. I have to make a face, and this is around leadership, because I think of this as a serious academic program, but the students discovered very quickly the first year, they’re like, “Oh, it’s philosophy camp.” So yes, it is a summer camp, and they called it a summer camp at The Chronicle, but I’m calling it the summer program in philosophy. When I started working with the American Association of Philosophy Teachers and just getting involved in that organization very actively, I recognized that I had a lot of creative people around who had really interesting ideas about teaching and what they kind of wanted to do with their teaching. I also recognized that a lot of them were in places that were not as welcoming of pedagogical creativity as Hamilton was for me. I loved hanging out and talking, teaching with thoughtful and creative teachers, and when I talked with them, a lot of times, they’re like, “We can’t really do this because my students are like in these certain ways, and I’m not sure it would work,” or “my expectations of my department,” or “I’m not really allowed to do the kinds of things I would like to do.” Of course, I worked with lots of contingent faculty in the organization who are really concerned about their abilities to be creative in the classroom, and what happens if it doesn’t work out, and what does that mean for them in their attempts to get jobs or to get tenure, and so I thought, “wouldn’t it be great to have a space where people could just experiment, innovate in ways, and not ever have to worry about who’s looking over their shoulder and who might be skeptical of the kinds of innovations that they’re developing.” So I wanted to find a way to encourage more creativity, and also to just bring folks to me, so I could just sit here where I am and have folks bring creative pedagogy into my orbit. So that’s really how it got started. We have a fund for philosophy at Hamilton College that supports the program, and when I brought it to the department as an idea, they were enthusiastic to let me take it and run with it.

Rebecca: Sounds like some sneaky moves there to bring everyone to you?

Russell: Yeah, well, we have this money in the department, and I wanted to see how could I use it to support the community that had been so sustaining for me in my career.

Rebecca: Can you talk a little bit about who the students and faculty are that participate in the program?

Russell: The way the money works is such that we need to have a certain complement of Hamilton College students in the program, and our current dean has been a little more liberal than the previous Dean about that. The previous team was like, “you must have half the students be Hamilton College students.” This year, we had eight of the 20 students were Hamilton College students, and then the other students, the other half, or more than half of the students, are from anywhere and everywhere. So we have an application, we send out calls for students to come to the program, and we get applications from all over the country and world. Most of the students are thinking of themselves as potential graduate students. They’re all undergrads, of course, for the program. So they apply. We get quite a few applications from students… again, all over the country and world… and our HR department has been just wonderfully supportive in granting visas. So we usually have three or four international students. We’re able to get visas to come in and do the program, so we really have a global population. That’s very exciting. But we actually start with the faculty. So the first thing we do is send out a call for proposals from faculty to teach in the program, and that just goes through regular like philosophy calls for papers or calls for conference presentations or something like that and we just send it out on the listservs and post it on the websites that are useful to get philosophy teachers. And then some years we only have, let’s say, 10 or 12 applications to teach in the program. Some years, we’ve had as many as 50 applications from faculty all over the world to come and teach here. We offer three classes a year, so we’re really looking for three creative, interesting class proposals from folks who want to come here and how they fit together. We have a committee that evaluates them, and so we get the three courses, and then once we have the three courses down, then we advertise for student applicants. Usually in the fall, we get the courses settled, and then over the winter, we get the students coming in.

John: How large are the classes? And when students sign up, do they sign up for just one course for the entire two-week session, or do they move from one course to another?

Russell: I’ll just say a little more about the overall structure of the program. So the three courses run simultaneously every day for the two weeks, Monday through Friday. So there’ll be 10 class sessions for each of the three courses. All 20 student participants will be in each of the three classes. So those classes are typically 90 minutes, sometimes we run them two hours. So the students are in class a lot during the day. The program is not graded. They’re not for credit, these classes. They’re really just for the love of philosophy. The students, they’re working hard, but they are not typically doing a lot of outside work. There’s not a lot of assessment of their work during the program. So we exhaust them, but they kind of like it. They come here because they love philosophy, they love trying to do it here. We warn them about it, “you’re going to be exhausted, you gotta be careful.” But they’re so excited to be with each other, to just be in a community of people who want to do philosophy together. They push through.

Rebecca: Can you talk a little bit about the support available for faculty? Because you mentioned that this is an opportunity for faculty to experiment. So, can you talk about that aspect of the program?

Russell: Absolutely. When I was designing this program, my fantasy was having three excited instructors coming in here, and we would all talk about philosophy teaching all the time, and I would be in all the classes, and we would go to each other’s classes, and in the evenings, we would talk about them and think about and work collaboratively on that. And then I discovered very quickly, as we started to run the program, that directing the program meant that I just had to be wandering around with buckets of water putting out fires all the time, so I didn’t have a lot of time to sit and talk with the instructors about what they were doing. I got to go to some of the classes and see what they were doing a little bit, but then we had a student bit by a bat one time, and then I had a run around Utica trying to find how to get her rabies shots. This year we had a power outage, we had two tornadoes touched down locally, and then Hamilton College was out of power for the first two days. It was 96 degrees, 98 degrees. There were no fans, let alone air conditioners, and so we had to move everybody to a hotel for a couple of days. And these things happen when you bring these people together. And I thought we would just be sitting around talking teaching. In terms of running the program, it’s me, and I’ve always had an assistant director working with me. That has changed over the years. It started as an undergraduate who I thought would just help me, I don’t know, make photocopies or something, and then that turned into a real leadership role as well. This year, I had three assistant directors, an assistant director and two student assistant directors. So there’s a lot of work running the program, just making sure people can get to where they’re going. And we’re also ambitious about developing the program, putting in extracurriculars and other kinds of activities that build community, activities that expand on the work done in the classroom. We do field trips and have visiting speakers here, and there’s all sorts of stuff that we have to arrange. So a few years ago, my assistant director at the time said maybe we should hire someone to do that job. And so we put together a grant, and we got a grant from the American Philosophical Association to hire what we call a pedagogy resident, and that’s someone who works with the instructors and the tutors. We haven’t talked about the tutors yet, I should mention them, to develop the courses over the years. So by November, we really have the courses picked, and we have a pedagogy resident who’s an expert in philosophical pedagogy. And this year and last year, we were very lucky to have Emily Esch, who is a professor of philosophy at The College of St. Benedict and St. John’s University in Minnesota, and she’s been working with instructors and tutors in preparing the classes and thinking about what kinds of classroom activities would be useful in this context, and refining them, and just thinking about teaching in general. This year also, we sent around a new book on teaching philosophy called The Art of Teaching Philosophy, and we sent that to all our leadership, our instructors and tutors and assistant directors and the pedagogy resident had conversations about that, rooted in that book, just having them think more about pedagogy. So it’s been a real learning experience for everybody now that we’ve included the pedagogy resident every year. We had the grant for just the first year, and then we got to include it in our budget. So on the run up, the time preparing for the program, as the instructors are refining their plans, they’re working with the pedagogy resident, as the tutors are preparing together with the instructors, they’re working with the pedagogy resident and some use the pedagogy resident more and some less. Once we get to campus, the pedagogy resident is sitting in on classes and working with the instructors just to process what’s happening and to think about how to take it back to their home institution.

John: When I saw a report on this, it reminded me of a couple of programs I’ve been working in. I taught at the Duke Talent Identification Program for gifted high school students from 1987 up until 2020, and then I’ve been teaching in the Duke Pre-College program, and one of the things that I’ve done in both of those programs is I’d often experiment with things before I tried them in my regular classes and it sounds like this is a great opportunity to do that, where the students aren’t really put at much risk, and they often appreciate the variety in the instruction, because they often don’t see that in their regular classes. Are faculty in this program often then taking what they’ve experimented with here and then going back and using it in the classes and perhaps sharing it with other professors after this experience?

Russell: John, you get us. That’s exactly what we’re hoping will happen, and it seems like it’s happening. Yeah, we have immodest aims. We want to change the world of philosophy instruction, and we’re hoping that people are going back to their home institutions and working, not only teaching their classes, but talking to other people about the work that they’ve done here. We’ve also been encouraging our faculty to disseminate the work that they do here and we’ve been successful in a couple of ways. We’ve had some people publish the work that they’ve done at the HCSPiP in journals, teaching philosophy journals. I’ve had a piece coming out in December that I co-authored with one of the instructors here, and one of the students who was here, who is now going on to graduate school at TC at Columbia. So that’s been very satisfying to see the work start to filter out from here. We also have been pretty successful because our student population is pretty ambitious, some of them do go to graduate schools, and we have one student who was in the first year of the program in 2018, she’s just finishing up her PhD now, and she’s got a position at Harvard, and she was at Wesleyan last year, and I think she’s at Harvard now, and bringing that kind of interest in pedagogical innovation to what is often… you know, philosophy classrooms tend to be kind of staid and stiff a lot, and we’re trying to really make them student centered and active learning experiences. So I think it’s happening, I don’t really have good measures of how much it’s happening, except that I know that I’m in touch with a lot of the people who’ve come through the HCSPiP and I hear from them about what they’re doing, and I know that I’m doing it myself. So this article that I just mentioned that we’re publishing, Ashley Pryor from University of Toledo came here, I think it was summer of ‘22, and she taught a class on philosophy of comedy. So the class was, in the first place, some philosophical theories of comedy. There was some dry stuff, but then she’s also an improv artist, and she brought improv activities into the philosophy classroom. She had the students outside just doing all sorts of activities. And I thought this was fun, the students loved it, and it did have some philosophical content to it. And then the next year, I was teaching a philosophy of education class, and I had as a course mentor for that class, one of the students who had gone to the HCSPiP, and now she was a senior at Hamilton, and she was working with me with the first- year students. And she was like, “why don’t we bring some of that improv into the classroom?” And I was like, “Well, I’ll try some!” And so she and I worked together on some activities that were useful philosophically in the classroom, partly to build community in the classroom and partly to enable some quick thinking. In philosophy, we’re often thinking very carefully and slowly, but there are moments when it’s useful to think more quickly. And so that was really great. I noticed it was working just as I wanted it. Yeah, bring people in with ideas, and then I would steal them. I’ll say this, every good teacher is a thief. We see other people doing like, “I’m gonna steal that and make that mine.” So that’s what I did. I started taking some of these improv activities and making them my own, turning them into productive exercises that I think helped to build trust in the classroom. And I think this is something that we’ve been thinking about a lot over the past few instances of the HCSPiP is, how do you help students to dialog across disagreement? I know we’re all thinking about this these days. How do we help students really engage each other despite the worries they have about how they might be judged for what they say, and how do we get them to speak honestly and really work out honestly their views? And I think that it’s important to build trust in the classroom, and so sometimes you have to build trust in various ways. So I found that the improv activities sort of helped to build a little trust in community that then we could take advantage of that in having conversations that are more difficult. So anyway, I do know that I’m not the only one who’s learning from the program, but I don’t know that I have good ways of talking about how much that stuff gets used after they leave here.

John: One of the things I really like about that is the fact that some of these students do go on, and one of the things we talked about a lot on our podcast is how so many faculty come into teaching, emulating the people they’ve just observed. And often in graduate school, in particular, there’s not always a lot of innovative teaching or very student-centered teaching approaches, at least in many disciplines. So it’s really nice that there’s this opportunity for students to see this before they go on to grad school, and it sounds like it provides a wonderful experience, which could be used as a basis for them getting started later.

Russell: So I like to say the first error of teaching is “I learned it this way, that’s the way I’m going to teach it.” And I think we all have to recognize as instructors that we were typically exceptional learners, and our students won’t always be exceptional learners. So how do we reach our students in the most effective ways possible? And that requires thinking in ways that we were not trained. And of course, in philosophy, I’m sure this is true in other disciplines as well, there’s very little incentive in graduate school and in the early stages of one’s career to be creative and innovative about our teaching, especially coming out of R1s. Most of us come out of R1s at graduate school by definition, and then our student body isn’t the ones that we’ve been trained to teach. So we have to look in other ways. Let me say this more about the structure of the program. For every class we hire not just an instructor, but a teaching assistant or a resident assistant. These we call tutors, and they play both a role in the classroom and outside of the classroom. All of the tutors are graduate students in philosophy. Some of them are alums of the program. Some of them are alums of Hamilton College. We try to use that tutor position as a way of helping continue with our network of folks. They get a lot out of the program. Part of it is just working with more experienced instructors in the classroom. Part of it is just being around people who care about teaching, which is not necessarily what they’re getting in graduate school. And part of it is just being in that context. Two years ago, we had an instructor pull out of the program at what was pretty close to the last minute. Maybe we had a month to go, and it was a personal problem, a family issue, and it was perfectly understandable, but it left us sort of struggling, “what are we going to do with this third class?” And the tutors said, “Why don’t we teach the class! And we’ll reconfigure the class in a way that’ll be useful for all the tutors,” and they were all so eager to take that on. I have, as a research interest of my own, team-based learning. I’ve been working a lot on team-based learning in philosophy, and so we decided to run a team-based learning class in philosophy. I kind of structured the class, and then the tutors took it over and taught the class, and they learned how to design team-based learning activities. In team-based learning, there are specific ways in which activities are constructed, and so I taught them all about how to do TBL activities, and they just took it and ran with it, and that turned out to be one of the most successful classes we’ve ever had. The students loved it. In part, I think the instructors were a little younger, a little closer to their age. They’re very excited about philosophy, they’re in graduate school. So the students were looking at these folks who are like in these positions that they were imagining themselves going to be in soon. They were hoping to go to graduate school, just like these folks. And then everybody was in there being a little vulnerable, learning together, and it was a great context and one of the most successful classes we’ve had.

Rebecca: I’m glad you picked up on talking about the tutors, because you had mentioned them earlier, and I was finding that really interesting and it seems like that’s actually an integral piece to the project, because it’s a conduit between the instructors, graduate school, and undergrads who are looking toward graduate school. So they’re kind of right in the middle [LAUGHTER] like a linchpin to the whole thing.

Russell: Exactly. And as I said, they have roles in the classroom, and different instructors use the tutors in different ways, sometimes their role in the classroom isn’t as robust as it is in others. It depends on the class and each of these classes is different. It’s the brainchild of an instructor, and they may not have thought about this in terms of how to use a tutor. And these are 20- person classes, so it’s not like we need TAs to be managing the classroom. So they have classroom roles, but they also have community roles, let’s say, outside the classroom, in helping the students to prepare for classes, to manage some questions the students might have about the readings or how to prepare, or what’s expected. But also, this year it was very hot, and this year the tutors wanted to do a water balloon… they called it a water balloon fight, I said, “I’m not going to endorse a fight, we’ll do a water balloon festival.” So this is a running joke this year, that they were having a festival. So I got a hose and a bunch of water balloons, and they were out there just trying to beat the heat on campus, and the tutors played a big role in that. So there’s some community building and some classroom work. And also, one other thing we do with the tutors is, on the middle weekend, we have the tutors run a session on philosophy after college and what it’s like to apply to graduate school, and how you might find philosophy in your lives other than graduate school, and we don’t have any instructors or leadership go to that, that’s just the tutors and the students. They have some open conversation and some hard conversations about how difficult graduate school can be and how difficult it can be to find an academic career. And sometimes students come away a little dispirited, but I think they’ve gotten an honest view from some people who are in the middle of it.

John: In a program like this, there are some costs for the support, for feeding the students, for providing housing, for providing air conditioning when it works, or water balloons when it doesn’t. How is this program funded?

Russell: So the Truax Fund for Philosophy. It was created in the 1950s by some Hamilton alums. It was an Hamilton alum from class, I think, of 1907 in honor of his father, who had been class of 1875 and there’s an endowed fund. And we are very grateful for the largesse of our forebears to have given us this money. It’s not easy to spend money in philosophy, [LAUGHTER] and the fund is quite robust, and so this is where the program started. We were just very lucky to have this money that we can support people. So let me say, not only is it feeding them and buying water balloons, but we also pay for all travel for all participants, and every participant gets a stipend, which we call a fellowship scholarship for the student participants, because we want to make sure that the program is open to anybody, no matter their financial situation. We pay for all transportation from anywhere in the world and back, and we pay everybody a fellowship scholarship for being here. So it’s quite an attractive thing for students if they have i nterest in philosophy, and they are very grateful, especially some of the international students. That money, which is not a lot of money, the students get $750 for the two weeks of participation here. We see that money as salary replacement for the time that they’re here. But for some of the students, that’s quite a lot of money for them and we heard from one student that it really funded his studies for the next two years. So that was so exciting for us, that he could be here and be such a great participant and also benefit, not just intellectually and pedagogically, but also financially from the program. I wish there were a better recipe for other people to do it, because it is quite expensive, and finding the money to run a program like this would be a little bit of a challenge, I know, for others. But I will say I think we could run a great program without all the money. I mean, we need to feed them still, obviously, but we’re glad that we are able to be as inclusive and diverse as that money allows us to be.

Rebecca: It’s nice when you have the kind of funding that will let all that stress to go away and you can focus really on the subject matter of the program. I really love all the layers of mentorship that you’ve described in the program, the mentorship from the pedagogy expert, the mentorship from the faculty, the mentorship from the tutors [LAUGHTER] right to the students. There’s so many layers there. Can you talk a little bit about some of the reactions students have had from the opportunity to interact with so many folks in the discipline?

Russell: I also hope that I have some mentorship role as well. [LAUGHTER]

Rebecca: Yeah, I didn’t mean to not include you. [LAUGHTER]

Russell: No, it’s very funny, because I really do, and sometimes the program, it feels like I’m just working behind the scenes and I don’t have an active role. But last year, not this past summer, the summer before this, I got COVID in the middle of the program, and I had to work remotely for the second half of the program. And I think I have some role in trying to foster a kind of supportive atmosphere that I think that’s very helpful. But yeah, okay, this wasn’t about me, this question. This was about students and how they find their mentorship. Look, one of the tutors said to me this year, this was his second year in the program, and he said he learned more in his two weeks with the program… the first time he was with it… about teaching, then he had his whole life all put together. And that was such a rewarding comment for me to hear how useful we’ve been. But Hamilton College, we typically send about three quarters of a student to graduate school every year, in other words, less than one a year. And this past year, we had four students who were thinking about doing graduate school and are looking at it. We’ve never had those kinds of numbers. And what happened was at the HCSPiP, they started seeing how exciting philosophy could be, especially when taught creatively and thoughtfully, and it just really was inspiring for them. So I see that in the increased interest in their classes when they come out of the HCSPiP and they come back. We had a rising sophomore a few years ago who was interested in philosophy, but kind of not going anywhere. He wasn’t the greatest student, he had, the interest in philosophy, but not quite the skills. And we saw coming out of the HCSPiP, how motivated he became and how sophisticated he became, especially being around some of the older students, not even the tutors and the faculty, but just some of the older students. And that’s one way in which we sell to our administration that we should use this money for this summer program is because it really does benefit the Hamilton students who participate. We can see the academic sophistication just grow by leaps and bounds in some of these students after they come back after the HCSPiP. We also had six students from last year’s program in the fall, they did their senior theses, and then they presented them at the APA at the eastern divisional meeting of the American Philosophical Association in January. And we’ve never had that kind of interest in professional philosophy and real academic scholarship. So we see it with the Hamilton students. For other students who come from wherever they’re coming from, this year, we had two students from Poland, we had a student from China, and we’ve had students from Indonesia recently, Colombia. We certainly hear from them that this was an amazing experience. We do some assessment. We have a robust survey at the end of the program, and we ask some questions. We’re looking at these results longitudinally and thinking about the program and how to improve it. But often we see them briefly, and off they go, and we hope to hear from them later, I will say, at the APA this past January where we had all the Hamilton students there, I noticed that there were like a dozen, maybe 14, HCSPiP alums who were on the program at the eastern divisional meeting of the APA, and so I found money to take them all out to lunch, and we had a little HCSPiP reunion there. And we started hearing from people. where they were going and how they were using it and how they were carrying in the hearts, the ideas about innovative, student-centered pedagogies.

John: Now you’ve talked about a number of specific methods, team-based learning, improv and so forth. What are some of the other interesting strategies that faculty have tried exploring in this program?

Russell: We’ve had some great classes. Not every class is super successful, but we learn from every class. A couple of years ago, we had a class on Hobbes and Minecraft. So this is Hobbes’s political theory. All the students got Minecraft accounts, and the class days basically took place on the Minecraft platform, and the instructor, Rebeccah Lieby, was asking the students to test out Hobbes’s claims about the state of nature. I’m sure you’ve heard the phrase that “life in a state of nature is solitary, poor, nasty, brutish, and short.” So she was like, “well, let’s see what happens in the Minecraft environment, and test Hobbes’ claims about that.” And so that was super fun. And really, the students really rose to the nasty, brutish, and short aspect of Hobbesian political theory. So I think there was some confirmation there. Last year we had an instructor, Sara Uckelman, from Durham in the UK. She was testing fiction writing as a philosophical method. They read a little philosophy, but what they were really doing in the class was writing fiction as a way of exploring philosophical themes, so that was pretty cool. It’s not a standard pedagogical tool to have students writing fiction in philosophy classes. One of our early very successful classes had students doing a lot of metacognition, so they were reading some really difficult work. They were reading George Yancy’s Backlash book, I don’t know if you know George’s “Dear White America” letter, that he ended up getting a lot of backlash, and he came to the program and visited with us and participated, and also Susan Brison’s account of her rape and assault. So they’re really, really heavy, difficult material. And when you’re reading that kind of heavy, difficult material on racism and sexual violence, you have to be careful about what you say in class, and students have to be careful. So the instructor was Ann Cahill from Elon University in North Carolina, she had developed these both hand signals and little note cards that allowed students to signal what kind of conversational move they were about to make before they made it, and just helped students to process what they were saying and to help other people see, are you presenting an illustration of what someone else said? Are you presenting more supporting arguments? Are you presenting a counter argument? Are you raising a question? There are all these different kinds of moves that can be made in a philosophical conversation, and sometimes it takes a while for students to understand what someone else is saying, because they don’t understand what kind of conversational move they’re going to make. So they had signals for what kind of conversational move that was going to be. That was really an exciting class, one of our most successful, not that I’m playing favorites or comparing, I’ll say one more. The first year we had someone who was a philosopher as well as an artist. Her name is Juli Thorson, she’s emeritus at Ball State University, now she is, and she had drawing activities that were related to questions about personal identity. So they were reading philosophy of personal identity and then doing these drawing activities for students to use the other side of their brain in processing these kinds of questions about themselves. I can tell you about more, but those are pretty good examples of really exciting stuff that is not your typical philosophy lecture.

Rebecca: One of the things that was mentioned in The Chronicle article was that a professor who had been participating in the program noted that in her regular classes, she was struggling with students submitting AI-generated work, as we all are.

Russell: Yeah.

Rebecca: Do you have any ideas of some strategies that philosophy faculty have been using to discourage the use of AI as a substitute of learning? With all this experimentation going around you, we’re eager for ideas.

Russell: Look, I wish I had a secret key that would fix things, but here’s the way I’m thinking about it, and I think this is what I bring to my own classes, and that I’m trying to do at the HCSPiP: learning is change, it’s changing the way you’re thinking, and change is hard, and so we have to start by acknowledging that real learning, real honest learning, done with real integrity, is not easy. I mean, some things are easy, but philosophy, not so much,[LAUGHTER] because we’re asking students to really dig into their core beliefs and examine them, and that can be scary for people. On the other hand, philosophy, when done well, is a lot of fun. It’s a social activity, it’s communicative. We have this image of philosophy as like the lone, great man speaking words of wisdom, but that’s not really how philosophy is done. Philosophy is done in community, in dialog, hearing various perspectives and listening to them and allowing your own preconceptions to be challenged. And if it’s done in a threatening way, or one in which there’s not trust in the community, then students will shut down, and then they’re going to go on and they’re just going to do whatever they can to get their grades. And of course, they’re going to turn to whatever resources are rational, like these AIs. In some ways. I understand why students do this. They’re overworked and anxious, and it seems like a rational thing to do, but what we’re trying to do is make philosophy classes more interesting, more engaging, more student centered, and more fun. And if we do that, we’re hoping that students are going to respond by saying, “Oh, yeah, this is fun stuff to do, and if I trust my community, I could really like grow” and look, students are skeptical about received wisdom. I mean, we see this all over. They’re skeptical about being told what to do. And so if they’re empowered to really think for themselves, maybe they’re really gonna actually try to think for themselves. So that’s what we’re trying to do in this program, is to change the way people think about philosophy and humanities more generally, from like, learning the received wisdom and regurgitating it to like, “Oh, here’s a chance for me to really think about these questions and to think about what people are telling me, and to develop my own views about it.” And that’s what student-centered pedagogy is all about. So it’s not a way of policing against the AIs, it’s a way of undermining the motivation that students have for turning to the AIs. We are very lucky at Hamilton College to have an open curriculum, and what that means is that students do not have to take any classes they don’t want to take. We don’t have a gen ed program. We don’t have required classes. So on the first day of class, almost all students there are a few exceptions, but almost all students are in classes they want to be in, and so that’s an opportunity for us to say, “Okay, you want to learn about this stuff, let’s do it in a way that is going to be useful and productive for you, so that when we give you assignments, these are assignments about things that you want to learn, that you’ve committed to trying to learn about, and they’re going to help you, rather than just be assessments of did you file away enough information and regurgitate it back to me.” It’s about assignment design. Can you make assignments in such a way that students see that they are for this student’s benefits, rather than just ways of policing and testing them.

John: That’s a challenge we’re all working with, we’re all facing, and that’s in general, the sort of approach we encourage people to use, giving students more autonomy and being transparent about the benefits they get by engaging in this themselves, rather than just outsourcing their assignments to some external tool.

Russell: Yeah, yeah.

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

Russell: So this year, we broadened our program by having early arrival and orientation for our international participants, and that was really productive. And every year we’re thinking about ways in which we can improve the program with trying not to extend ourselves financially or too much personally. There are some sustainability questions, both the money and my own ability [LAUGHTER] to work over the summer on this project. It’s a lot of work, so we’re moving to an every other year approach for sustainability reasons, but mostly the big project that we’re going to work on this year, while we’re taking a year off, is to develop more courses led by graduate student tutors, with help from the experienced faculty because I think that was the place where we realized that, and you caught this, which was great, that that kind of mentorship of the graduate students was something that we’ve done quite effectively, I think. And so we’re going to be writing some grants and thinking about, how do we expand the program in such a way that we can give a little more space for graduate students to be developing their work? But I think the core of the program is really very good. We had a great year again this year, and we’re just going to gather our strength and do it again. I wish we could expand it to more people. And it’s not even the money that is the big barrier, It’s just It would be so much more work and not sustainable.

John: It would be really nice if more disciplines had this. I’d love to see something like this in economics.

Russell: I would love to talk to people, if they’re thinking about developing these kinds of classes. I have a lot of resources. I’ve developed this stuff ground up and anybody who wants to try something like this out, I’ll say this, several of our alums, that is, people who were students in the program have gone on to graduate school and started summer programs in philosophy for high school students, and what a wonderful testament that is to our success. They’ve taken from what we’ve done here, and they’ve sort of applied it in different contexts. So I think the way in which we create a context for learning that’s not tied to grades and credits and just for the love of learning, that’s been a real joy.

Rebecca: Well, thanks for sharing your program with us. It’s always nice to hear about these opportunities for learners to be excited about their discipline.

Russell: I really appreciate you inviting me on, and I’m always happy to talk about the HCSPiP, so thanks for the time.

John: Well, thank you. We really enjoyed this conversation, and we look forward to talking to you at some point in the future as well.

Russell: Great. I’d be happy to come back and talk about team-based learning or something. I’m writing a book on team-based learning this fall as I’m on leave, and that’s my plan.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on iTunes 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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407. Covering or Teaching

New faculty often begin their teaching careers by emulating the teaching methods that they have observed, but these practices are often not consistent with evidence on how students learn. In this episode, Chris Hakala joins us to discuss the role that educational developers can play in shifting faculty focus from presenting to teaching.

Show Notes

Transcript

John: New faculty often begin their teaching careers by emulating the teaching methods that they have observed, but these practices are often not consistent with evidence on how students learn. In this episode, we discuss the role that educational developers can play in shifting faculty focus from presenting to teaching.

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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 Chris Hakala. Chris is a professor of psychology and is the Director of the Center for Excellence in Teaching, Learning, and Scholarship at Springfield College. He is the author or co-author of numerous books and articles on the science of learning, assessing learning, large-class instruction, and on teaching centers. His most recent work, co-authored with Leslie D. Cramblet Alvarez is Understanding Educational Developers: Tales from the Center, released in late December 2024. Welcome Chris.

Chris: Well, thanks. Thanks for having me. I’m really excited to be here and have this conversation with the two of you. So I appreciate it.

Rebecca: Today’s teas are: … Chris, are you drinking tea?

Chris: So this is an interesting thing. I’m routinely a coffee drinker. However, my wife and I every morning get up in the morning and we share time together. We actually have a station in our bedroom where we make coffee and tea. And so in honor of my wife and the tea, I’m drinking her tea, which is a Zen green tea, which I will sometimes drink as well. So yes, I’m having Zen green tea this afternoon.

Rebecca: Excellent. Thanks for ditching the coffee for us.

Chris: Happy to do so, happy to do so. I’ve had enough of it already today.

John: And I am drinking a spring cherry green tea, even though it’s late summer here in Oswego.

Rebecca: It does feel more like a spring day today, though, after being so hot for so many days. I have a weird mix of random black teas because I was cleaning out my tea cupboard, [LAUGHTER] so it’s an eclectic mix.

Chris: Whatever gets you through the day, right?

John: I imagine no cherry teas, though.

Rebecca: No, they are black teas without any weird flavors, John.

John: We’ve invited you here today to discuss the relationship between pedagogical practice and what research tells us about how students learn. First, though, could you tell us a little bit about your path to becoming an educational developer.

Chris: Sure, it’s an interesting path that actually we talk about a little bit in the book, but one of the things that is fascinating to me about this field of educational development is it’s not a field people often go directly into out of college. And so I went to college in Vermont, at a small school in Vermont, went to grad school, and in grad school, I earned a PhD in cognitive psychology from the University of New Hampshire. And I know we’re going to talk about this a little bit more later, but I just want to point out that during that time, we had a course in a seminar in practicum and teaching of psychology, and that course was taught by a faculty member named Victor Banassi, who was doing teaching center work at that time. So it was the first time I began to hear about educational development. It was back in the mid 90s, and so I thought it was kind of interesting, but I went on, I became a faculty member, I received tenure, and then as I was working through my career, I began to notice that many schools had teaching centers. Many schools had the opportunity for faculty to talk to other faculty about what they’re doing and to share ideas. And so I approached our Provost and asked him if we could start a teaching center. And he said, “No, there’s not enough money.” And so I approached him again, and he said, “No, we don’t have enough money.” And finally, I was able to steal an idea from Todd Zakrajsek, who is a name you are probably aware, and Todd recommended me just buying coffee mugs that said Center on it. So I did. I bought coffee mugs that said Center for Teaching and Learning, and I had them branded, and then I handed them out and gave one to the President, and the President said, “Do we have a teaching center?” And the Provost said, “Well, I guess so.” And so they gave me a little bit of money, and so I had this very small teaching center. I would do a little bit for faculty orientation, I’d run some workshops, and it became really interesting for me to see how sharing ideas with faculty about teaching and hearing what they’re saying and what they’re doing and what they’re experiencing with their students was a really wonderful way to engage with my colleagues, to go across disciplines and to really sort of work to improve our students’ learning. So I left there and became a full-time center director in 2014, and I’ve have been doing that ever since. It’s been an interesting process, because as we were speaking off-mic, in 2020 our world changed dramatically, and teaching centers became very important. And it was a really interesting time to see all that work ramp up. And I think now we’re in a different sort of era of teaching centers, as people began to realize the value of educational development when they needed us. Yeah, so it’s been an interesting journey.

Rebecca: I see some sales of teaching center mugs going up [LAUGHTER] with the sneaky strategies for starting teaching centers, moving forward. [LAUGHTER]

Chris: That’s all Todd. It’s all Todd.

Rebecca: He’s always good for a good idea.

Chris: He is good for a good idea.

Rebecca: Most faculty begin their teaching careers without the kind of training that you mentioned that you had as part of your graduate program in evidence-based teaching practices and kind of rely on what they experienced with their own instructors. What are some of the disconnects between learning science and pedagogical practices?

Chris: So it’s a great question. It was really interesting. I’m gonna go back to go back to my experience and sort of talk through that, because I think it’s a great question. I went into my first experience teaching in 1992… by the way, I still have a video of it. I had spectacular hair. It was wavy and flowy… and I went in with this idea that I was now the disseminator of knowledge. I was going to share what I knew with these students, and I did what I had experienced the faculty that I admired do. I lectured. I had an overhead projector, I wrote on the board, and my students were doing the tasks I was asking them to do. They were studying. They were doing well on quizzes and all of that. But what became really apparent to me over time was student learning is not as simple as they just remember things, because there’s more value in students taking what we teach them and expanding that out to other concepts. And so I began to think about it in lots of different ways, and my teaching now looks very different. One of the things that becomes problematic, though, is, as you brought up, Rebecca, many faculty end up in a teaching experience without having any kind of guidance or support. They have very little in the way of reflective teaching, and they think their job is to just share what they know with their students. I hear “covering content” so much from faculty with this idea that they have to make sure they say everything. And I think that helping faculty understand that the magic words coming out of your mouth doesn’t mean a student learns it is a really important thing. And one of the distinctions I draw up with my faculty all the time is this distinction between presenting and teaching. And so I think a lot of faculty are good at presenting, but they haven’t really thought through teaching. And so a lot of times I spend time working with our faculty to talk to them about who they are and what they bring to the table as an instructor, who their students are and how they can begin to find what is going to leverage what we know about student learning in the classroom in the context of who they are and who their students are. So for example, I tend to be pretty expressive in class. I tend to move around a lot. I tend to do lots of interactive things, but that’s also part of who I am. You don’t have to be that to be successful. You can be lots of ways in the classroom, but it’s a matter of understanding that the processes I’m engaged in are to help students focus attention, to help engage them, to draw them in with different anecdotes and stories, to try to tie those things together, and then have them reflect it back to me so we can work through that. People have different approaches, but you have to sort of to be aware of that and know that the student learning is essential. The key to student learning is that they do the work. It’s not that you’re doing all of the work. They’re doing the work. And so you have to help faculty sort of navigate that. And it is fortunate for me. I’m at a small college where teaching is really emphasized and faculty care about it, that I get to work with wonderful people and help them understand some of the things that we know about human learning, and help them sort of integrate that into their classes.

John: And we have lots of faculty who are very interested in improving their teaching, and we also are a teaching focused institution, but there are some faculty who are a little reluctant to change their practices, or consider changing their practices. What are some strategies that you’ve used to help reach out to those faculty who may not see the benefits of changing the way in which they’re teaching.

Chris: Yeah, the recalcitrant faculty problem. It’s a big one. I have had a mixed success. When I arrived on campus, I was fortunate to have someone here who handed me a list of people. On one side was “these people will come to everything you offer. Don’t worry about it. You could roll out a boring thing, they’ll show up.” “These people would come if it’s interesting.” “These people will never darken your door.” And so I went right after the people who would never darken my door and went and had a conversation with them about their teaching. Now, this doesn’t scale up to a big institution because there’s too many people, but in a small institution, it was really effective for me to go talk to faculty, find out what it was they were doing in the classroom, ask them if they were happy with it, and start to look at things in their classes they might want to work on. Now, again, you scale it up to a big institution, you start with departments and ask the department, “what would you like support with?” But one of the things that’s been really helpful for me as a center director is showing faculty things that they can implement, that they can see the impact on, and it’s been simple things. One of the things that our faculty in our health science really like is that I taught them about retrieval practice. They had not really thought about retrieval practice, and so now they’re using tools, including some AI, to build out many little quizzes the students can take on their own that seem to be benefiting the students’ ability to do well on these very high-stakes exams. So it’s a matter of finding the niche where the faculty member’s issue and your expertise can come together. I also strongly, strongly encourage faculty to watch each other teach, which is a really interesting thing. I have an open-door policy. Faculty can show up in my class anytime. It doesn’t matter when, they don’t have to tell me, they can just come. And I have 5 to 10 faculty a semester show up in my class and they just are there, and we talk, and then I go to lots of other faculty members’ classes, and we have an open-door week where we open up classes for faculty to go to each other’s classes. I think that’s really helpful for people to see lots of different ways of doing things. I have a line that my associate director always gets annoyed with when I say, but I believe this. I have faculty who say to me, “Chris, I’ve heard that if you’re funny, students really like it.” And I say “that’s true, but only if you’re really funny. If you’re not funny, it’s terrible.” And I’ve seen faculty try to be funny and they’re not and it fails. So one of the big messages is, “lean into who you are.” It’s okay if you’re a bit of a dork. I love statistics. My students, I get them on board eventually, but I come in with the enthusiasm of someone who loves statistics. So it’s a challenge, and we’re humans. So John, you know, humans don’t always respond well, when you say, “we can help you do something better.” So there’s a lot of nuance to it. I will say teaching centers should never be where bad teachers are sent. I push back against it.

John: We do too.

Rebecca: I think one of the things that’s interesting about teaching centers, and what you said about faculty not having support, is that actually a lot of institutions have teaching centers, so there is the availability of support, but getting said support is not built into requirements or time is not necessarily allocated by the institution to participate in teaching center things. It’s voluntary. It feels extra sometimes. So you talked about your list and individually reaching out to folks. But what are some other strategies to get over this hurdle of helping folks realize that this is a good investment of their time.

Chris: We have some events that are campus wide, and we use these events as a way to try to promote options of how you can interact with the center. So to just give you a couple of examples. Our President is incredibly supportive of the center, so twice a year, we host an evening event at the president’s house, it has wine, that goes over well, and we bring in speakers, and we try to get speakers who will hit a variety of different areas of interest for faculty. And we often will get faculty at those events that don’t come to anything else, but then may come to other events, as they’ve seen the value in those events. So that’s been a good one. Diversity of programming has been really helpful, and I’m super proud of this. We run a summer conference. It’s a week after commencement, all faculty are off contract, and we will get roughly 40 to 50% of the full-time faculty showing up at our Summer Conference off-contract because we structure it in such a way that faculty are talking to each other. We’ve been very fortunate having some really, really amazing outside speakers, and then we give faculty the opportunity to do some chatting with their colleagues, get to see people that they don’t normally see, and that’s been another way. Part of it is having a big footprint on campus, Rebecca, and being involved in lots of things. I go to department meetings once a year. My associate director goes to department meetings. But I will say this, our office also has this footprint, because we not only oversee educational development, we oversee all of instructional design, we oversee all online course builds. We also oversee all student research. We also oversee the IRB. We also oversee sponsored research with our staff of two. So it’s hard to have something on campus that doesn’t involve reaching out to one of us once in a while, and that’s been really helpful. But it is, it’s challenging, because those resources are there and they’re often not used as much as they could be.

Rebecca: I think it’s important to ask how you went from a mug to overseeing that many things.

Chris: I switched schools, that helped, and then… I said this to John… this is 33 years of me being in higher ed, and you learn a lot when you’re just around in higher ed. And I’ve been tenured full professor for a long time, and I still teach, and that actually buys me credibility among the faculty, but I also manage budgets, that buys me credibility among the administrators. So that’s all been really helpful. It’s a needle you have to thread sometimes, and teaching centers are interesting because if you look at it, I mentioned before how we were able to show the utility of teaching centers. But as budgets get tighter and things begin to change across the landscape of higher ed, you’re starting to see teaching centers get shrunk and cut again. I know of at least two teaching centers at major institutions that were simply cut and all the people were fired because of budgetary crises. And I’m actually down four staff members right now, which I may or may not get back. So it is a very difficult time financially, and you work for a state system, so you know what that’s like. You look at the state budget. We were private. Stuff just happens in a private institution. It just happens. It’s not easy.

John: One of the things we found has been helpful to reaching out to more faculty is getting faculty who were in that wave of people who were interested in attending more workshops, having them do things and then present on it to other faculty, because the more faculty hear from their peers about how things have worked and made their classes more enjoyable, the more likely they are to show up and to try these things as well. There’s still some areas, though, that it’s difficult to reach and we keep trying.

Chris: Yeah, John, it’s a great concept you’re talking about, and that’s the Summer Conference. I do nothing at the summer conference but serve food. I simply show up and all my colleagues talk to each other. And that’s really the joy of it, because they see other people, and we have a SOTL fellowship program, and so they’ll present their scholarship of teaching and learning work, and the opportunity to do that is often gonna draw people in. It really is a grassroots thing. And one of the interesting things that Leslie and I saw when we were working on this book is that there’s no one way to run a center. It’s deeply contextualized into the context of the institution and the structure and culture of the institution. And some places have had a long culture and a long history. I was the first full time director at my place here, and by the way. I didn’t say this before, but we’re Springfield, Massachusetts. I know there’s Springfields in every state, and I feel like I’m contractually obliged to say, if you know anything about Springfield College in Massachusetts, we are the home of basketball. Kansas wants to claim it, but we are where basketball was invented, so I just have to say that. My president will be proud if she hears this.

Rebecca: These are important details.

Chris: These are important details.

Rebecca: Can you talk a little bit more about how teaching centers can continue to help faculty align their practices so that we’re really teaching students how to learn and building that into the work that we’re doing, and help faculty see value in spending some time or allocating time in their classes to that kind of work.

Chris: Sure, the change in teaching practices over the last 30, 40, years has been pretty remarkable, and a lot of it has to do with the changing demographics of students entering college and the changing demographics of people teaching them. And what we’ve learned as we’ve looked at the science of learning is that human learning hasn’t shifted as much, but there are other outside variables. And I like to talk about, with faculty as we discuss these things, the notion that a student is going to walk into your class, any given student with a number of variables that are impacting what they’re doing in the classroom. Some of them you have some control over. Many of them you don’t. One of the things that you can help them with is a simple concept, such as attention. And I always start with attention, because it’s the one that faculty can begin to sort of wrap their head around. And I say you can help by using the classroom space in a way that is not going to cause them to have other distracting variables pull them away. And then I get the same comment, “well, students’ attention spans have changed.” And my response always is, “Nope, hasn’t. Hasn’t changed at all. What has changed is how many things they can be distracted by.” So you have some control over that. What are you going to do about that? And so it’s a way in, and then I can talk to them about… and because I’m a practitioner who does this research too… I can talk about data. Now, by the way, the data always get the bench scientists. They love that, because I’m talking about we have results that show this. Here’s the data, here’s the results, here’s the interpretation of the results. And things like I mentioned, retrieval practice, things like interleaving, or worked problems, the data are pretty strong that these kinds of procedures are effective. Now what is really, for me, the joy is working with a faculty member to see how that particular process works within their class, with their content, with what they’re trying to do, and try to really personalize it to their approach. One of the ones that my faculty seem to really like is this notion of interteaching. It’s a way to help students sort of structure their reading and so students get questions before they read, and they answer the questions, but they come in with two copies. One copy goes to you, one they sit and they discuss with a partner, and they look at where they answered the questions the same way, they answered them differently. They talk about it, and then everyone comes back together and talks about it. It seems like they’re going to not say much to each other, but they actually get pretty competitive about who said what on these things. But the real joy is not that they get it right or wrong, but it’s rather that they’ve thought through these things and now they’re engaging with the concepts in a different context. And I always talk to faculty about the key is to get the students to not just hear it, but to do something with it, to engage with it. And so how you structure that is the most important piece in terms of that they’re doing it, but how you do it is going to vary. So I think that that’s where I get some buy in. But I will say, Rebecca, it causes people to have to change their classes and they don’t like it, because you have your slides and you have your presentation, and you know what you’re going to do, and this opens up the probability, and I say, probability, not possibility, that class is going to be derailed sometimes. That’s okay. I love to tell faculty I teach two sections of intro sometimes back to back, one at eight and one at nine. I might be two chapters apart in those two classes because they’re going at different rates, and I’m okay with that.

John: So you encourage faculty to build in some forms of retrieval practice and interleaved practice, spaced practice and so forth. But sometimes students react poorly when you build that into your courses. Do you have any strategies you share with faculty in terms of getting buy-in from students? Because students, in general, in a number of studies, prefer to be lectured at, but we do know they learn more when they use evidence-based strategies. How can we get students to buy into that?

Chris: I’m going to tell a story while I talk about this. Part of it is being incredibly transparent about why students should be doing these kinds of things. And I think that’s pretty obvious, but I’m gonna tell you a story. I did this study at a different institution, and we never published it. We had two sections of neuroscience, and they both had 60 students. So we had 120 students total. And for the students, for exam one, the students were told in class A, we’re going to teach you how to concept map for studying. So we taught them how to concept map. Class B, study however you want. For exam two, we flipped it and we flipped it all the way through, and what the students found was when they concept mapped, they tended to do better than the other class, and then on their next exam, they did better when they concept mapped then when they didn’t concept map and we showed them the data. We showed them students tend to do better. For the final exam, we told the students, “Pick whatever you want. It doesn’t matter. You study however you want. We’re done with the study. Just study however you want.” They took the exams, and then we just asked them, “What approach did you take?” And the idea was, many of these students would adopt concept mapping, because it worked. Well, much to our chagrin, the number was so low that it was actually zero. None of them concept mapped. They all said, “I know it looked like it worked there, but my way is better.” So you’re absolutely right, John, it’s really difficult sometimes to convince people of these things. The approach I tell faculty to do is to introduce these things slowly into your classes, to build up and get small gains. So I’m a firm believer in formative assessment at various times throughout the semester, including my intro psych class, we do an assessment every class. It’s simple. It takes a minute or two, but it’s a way for me to gauge where they are. And I share the results with the students, and I talk to them about why the results might look the way they look, and I talk to my fellow colleagues about how to try to incorporate that in terms of how they’re doing it. The students, they do not like when you do retrieval practice in class, yet, if you give them the opportunity to build a Quizlet outside of class, they’re all over it, which is really retrieval practice. And so it’s a matter of trying to have that conversation. And it’s one of those things about human behavior, and you’re an economist, you know this. It’s so frustrating. People don’t do things that are good for them sometimes, and it’s frustrating, but you just keep working at it in a variety of ways. I know we’ve had success by hosting student faculty conversations, where the students and the faculty will talk to each other about what is going on in class, and it helps the students understand why the faculty members do things. It helps the faculty understand why students do things. But at the end of the day, it’s incredibly challenging, and I think it’s one of the things that makes this work. We’re going to be around for a while, because we really need to continue to work. And I will say one more thing, it also doesn’t help that faculty are incredibly resistant. And they’re resistant because you try something, the students don’t like it, you back off of it because the students don’t like it, even though it might take two or three iterations for them to like it. So there’s a lot of challenges there.

John: And we’re also coming up against students who’ve had 12 to 13 years of practice before entering college without using those techniques with high stakes exams and so forth. So there’s a lot to overcome there.

Chris: Yeah, and the high-stakes exam thing, we have a large group of physical therapists, physicians assistants, and OTs (occupational therapists), and they all have this national exam they have to sit for. And so they just want the stuff. They just want to know stuff. And I always try to tell them, you could know a lot, but you have a patient in front of you, and this is the example I give. I say, “If a patient comes to you and says, ‘My arm hurts when I bend it up’ and you say, ‘Okay, here’s what you’re gonna do. You’re gonna do this.’ And then the patient comes back says, ‘I did that and it doesn’t help.’ You can’t look at the patient and say, ‘Well, boy, that’s terrible.’” You have to have another approach. You have to have something else to try. And I try to help them sort of work through that. Again, we’re fortunate here. I do go in and talk to almost all the grad students when they go from undergrad to grad, to talk about the application of knowledge. I also talk to all the undergraduates when they show up on campus to talk about some of these things too, with the idea that if we repeatedly talk to students about it and try to help show them, we may get more buy-in. There’s no panacea to this.

Rebecca: I think what you’ve both highlighted is that there’s resistance on both sides. There’s resistance from students and resistance from faculty, because change requires effort, and establishing new habits takes time and effort. But I like the idea of the dialoguing so that you’re working on change from both directions.

Chris: Yeah, and it’s interesting, I always ask faculty, especially like, if they give an assignment and say, “Why did you give that assignment?” And I’ll sometimes hear, “Well, it was time.” Well, that’s not a good enough answer. If you can’t relate that back to what you’re trying to get students to learn, then the students don’t know why they’re doing it, and they’re just doing it, which is the big issue with AI right now, is faculty are like, “Why are students AI-ing my assignments?” They see zero value. You work towards things that you value. You create value in education, students are more likely to work. It’s not guaranteed, but it’s more likely they’re going to do the work.

John: Earlier you suggested that one strategy you encourage is transparency, so that students understand why you’re asking them to do these things. Might that help in getting students to break the habits they’re developing of using AI for so many assignments now.

Chris: Yeah, I feel pretty strongly about this. I’ve done quite a bit of work in the AI field, working with AAC&U as well as with the American Psychological Association. And I think that the transparency with AI, we just have to do it at this point. What we need to do is lean into teaching AI literacy, teaching students about what AI can do, the limits of AI, showing them some of the limits of AI, and allowing them to see this as a tool, not as learning, but as a tool to help them learn more effectively. John, you were teaching at the time. I remember in 1995 we got essentially a graphic browser called Mosaic, and I could actually see the internet versus just typing things into gopher. And I thought my life has just gotten better. It’s just gotten better, and I was able to do things I couldn’t do before. Well, AI has done the same thing. I can’t tell you how many times n ow, when I’m generating a data set for statistics, I give it the parameters, and it just generates the data for me and it comes out the way I want it to. I used to calculate those numbers, and it was terribly difficult to do. Now it just does it, and that takes the time from that for me to give students feedback or do other things. Well, the students, just like with the calculator, can use it in a way, and I’m not saying it’s a calculator. It’s much different than that. I’m not saying that, but it’s a tool that we can use, if we can figure out how to leverage it. But transparency is key. We are talking right now about creating a guidance document through the center that’s going to be shared with faculty and students about how the center and how academic affairs views AI and the value of it, as well as some of the pitfalls and concerns and some of the caveats that need to be around it. Because I think, even if you hate it, and we have faculty who truly hate it, they truly hate it, it’s not going anywhere, and the students have access to it on their phones. It’s there, so we have to figure out what to do with it.

Rebecca: One of the things I want to underscore about what you just said is that you’re going to develop a guide for faculty and students and that you’re going to share it to both parties. And I think that’s a practice that institutions could just do more generally, so that both sets of individuals are getting the same information, because especially around AI like literacy is necessary for all members of the campus community, not just students.

Chris: Yeah, yeah. We have a task force right now we’re running with my associate director, which involves five staff members and five faculty, and we’re going to cycle in some students so that we can sort of figure out what we need to be talking to people about. Our staff, many of them are afraid to use it because they’re afraid that it’s going to work them out of a job. But we say “No, this is gonna make your lives better if you use this.” So we’re trying to work through some of that. But the transparency is really key. I would argue that for everything, Rebecca. I’m a firm believer in being incredibly transparent with my students about all sorts of things that I think help move the learning process along, and that’s been really valuable for me as an educator and with my colleagues as a faculty member, as an educational developer.

John: And one of the things we’ve been advocating is that faculty have these conversations at the start of the semester with their students about what uses of AI are appropriate to see if they can get some degree of buy in. Do you think that might be helpful in convincing students that they should use it in ways that support their learning, rather than ways that serve as shortcuts to their learning?

Chris: Yeah, my associate director, Matt Citron, developed a system for one of our majors because the faculty really wanted it. And it’s not a new system, people are using it, but they just have a system built into Brightspace where an assignment will have either red or green or yellow light saying: red, don’t use AI; yellow, use it with caution; green, do what you want with it. But even before they see that, there’s a whole conversation about what “use it with caution” or “go crazy with it” means, how you’re going to involve that, and what you’re going to do with it, and how you’re going to report what you got out of it to the instructor, so they know exactly where this is coming from and how it looks. Again, It’s a transparency. But, you know, I’ll say this, I am continuously amazed when faculty are trying to sneak things in and trick students, because learning is not about being tricked, man, it’s about being able to do things more effectively. And I even do this, and faculty don’t love this. But when I give multiple choice… I’d really moved away from it…but a few years back, when I gave multiple choice, I told students, look, if you don’t like my answers, give me your own. If I like it, I’ll give you points for it. Or if you think my answers aren’t good enough, and you could write it better, fine. Write it underneath the multiple choice, I’ll read it. I mean, students don’t often do it, but every once in a while they do. It’s not like I have the magic knowledge, that’s the thing. I just know more about this than you do. I’ve learned more, I can think about it differently than you can, but let’s talk about it. And I have conversations with students after exams. I have conversations with students about their papers. I’ve taken to shooting video feedback to all my students on assignments and their responsibility is to give me video feedback, telling me how they understood my feedback and what they intended to do with it. So it becomes this virtual conversation. I talked to my faculty about that. Teaching and learning is an innately humane relational process, and the more we can continue to build that in, the better off our students and our faculty are going to be. But Rebecca, it’s work. It’s work.

Rebecca: It is work.

Chris: It is. I have two classes. I have 67 students this upcoming semester, as well as running a teaching center. It’s a lot of work. I grew up as a first kid to go to college, first one to graduate. I feel pretty lucky to do this job. I’ve worked other jobs, man, I built fences. This is way better than building fences. This is way better than building fences.

Rebecca: Sounds like we’re trying to take them down, actually.

Chris: Yeah.

Rebecca: One of the things that we’re seeing with budget crises across higher ed is sometimes increases in class sizes, and you just talked about how deeply humane and relational [LAUGHTER] teaching and learning is. Do you have some strategies for student engagement and some of that relation building in those larger classes.

Chris: Yeah, it’s a great question, and it’s interesting because at Springfield, and I’m not sure about the size of Oswego, but for us, a big class is like 60 students. Faculty freak out when they hit 60. And the fact is, I think that relational piece is going to look different depending on the size of your class and how you’re able to navigate that. I think part of it is understanding a variety of ways to give feedback. So I tell faculty, in particular with large classes that I would encourage them to do small assignments and give feedback to percentages of students every class to ensure that every student gets written feedback across time, but they don’t have to get all the same written feedback every time. You would never not read a student’s work, but you would give more substantive written feedback to a percentage of your students, but work in ways to develop those kinds of touch points. It’s not always easy. Sometimes it involves using the LMS, sometimes it involves getting there early, but being aware that your students, if they identify you as a human, they’re much more likely to be kind to you. So it’s really a matter of trying to figure out a way to allow yourself to be perceived as a human in students’ eyes. That’s idiosyncratic to the instructor. My students know a lot about my life. Part of it was I was a single dad for six years. I taught with my kid in a Snuggie. There was no way for my students to not be aware of what I was doing. I had a child strapped to my chest. She fell asleep. I told my students, come closer. She’s sleeping. I can’t wake her up. So I’ve always been pretty transparent with my students about lots of things. It makes me human, and I think part of it is allowing the students to see that you’re human too at whatever level you’re comfortable, whatever level people are comfortable, some people are more private. I get that, but I think you have to allow that humanity to kind of show through. I’ll say one more thing about that. If nothing else, you went and got a degree in this discipline. Show your passion for your discipline. Let your passion for your discipline come through however it does for you. I can’t talk about some psych topics without being jazzed. I’m a psycholinguist by training. I can’t talk about language without being super excited about language. It’s fun. I was just in Europe. I was looking at things written 1000 years ago. My camera is filled with pictures of stone tablets. My wife’s like, “What is this?” I said, trust me, “That’s linear B, man, that’s good stuff, right there.”

John: One of the issues that we do hear a lot of concerns from, especially since the pandemic, although it seems to be a little bit less last year than in the previous years, is students seem a little bit less engaged with some of their classwork, that students are more likely just to blow off some assignments and not complete them. And also, many faculty complain about having lower attendance in their classes. You mentioned using polling as a strategy. Are there other strategies that faculty can use to help increase that connection, or to get students a bit more engaged so they don’t disappear on us.

Chris: Yeah. I mean, the mental health crisis has really had its fingers in lots of this, and students’ anxiety has led them to be less willing to chat in class, less willing to engage in class, and with the devices they get to class, and they used to talk to each other, they sort of look at their phones, and so that’s an issue. I am pretty transparent about what I expect my students to do, and I talk to faculty about stating your expectations clearly and being open to knowing that students are going to sometimes struggle with this. I’ve got a couple of simple little tips. So for example, I give this tip to faculty. Have students answer questions and just write them down on a note card without their name, and then they take their note card and they stand up and they walk around the room, they hand it to someone, they take theirs, and then they take that note that note card and hand it to someone else, and then they take that, and then they just pass them around. And then I can call on any student, and all they’re doing is reading the note card, and so that helps them sort of see that it’s okay. I’ll tell you another secret… my students, whenever they find out about this… they get really mad, but I do this. I collect note cards at the beginning of the semester with all my students’ names on it, and I tell them, sometimes I’m going to randomly call on them to see if they know an answer. And they’re always softball, simple, very easy questions. So I’ll shuffle the cards and call someone. I will almost always call a name that is not on the card. I’ll call the name of the student who I know hasn’t said much, but knows what’s going on. So they have a chance to do something in class, be successful, and shine, and then they’re more likely to talk. And students don’t know what name I draw off the card. It doesn’t matter to me. It could be blank cards for all they know, but it’s a way to get the students… and again, as a psychologist, I look for ways to sort of shape that behavior up. Also, I tell faculty: “Get the class early, greet students when they come in, just talk to them.” I don’t know if this happens at Oswego, but this is a Springfield College thing. Our students thank us. At the end of every class, I get fist bumps as my students are walking by after every class. And I’m not unique. Lots of our students do that, and part of it is a culture that has built up over campus, over time, this is before my time. This is sort of who our students are. And so you work with that culture, okay, if the students are gonna do that, then I know they’re polite. Okay, good. Then I can work with that. Your students might be different. You know what they are. You try to work with that. It’s like I say to people all the time when faculty say, “Well, what can I do to be a better teacher?” I can’t pull a book off the shelf and say, “Here, do this.” I have to know who you are, who your students are, and all of that. And so it’s why the field is so much fun. I get to know lots of faculty, and I get to work with lots of faculty.

Rebecca: One of the things that we’ve been talking a little bit about, are some budget cuts in higher ed and teaching centers being on the chopping block? What are some of the things that you’re concerned about in this space?

Chris: So you don’t want to open that door too much. I really appreciate this in part, because I think there is a sense with teaching centers right now that if we let them go, we’re going to be okay. And I think faculty need the resource, even if it’s a perceived resource. My wife and I talk about it all the time. Perceived help is often as useful as the help you get, and so us existing is really important, and I worry that teaching centers are going to start to shrink, and it’s going to make faculty struggle in their teaching. So I hope that we continue to talk about these things and I appreciate having the opportunity to talk about it.

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

Chris: So what’s next for me? I have an idea for another writing project. I want to look at intentional teaching. I want to talk about barriers that make it difficult for faculty to be as effective as they can be in the classroom, starting with things like lack of pedagogical understanding and going through variables such as student variables, classroom size, physical space, etc. Because I think we need to take a harder look at what we are doing, knowing the stakes are higher than ever at being able to be effective at what we do. We need to be incredibly intentional about creating really strong learning environments for our students to succeed. And I think our livelihood depends on it. Listen, I teach at a small liberal arts college in the Northeast, and have either you ever been to Springfield, Massachusetts?

Rebecca: Yeah.

Chris: So from my office, if there were no trees, I could see two other colleges less than a mile away from me. So there’s lots of schools and fewer students. We just need to be really good at what we do. So I’m thinking about that. The other thing is, I am excited to be building out some additional resources for our faculty that I think are going to support them with short burst sort of faculty development that we’re building with our media team, which has been really fun. So we’ve got lots of stuff coming. We’re excited to get the year started and to do these things and to share them out. I’ve lots of ideas.

John: Well, thank you for talking to us. We very much appreciate the conversation and look forward to future conversations.

Chris: Yeah, thank you both so much, and I appreciate having this conversation. I know that you folks talk to lots of people. I went through, and I just have you know I run every day. You’ve gotten me through many runs, because your times is just about right, so I really appreciate that, and you’ve talked to some people that I have deep affection for. So I really appreciate being part of this, and thank you for all the work that you’re doing for teaching and learning. I think it’s super important for people to hear from these other people, so thanks.

Rebecca: Yeah, thank you. We’re looking forward to sharing out this episode with our audience, and we’re grateful for all the people that we’ve gotten to have on the show, you included.

John: And that’s why we keep doing it. [LAUGHTER]

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on iTunes 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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401. Making Technology Fashionable

Technological change affects all aspects of our lives and our instructional practices must evolve to prepare students for their futures. In this episode, Loy Gross joins us to discuss strategies that she uses to prepare students for an industry that is evolving with digital tools. Loy is an online learning specialist and an adjunct technology instructor at SUNY Genesee Community College.

Transcript

John: Technological change affects all aspects of our lives and our instructional practices must evolve to prepare students for their futures. In this episode, we explore strategies used in one program to prepare students for an industry that is evolving with digital tools.

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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 Loy Gross. Loy is an online learning specialist and an adjunct technology instructor at SUNY Genesee Community College. Welcome Loy.

Loy: Hey, thank you.

Rebecca: Today’s teas are:… Loy, are you drinking tea today? [LAUGHTER] See, the thing is, I know what the honest answer is. You told me, you outed yourself.

Loy: I did. [LAUGHTER] I meant to make a cup of tea, and I didn’t.

John: I am drinking a mix of peppermint, spearmint and tarragon tea.

Rebecca: Always a good choice, John. Today I have a decaffeinated Irish Breakfast tea, which just seems like an oxymoron. I don’t know. [LAUGHTER]

John: It could have been the really strong builder’s tea that’s decaffeinated, which would really be even more of an oxymoron.

Rebecca: Yeah.

John: We’ve invited you here today to discuss your course on fashion industry technologies, in which students work with AI image generation, 3D virtual design and modeling, magic mirrors, and artificial and extended reality technology. Can you tell us a little bit about this course and what role this course plays in the Genesee Community College fashion curriculum?

Loy: So, the course is actually called Fashion Industry Technologies. It’s an overview course, and whereas many of the classes in the curriculum are focused on hands-on making something right now, this class is designed to be a little bit more forward looking. It’s not the capstone class, but it does set the students up for their capstone, which is the Spring Fashion Show where they present their final portfolio. And this course really stands out from the rest of the program with the tech that we introduce the students to. So we do AI generated images, we do 3D garment modeling, all on the computer, we do digitized patterning, we do extended reality. We do things that right now, they’re being used by like the really, really big fashion houses. We’re talking Gucci, Louis Vuitton. We’re doing the kind of work that is being pioneered by them right now, and they use that technology to streamline the whole design process and accelerate to market. And so my philosophy with the course is that I’m not just teaching students or preparing students for a job in a year or two years, they may go on to another college. I need to prepare them for a job that’s going to exist four to five years out, and that’s where the focus of this class is. We’re giving them early exposure to very, very high tech, very, very cutting edge sort of tools that are really shaping the future of fashion. And honestly, I tell my students every day, it does not matter to me if you learn to press the correct button or you learn the names of all the buttons. What matters to me is that you learned how to think creatively in that new tool, and you learned how to problem solve, either with or without the AI, and you learned how to communicate your ideas in new ways and really think about fashion as an experience and not as something that you cut and sew together.

Rebecca: Can you talk a little bit about what students are actually doing? You’ve talked a little bit about what the technology is, but what are some of the actual things that they’re doing in the class?

Loy: So the way that I designed the class, and I took this class over from another instructor, and it was kind of funny, because she said, “Well, we do this and this and this and this, and then at the end of the class you have, like, these three weeks where you can pick a topic that you want that’s on the edge of fashion, and do what you want.” And then right away, I thought, “nope, nope, nope, nope, nope.” I’m gonna let the students pick something, and they’re all gonna pick something different, and they’re going to share it, and we’re going to have this wonderful peer experience that’s going to be much richer than anything that I could choose. So we have two tracks that run sort of side by side throughout the entire semester. On the one hand, they’re designing a single outfit that they’ll eventually have in the spring fashion show. So they’re using Photoshop, and they’re using Illustrator, and they’re using the 3D modeling software and other digital tools, and they’re realizing that vision. They’re working in the digital realm until they realize their vision, and then we project it onto the table, cut it out in the fabric, make it, which is wonderful. So that’s like one whole semester long project divided into a series of modules of different parts. But at the same time, I have them doing a semester-long technology project, and that can be anything they want to do. I ask them to develop a proposal at the beginning of the class, and I approve it. And then they go throughout the class, and at the end of the class, they present whatever their end result is. I have had students who have programmed AI to make sewing patterns. I’ve had students who’ve done custom textile prints and had them printed. I’ve had students who’ve made prototypes, I’ve had prototype motorcycle helmets, I’ve had prototype pants for wheelchair users, all kinds of really cool stuff. One student did an entire video series that was influencer marketing. It’s like, this is awesome stuff. So they do that sort of at the same time. So there’s really two tracks going on in the class, and they’re going on simultaneously, and that’s just how we run it.

John: Could you elaborate on those two tracks? What components are students choosing, and what components are common to all the students?

Loy: Well, I’m a glutton for punishment. The common track where they’re all designing an outfit, they are all designing an outfit, and they’re all at the same place at the same time, on the process of designing that outfit, but all of those fits will be different. So one of them is doing a beach outfit, and another one is doing a ball gown made of denim. So they’re all over the map, but they’re working side by side using the same tools, and they’re sort of at the same development process or design process. They’re at the same place. Now the second track, which is the individual project, they’re all over the map. Once they have submitted a proposal and I have approved it, I meet with the students once a week to say, “How are you doing on your project?” We basically have scrum sessions. “What did you do last time? What are you going to do next time? What’s standing in your way? How can I help you get over whatever your current obstacle is?” But I am talking to one student about the unreal gaming engine, and then I literally turn around next student, okay, we’re talking about flashing lights that are going to go on your helmet, [LAUGHTER] so that one’s all over the place. But I don’t think that students get as much out of the class when it’s a set assignment. I prefer that the students pick their assignments. So in both cases, they pick what they want to do.

Rebecca: What role does AI play in the class? I know this is one of the technologies you noted that you’re experimenting with in this class. Is that something that’s built into the track that everyone’s doing, or is that just something that some folks are choosing to do?

Loy: It’s a little bit of both. It shows up in a couple of different ways. So we do an AI-focused unit when we hit Photoshop, because we talk about AI image generation, and we talk about it from a practical standpoint of, what can you do, what can’t you do? What are the limits of it right now? And we talk about it from an ethical standpoint, is it really appropriate for us to be developing, let’s say copies of things? How much of the AI that you’re using is actually scraped from somebody else’s work, and how much of that are you comfortable working with? So we have those kinds of discussions, and I make sure that everybody has those kinds of discussions. A deeper use of AI usually turns up in the research projects, and it could be students can use AI to generate mood boards, they can generate new prints. They can generate something that sparks their own creativity, or they can use it maybe to do research so they could identify trends or do some analysis and use it in that way. I did have one student that tried to program an AI to develop technical patterns, and at the end of the day, he made it work. And I was just like, “Yay. I didn’t think you were going to be able to do that.” [LAUGHTER] I honestly did not think the project would work. And I was explaining to the student, if you come to me and you have a paper that says, “Here are five ways I tried it, and none of them worked for these reasons, that’s an A. I’m going to give you a solid grade on that. And he made it work anyway. He insisted he was going to make it work. [LAUGHTER]

John: You also mentioned that students are using augmented reality and extended reality. Do you have headsets for all the students, or are there a number of headsets that are available that students in the class can use while they’re working on these projects?

Loy: So we don’t. We got a grant this year. It’s a President’s innovation grant that happens every year at GCC and I, along with our media technologies department, we wrote up a grant for four VR headsets and five magic mirrors. And the thing about working on those is that when you’re developing the environment, you’re not wearing the equipment, so you’re sitting on the computer. We have 12 very, very expensive, very, very heavy gaming laptops that we use to do our 3D modeling, and those laptops are the same ones that we use to develop the digital environments. So there’s two things that we do with those. We do the magic mirror concept that takes the outfit design that the students have made and it puts it on a human being. And then we have the extended reality, or the virtual reality, where we can do a full VR showroom that you can walk around in and see the models, and you can reach out and touch the fabrics. You don’t get a tactile sense, but if you’d like to know, “Is this skirt full?” you can pick it up and spread it out to see how it looks.

Rebecca: How are students responding to using these different technologies in your class?

Loy: So, as with all things like each according to their own idea of how they like it or don’t, some of the students, they jump in and they just absolutely, “Oh, I’m so excited technology, gimme, gimme, gimme.” And some students are very frustrated, because I’m essentially teaching them how to design in computer, to do a thing that they know how to do in real life on a mannequin. So they’ve already learned how to drape a new garment on a mannequin and create something new, and they’re getting pretty good at it, and then they have to go back and relearn it on the computer, and of course, you’re starting over again. And so it’s very, very slow. It’s like learning a new language, like, “I know how to say this, but how do I say this?” And so some of them get very frustrating. But one thing that’s really fun is a student will come in right at the beginning of the class. You can always tell when it’s going to happen, because they come in and they crack open the laptop, and they’re all excited. They’re like, “Hey, hey, hey, hey, you got to see what I made last night,” and just it lights up the whole room, and all the frustrations are just out the window, and let’s get to working on our projects today.

Rebecca: Sounds like that would definitely lend to some momentum in class, for sure. Can you talk a little bit about how some of these technologies are being used in other contexts on campus. Are students having opportunities to use extended reality in other spaces, beyond the fashion program or some of the other tools that you’re talking about?

Loy: So I can talk a little bit about that. I personally would love to bring these tools into a whole bunch of courses, but we’re a two-year college, and time is tight, and there is what you can do, is what you can do. So I’m kind of looking at ways that we can layer it in. So for example, I mentioned that the students have already learned how to drape on a mannequin. One of the things that I’m talking to my department chair about is about making my class and that draping class co- requisites, so that the students are learning to drape in two modes at the same time, and one set of knowledge should be able to feed on the other, and that should improve our frustration level with it. The other thing I’m talking about, kind of talking to the Dean, is about the potential for teaching this class, not just to fashion students, but to digital art students and to CIS students, because when you’re creating a brand new avatar, and then you’re creating garments and accessories on the avatar, the end output of my class, it could be a fashion design, that’s what we’re doing right now, but the output at the end of my class could just as easily be a fully rendered digital avatar, and both of those programs could benefit from that technology. So we’re having those kinds of discussions. We haven’t moved anywhere yet, but I think that’s where we’re going.

John: You mentioned that students are doing the types of things that higher-end designers are doing? Could you talk a little bit about some of the applications that are being used in industry, and how that might perhaps motivate students a little bit to see that they’re doing something that is state-of-the-art work?

Loy: So when you’re talking technology, language sometimes gets fluid and becomes difficult because we don’t have words for the things that exist. You know, [LAUGHTER] it’s like, let’s go back to the 1800s and ask about a refrigerator. There is no such thing. But what we are doing is the students design the fashion, and when they’re designing in real life, they’re designing, they’re cutting, they’re sewing, they’re attaching, they’re fitting, and they’re coordinating and styling on a real-life person, and it’s a sort of a, I don’t want to call it a fixed experience, because it can be radically different every time. But it has a very fixed sort of sequencing. And when you talk about some of this virtual technology, a lot of that goes right out the window. So we can fit clothing onto our bodies before it’s ever been made. So I can make a nice Sherpa jacket, okay with the fuzzy Sherpa texture, and I can make accessories and make designs and patches and what have you on it. And I can put that on my body in my sizing and see in a magic mirror what it’s going to look like. And then I can put it on one of my students with a very different body type and see what it’s going to look like on her. And it changes the way that we think about the fashion experience. Instead of us producing a solid, sort of unchangeable garment, we start to think about ways that you can experience fashion, both before it’s been made and even after it’s been made. So some of my students have been inspired to do things. One of my students was inspired to create a light- up design in their fashion that reacts to the user and the user’s movement, or the wearer’s movement. And this is just a way that we’re rethinking how. We experience fashion

Rebecca: I know you mentioned that students are kind of prepping for a fashion show that follows this course. It’s a prequel [LAUGHTER] to the fashion show. Can you talk a little bit about how that has evolved over the last couple of semesters or years, as students are integrating more and more with different technologies.

Loy: So integrating it is actually one of my big struggles at the moment, that the students, as I said, they know how to design a garment on a mannequin, and they don’t want to learn a new way of doing things just before the fashion show. And so every year, we’re sort of increasing the number of requirements, or trying to replace the requirements, maybe, where they have to do X number of designs for the fashion show. Well now we can maybe do X minus one, and then you have to do one digital. One of the nice things about the spring fashion show is that that’s the point where it kind of all comes together, where students have been working on a digital image, and it’s in the computer, and they can see it in the magic mirror, and they can see it in the VR space, but it’s not real. You can’t touch it quite yet. And when you project the pattern on the table, and you actually cut it out, and you sew it together, and you put it on a model, now it’s real. Now it’s a real thing. I made this really cool digital thing, and here it is. It’s there. That’s fun. [LAUGHTER]

Rebecca: I’m sure it’s exciting for students to see the things that they’ve spent a lot of time developing in a digital space, and then to see that in a physical space. I know I have that experience with design students who make an image on screen, and then they see it in print, or they see it produced.

Loy: Right. When a student designs a new textile pattern or they design a dress, and we put it in the digital space, and maybe you’ll see different people wearing it in the magic mirror, but then we see it in person, that fabric arrives from the vendor, or that design is fully formed, and it’s put on a model who’s walking in it. It’s a whole full circle moment. I had this vision in my head. I made it happen on the screen, and now here it is in real life. So that’s the direction that we’re going in, and continuously sort of moving that way.

John: Are there other faculty who are teaching fashion courses, who are starting to consider using these tools?

Loy: So far, we have not had a lot of crossover between the various classes. There definitely is the potential there. And I think as the technology gets deeper and deeper into the program, and the students get more used to it, we may see some collaborations happening, particularly in merchandising, because there’s a lot of merchandising opportunity with this sort of technology, but so far, we haven’t done any of that.

John: One of the things that could also happen is, if the students get really excited about this, they may start suggesting that to other faculty, who might perhaps be inspired to extend their courses a bit in a new way.

Loy: I can only hope. I’ll go back to my student that I seriously didn’t think that he could get the AI to do what it did, and he got it to do what it did, and he was so excited by it, and he just brings that excitement over. And other instructors will come up to me and say, and [the student name] is so excited about the project that he did for you and told me all about it. Okay, great. Let’s talk about how we can kind of cross collaborate. Let’s talk about how we can do joint assignments. Let’s talk about how my teaching can feed your teaching and grow into something to give the student a more holistic view of a process, as opposed to I need this thing for this course and that thing for that course. I’m all about holistic learning, can you tell? [LAUGHTER]

Rebecca: I’m sure by the time the students are completing their fashion shows and they’ve gone through the struggle, and then they see things in reality, that they start to see the value in adding an electronic or digital practice into their process, because some things can be speedier. They can make some different decisions without having to make and remake physically, because those adjustments can be made. You can see it in the magic mirror. Try something else. See in the magic mirror again before you’re actually creating it. Have students reflected on that experience a bit and indicated, like, yeah, I can see how I was super frustrated in your class. [LAUGHTER] Now I can see how I might use this moving forward. I know that in some of the kinds of technology classes I’ve taught, I’ve definitely had students who are, like, super resistant during the class, but then, like, three years later, I get an email from them saying, “Guess what I’m doing?” [LAUGHTER]

Loy: This fall I’ll be rolling into my third year teaching the class, so I’m still waiting for those students to come back, but I will say that I’ve had some really, really good conversations with students about actually selling their digital designs, because beyond creating the physical element, there’s the element of selling your design in, I don’t know, The Sims or in the Metaverse. Of course, I’m a nerd. I have a Metaverse account, and my avatar in the Metaverse account has an outfit that I purchased from the Metaverse store. So I’ve had some really interesting conversations with students about the potential for designing fashion that is strictly digital, that will never be made. And that gets to be really interesting conversations. And obviously there’s no other way to do that.

Rebecca: No, digital clothing can do things that real clothing can’t.

Loy: Oh, absolutely, absolutely. [LAUGHTER] So I do cosplay from time to time, and when you talk about digital clothing that does things that real clothing can’t, the engineering that we have to go into to try and make that video game character outfit actually come to life, and then make it actually behave the way that it behaves when it’s on the avatar. To make the skirt fling properly, to make the cape fling properly. You got this feather thing sticking out your shoulder, like it doesn’t even have a name. We’re not really sure what it is, but it’s sticking out the character’s shoulder, and it goes three feet in the air. How do we make that without it wobbling all over the place? And we can use these new digital tools to try and design different sorts of structural elements to the fit so that when we do make it, hopefully it behaves the way that it should. But yeah, you can make a whole lot of things in the digital realm that just don’t work in the real world. [LAUGHTER]

John: So could you tell us a little bit more about the fall symposium and what students have done there and how it’s worked.

Loy: So the fall symposium is absolutely my favorite part of the course. It is their final exam in my course. And what they do is, whatever that long tech project that they decided they were going to do, they go and they present it. And so we try and make it as much like a symposium as possible. We don’t always get people to come, but we invite high school sewing classes, and we invite the 4H and we invite every student on campus and all of the faculty to come in and see what the students are doing. And we usually get a half a room full, big lecture hall, half a room full, and the students will present anything from a research paper to a fully realized prototype that people can pick up and come and talk about. What is really exciting for me is to see the students in this culmination of this project, because it’s something that is deeply meaningful for them, every single time. I mentioned a student who did a prototype pair of pants for wheelchair users, and that student had been in a car accident, and she came in with a prototype pair of pants and did a short presentation on why you should do adaptive fashion, why the major fashion houses should be paying attention to it. And what she had done for her project was a TikTok video explaining how to take a regular pair of pants and make it into an adaptive pair. And she finished really, really quickly. And the first question that I wanted to ask her, and I asked her, I said, “Now, how many times did you make that prototype before the one that you brought with you today?” And she said, “I think this is number 16,” [LAUGHTER] but they’re so excited, and they’re so involved, and they go so deeply into it because they care so much about what they’re presenting. And I try and give them a nice, wide audience, and we have the before symposium gathering where I say “It’s okay, I know you’re not a public speaker, but you can do this.” And they go in and they have that really transformative experience, from the fear of public speaking to “Hey, I’m gonna tell you about this thing.” It’s my favorite part of the whole class.

Rebecca: That’s awesome. It’s always great to see our students shining.

John: …and gaining confidence too, so that when they do go out into the job market, they’ve already had some practice with this type of thing, which is often a bit of a shock when people first do that, if they haven’t had those sorts of experiences.

Loy: Part of the reason that I put it in the class. At some point you’re going to have to pitch your designs to somebody. So this may not be a design, but give us your pitch.

Rebecca: Sounds like a great experience, for sure.

John: The fact that a student is willing to do something over and over again, that doesn’t happen with all the assignments [LAUGHTER] we give in other classes. So, that certainly provides a bit of information about the amount of intrinsic motivation that projects of this sort generate.

Rebecca: Especially when it’s going to be realized in front of an audience. It has an audience, I think that’s a [LAUGHTER] key component.

Loy: The first semester that I ran that, I had a student who wanted to do something else sustainability in fashion, deeply committed to sustainable causes and eco causes, and concerned about the Earth and its ecological health. And that student came in with a presentation that would have been fine as a dissertation. She had two slides of references and citations and just the depth that she had gone to. And I remember checking in with her weekly and saying, “This is great,” and she’s “…but I have to look into this.” “No, this is great. This is a two-year college. What you’ve done is good.” but she was determined. And of course, I’m not going to say “No.” [LAUGHTER] She was determined to go deeper.

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

Loy: That’s a really good question. So we’ve revamped the course learning objectives and the description to make it more accurately reflect what we’re doing in the class, and to introduce some of the very heavy technological elements, the AI and the cutting edge engineering, CAD designing. So we’ve got those sort of cemented in place. And now I kind of want to see how we can get students thinking differently. Students are starting to get the idea of, “I can make this digital design. I can create it here, I can make it physically. I can export it.” But I really want to start getting them thinking about what that means for the industry as a whole. Because you can sell garments you’ve never made yet, it flips the entire process on its head. You can make garments that are never designed to be worn physically. They’re only ever made to be in that 3D modeling space. And these are just new and different ways of experiencing fashion. Instead of being something that you just physically put on your body, and okay, we’re going to walk around in it. It’s something that might react to how you walk, or it might react to how you move, or it might react to the environment that you’re in, although I’m not sure I would like it if my shirt did something if I walked in the gym, but hey, I want the students to start thinking about those things and think about what the next steps might be in the industry. So we know, like Neiman Marcus is starting to put magic mirrors in their flagship stores, where users can go in, visitors can go in, they can stand in front of the mirror. And you walk into that fitting room, and instead of trying things on, you just push the buttons on the side of the wall. Boom, I want to wear that dress. Oh no, I want it in red. And get the students to start thinking about what’s really possible out there. That sort of big picture thinking is a big part of where I want this class to go. We’ll add to that cross collaboration with digital art students, maybe with some CIS students, and start making it into something that’s more than just fashion.

Rebecca: Well, thanks for giving us lots to think about and maybe motivate some of us to integrate more technology into our classes.

John: Thank you. This was an interesting conversation, and was something a little bit different than what I normally do in my classes, and it’s nice to hear about these innovative approaches.

Loy: Thank you.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on iTunes 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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398. Multicampus AI Initiative

Faculty are faced with the need to adjust instructional strategies in response to AI. In this episode,  Racheal Fest and Stephanie Pritchard join us to discuss a professional development initiative for faculty involving six campuses.

Racheal is a Pedagogy Specialist at the Faculty Center for Teaching, Learning, and Scholarship at the State University of New York at Oneonta. She also teaches writing courses in the English Department. Stephanie is the Coordinator of the Writing Center, the Coordinator of Writing and Ethical Practice, and an instructor for classes in poetry and English composition here at SUNY Oswego. Racheal is the Principal Investigator and Stephanie is one of the campus coordinators on a SUNY multi-campus grant focused on faculty development related to AI.

Show Notes

Transcript

John: Faculty are faced with the need to adjust instructional strategies in response to AI. In this episode, we discuss a professional development initiative for faculty involving six campuses.

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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 Racheal Fest and Stephanie Pritchard. Racheal is a Pedagogy Specialist at the Faculty Center for Teaching, Learning, and Scholarship at the State University of New York at Oneonta. She also teaches writing courses in the English Department. Stephanie is the Coordinator of the Writing Center, the Coordinator of Writing and Ethical Practice, and an instructor for classes in poetry and English composition here at SUNY Oswego. Racheal is the Principal Investigator and Stephanie is one of the campus coordinators on a SUNY multi-campus grant focused on faculty development related to AI. Welcome Racheal and welcome back Stephanie. It’s been a while.

Racheal: Thanks for having us.

Stephanie: We’re very happy to be here.

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

Racheal: Yes, I actually came prepared with two teas. I’m a big tea drinker, and I’m starting with an organic loose leaf orange pekoe, and then that will be my allotment of caffeine for the day. And then I’ll be drinking an herbal tea, which is a honey turmeric chai.

Rebecca: Both of those sound delightful, but maybe you’re slightly an overachiever in the tea category. How about you, Stephanie?

Stephanie: I am drinking my go to Earl Grey tea, and then perhaps this afternoon, will indulge in some blueberry green tea.

Rebecca: I knew that Earl Grey was gonna be involved.

John: We do have, by the way, I think, four different varieties of Earl Grey tea at the teaching center, and I think Stephanie has tried each of those at one point or another.

Rebecca: How about you, John?

John: I have a spring cherry green tea. Now that it really is spring here, we haven’t had snow for over a week.

Rebecca: At least today, it feels like spring. We’ll see what the dice rolls tomorrow. I have Chai today.

John: We’ve invited you here to discuss this multi-campus grant funded AI professional development program that you’ve been working on all year. Can you provide an overview of the program?

Racheal: So, this program is called Teaching with AI: A Cross-Campus Community of Practice, and essentially we brought together faculty from six SUNY campuses, including regional comprehensives, technology schools, and community colleges, and we asked them to design learning activities that integrate AI into the classroom in ways that are critical, active, and inclusive.

Rebecca: So you mentioned that there’s six campuses involved. Can you talk about how many faculty are participating?

Racheal: We have about 66 faculty participants at this time, and the majority of them are funded by our IITG funding. But we were able to get some additional funding at Oneonta and Oswego as well. And Oswego, as Stephanie and John, you can attest, had a really big and enthusiastic cohort.

Rebecca: For those that aren’t familiar, can you describe what an IITG grant is?

Racheal: The IITG grants are innovative instruction technology grants. They’re awarded across the SUNY system, and they’re peer reviewed through a competitive process that is used to distribute funds, especially focused on educational technology, and often OER (open educational resources).

Stephanie: We are participating with six other SUNY campuses, and those campuses are Alfred, Morrisville, Oneonta, Orange, Oswego, which is my campus, and Schenectady.

John: Could you talk a little bit about what the participants in this program have been asked to do as part of their work in the program.

Stephanie: Our project began in January, and we started with a webinar from Anna Mills, who is an advocate for AI literacy and also for OER or open educational resources. She teaches writing at the College of Marin, and we made her webinar available to all of the participants in our grant. But we also had some interest from SUNY, so we advertised the webinar through SUNY’s Center for Professional Development, and we had a very large group of participants attend Anna Mills’s talk, which really focused on thinking about AI, how it’s affecting the different assignments that we’re giving in our courses in all sorts of different ways in higher education. She also spoke about some strategies that teachers should consider as they are really working to either adapt AI into their courses or sort of try to manage how AI is affecting the classes that they teach.

Racheal: And I’ll just add that one thing I really love about Anna is she is focused on critical AI literacy, and that was a big piece of our project, as we’ll talk about as we develop the details, but she has been a voice that is kind of guiding conversations about evaluative engagement with AI and not only ways to integrate and use it in the classroom, but also ways to think about how to protect learning objectives now that these tools are widely available.

Stephanie: So we started our project by listening to Anna Mills, like I mentioned. It was very well attended, and that was in early January. After that, each of our campuses had a day-long kickoff event where participants in each of our cohorts gathered in person on the various campuses, and we led them through a series of structured activities and introduced them to the grant objectives and deliverables. We also shared with them the Brightspace shell that we had created specifically to help guide our cohorts through this process.

John: And I’ll add that in the initial meetings, one of the things that was really helpful in getting everyone there, besides requiring people to be there as part of the process, there was also lunch available, which made it a little bit nicer, and it created a very nice environment for people to begin their work together.

Stephanie: It was a very nice way to pull everyone together on our different campuses, introduce them to one another, and also sort of see what level of knowledge or engagement our different participants had at the very beginning of the project. We had participants on our campus who were doing all sorts of different things. Some had already been integrating AI into their courses in various ways. Others were experimenting with ChatGPT for the very first time during that day-long kickoff event. So it was really interesting and kind of energetic and wonderful to see all of these different people in a room learn what they were doing and also learn about what they wanted to do and what goals they wanted to achieve with the project.

John: And I’ll also note that some of the faculty were there mostly to learn how to keep students from using it as an alternative to learning. Once they started seeing, over the course of that day, ways in which they could use it and perhaps have their students use it productively, t here was a pretty significant shift in their attitude towards AI use over just the course of that very first day, which has continued over the project.

Racheal: That’s a great point, John, we saw that here at Oneonta as well, and I think that we have a number of faculty on our campus who are quite enthusiastic about AI and have been adopting it, and some of the other diversity that we saw in the room that I thought was really interesting. There were folks who were more skeptical about AI or more invested in kind of protecting learning objectives, and I did want to make sure that the experience helped them to think about those problems and questions, because those are so pervasive. And then there were faculty who also have been adopting really enthusiastically in different ways, so creating a space for both of those groups to kind of learn together and hash out different positions, even while maintaining those different positions, because I think AI’s integration, it really varies across disciplines, and I think that that richness and diversity was a really valuable and important part of this project.

Rebecca: So beyond that first kickoff day, what were some of the other things that faculty have been required to do as part of the project?

Racheal: So after the kickoff, we moved on to a series of monthly meetings that we had planned, and those meetings were designed to continue the conversation and also to offer pedagogy support around designing learning activities. So in addition to thinking about AI integration, we were also offering that pedagogy support of thinking about backward design, thinking about how to begin with course objectives, how to design an activity that is active and inclusive for students, and then how to think about feedback and assessment. So we’ll talk a little bit about the rubric that we use. I mean, we did use it to assess faculty deliverables, but it was more of a tool to guide the conversations about what a strong learning activity could look like in a range of contexts. So that rubric kind of guided our monthly meetings. We were focusing on having an AI policy in your syllabus, developing at least one course objective related to AI. And then we were focusing on designing the learning activity and finally offering feedback and assessment on the learning activity. And we’re wrapping up those monthly meetings in April. Some campuses have already hosted them. Mine is actually this Friday coming up, and at that April meeting, we were giving folks a chance to share and give each other feedback on drafts of their assignments and learning activities before they submit those final drafts.

Stephanie: One of the things that we tried really hard to do in those monthly meetings was to model effective ways of teaching to everyone who was participating in our program. So in addition to the things that Racheal mentioned, like backwards design, we also spend a good chunk of time focusing on the TILT approach. For example, they had an option, they could do an in class learning activity with AI or they could have their students submit something in an out of class way. So we spent some time talking about what those assignments could look like, and why that level of transparency in your expectations as a teacher matters, and why that’s really important. We also spent time, as Racheal said, talking about feedback and all of the different ways that we give feedback to our students, whether it just be a grade or written feedback or verbal feedback, using a rubric or not, using peer feedback as a successful strategy. So we really tried, during these monthly meetings and through our Brightspace shell, to really model what effective teaching looks like, in hopes that our participants would take that and then apply it to the projects that they were developing.

Racheal: And another important piece that pairs with that, I think, Stephanie, we also tried to be really responsive to the different needs and contexts that our faculty were bringing. If you were planning an in class learning activity, for example, you’re not going to grade that or provide written feedback, and we certainly weren’t expecting that. We wanted to allow for a range of approaches, so we talked about strategies for giving feedback during class or through conversation. We tried to ensure that a range of different approaches to learning activities and assignments were represented in the conversation.

John: And just following up a little bit on that, one of the reasons we focused on the TILT framework is because, in the current environment, students may choose to use AI as an alternative to learning material, and we’d like them to be aware of why we’re asking them to do specific things, because if they understand that they’re developing skills or tools that are going to be useful for them later, it’s more likely that they’ll actually engage in it actively themselves, rather than trying to skip the actual learning by using AI. So we’ve encouraged faculty not only to do it in these activities, but also to integrate the TILT approach into all their teaching and learning activities.

Rebecca: For those that aren’t familiar, TILT stands for Transparency in Learning and Teaching. We do have a previous episode on that, that we’ll put in the show notes.

John: And we’ll share a link both to that and to Mary-Ann Winklemes’s website, which describes this as well. And just as one more aside related to this, if you’re not sure of how to create a TILT assignment, you could always feed an assignment into AI and ask it to put it into a TILT framework. And that works especially well if you also share your course learning objectives with the tool as well.

Rebecca: One of the things that I’ve heard all three of you mention is this Brightspace course that you set up for faculty. Can you talk a little bit about this?

Stephanie: So we spent some time as a leadership team developing the Brightspace shell. We knew that we wanted a common place for all of these materials to live, especially the support materials, as our participants were working their way through our different months, or our different modules. So we worked with members of the leadership team to develop this Brightspace shell that could kind of serve as a common place for us all to come together. In that shell, in addition to uploading our monthly materials that would focus on a different piece as we moved through that rubric that Racheal was talking about earlier, we also established clear guidelines for all of our participants so that they could understand what their requirements were as they moved through the program, which we’ll talk more about in a little bit. But that Brightspace shell also offered opportunities for our participants to talk to one another. One of the things that we required our participants to do was engage with one another in what we called our community of practice discussions. So our participants were required to engage with a small group of their colleagues from different campuses. This was a little bit interesting, because Oswego and Oneonta had larger cohorts than some of our other participating campuses. At Oswego, we were lucky enough to receive some additional funding and support from our Chief Technology Officer, Sean Moriarty, which we really appreciated. And I know that Oneonta had some extra funding as well. Is that right, Rachael?

Racheal: That’s right. We had initially requested funding for 10 faculty to receive stipends of $600 each, but our Faculty Center for Teaching, Learning, and Scholarship was able to provide additional stipends for, I think for us, it was six more members to participate in the program. And I think for you, you were able to double that number.

Stephanie: So, Oswego ended up with 24 participants total, which was about 14 more than what we had initially planned for. So in the community of practice discussions, it was a little bit heavier with membership from Oswego and Oneonta, but we were still able to design these discussions based off of what our participants requested. So some wanted the opportunity to speak with people from other campuses who were in similar disciplines as themselves, and some wanted to be in groups with people who were from totally different disciplines to sort of learn what other people were doing.

Racheal: I think we’re bringing out here the two tracks that ran parallel throughout the course of this experience. On the one hand, we had our in-person cohort meetings on each of the six campuses, and that allowed people to connect across departments from a range of disciplines with folks on their own campus. And then we had this second element that Stephanie has been talking about, that was the cross-campus community of practice element, and those were virtual meetings with three or four faculty members per group. And we asked those cross-campus groups to meet for a minimum of two times and then to post their takeaways to the discussion area in Brightspace. That way we could track and see what our faculty were talking about. We could take a look and answer questions that might have come up. And we also, as a leadership team, responded to those discussions on Brightspace, and we really saw the groups talking about a range of topics. One of the reasons that we wanted to connect faculty across campuses and not just run these cohorts on individual campuses, is that we’re seeing faculty bring a lot of different approaches and attitudes to AI, and a lot of that is place specific. A lot of it is related to institutional cultures or resources, and so the ability for faculty to meet from across the system and to share what their institutions are doing to support professional development related to AI, as well as to share what different departments or what conversations around AI might be happening in those different spaces. We wanted to create space for people to come together and have those conversations.

Stephanie: It was also really interesting, because participants in the cross-campus groups, in addition to having these other conversations about AI and pedagogy, began to really work well together as a unit as they worked on the deliverables for this project. So they worked together to share their ideas for their learning activity, their learning objective, their syllabus statement. And then as we got deeper into the project, they started to share drafts with one another and to ask each other, is this meeting the criteria for the grant? Are we making a learning activity that’s critical and active and inclusive? Do you have feedback for me? So as we talked about earlier, we had the meetings on each of our individual campuses with our campus groups, but it was really nice that the cross-campus groups were engaging with one another beyond what we had initially imagined, which I thought was really a nice bonus from this.

Racheal: I completely agree, and to try to facilitate some of that conversation as a leadership team, we didn’t want to impose structure on these meetings, but we also wanted to provide some resources for getting those conversations started. So we created on Brightspace question banks that faculty could choose to draw from to guide those conversations. And so with those suggested structures, I think we were able to leave room for that creative exchange. And each group was able to kind of determine how they wanted to use that cross-campus group time.

Stephanie: We also had, in addition to the cross-campus group discussion boards, we also made additional discussion boards that were optional, so participants could choose whether or not they wanted to participate in a board called community conversation, which was very informal, just a way for people to share their thoughts or ask questions to one another. And again, this was not required, but we did have participants posting there, engaging with one another in that way. Then we also had another discussion board that we called news and tools, which was basically the opportunity for participants to share new tools that they had stumbled across, or post things related to things that were happening as new AI tools were developed and came out. As we know they’re changing so rapidly. Things are coming out every single day, so participants had the opportunity to share that information with each other as well, and I think it was nice to give them the opportunity to participate in more than just one forum, because, as Racheal said, we had several members who were very engaged in the project, and I think valued other spaces to collaborate.

John: What are some of the examples of learning activities under development by faculty?

Stephanie: So, materials for this project are not due for another two weeks or so, but we have had several participants submit their materials already, getting things all squared away before the madness of final exams week here. So we did have a few faculty who were thinking about engaging with AI tools as part of the brainstorming process. So for example, one of our participants here gave an assignment where students were asked to use Perplexity, which is a free AI tool that also can access the web and provide suggested sources. He’s asking his students to use Perplexity to help them continue brainstorming for an assignment that they’re working on in the class. So students already have some general ideas of what they want to do, and he’s asking them to engage with Perplexity to deepen their brainstorming. So for example, ask Perplexity to provide some suggested sources to the topic that they’re considering, and then evaluate the sources that Perplexity is suggesting. He also asks them to prompt Perplexity, to give them a counter argument to what they’re exploring. So those are some examples. T hat would be an example of a learning activity. It’s something that he’s going to have them do in class. And the nice thing about his prompt is that he gave his students four different sample prompts that they could take and then put into the tool, but they would have to fill out the details of their particular brainstorming. So that’s one example from our campus. Racheal, would you like to give one example from yours, and then I can do another one?

Racheal: Absolutely, that sounds great. I’ll share an example from Oneonta, while saying as well that the leadership team will be taking a look at the examples across campus once folks are uploading them, as Stephanie has said, but we haven’t done that yet, so we only have the knowledge of folks we’ve been working with on our own campuses. So one faculty member in sociology here has been developing a project that I think is really interesting and exciting. He’s going to be teaching a course in the fall that has a unit focused on conspiracy theories and thinking specifically about how to debunk or counter conspiracy theories. And so he has identified an AI chatbot called DebunkBot, which has been developed, I think, out of Cornell. And he’s going to ask students to identify a conspiracy theory of their choice, engage with DebunkBot to see how the AI counters that conspiracy theory with factual information, and then they’re going to evaluate those AI outputs. And I think within the conversations he’s having with students, too, is the question of, do facts work against conspiracy theories? There are various opinions on that, so not only evaluating DebunkBot’s outputs, but also thinking about it as a tool to counter conspiracy theories. What are its strengths and weaknesses? And what I really like about this project is, I think that it exemplifies that critical element that we’ve been emphasizing through this project. By critical, we really mean evaluative. We are asking that all of these learning activities and assignments take up that AI literacy task of helping students evaluate AI outputs and evaluate AI as software.

Stephanie: Tagging on to the evaluative piece, one of our colleagues here is working on an assignment that asks students to sort of reverse write a paper. So she’s giving her students a prompt and asking an AI tool of their choice, probably ChatGPT I imagine… one of the components of our grant that we really emphasized was trying to make these assignments or learning activities as inclusive as possible. So we’re thinking about access, who has access to this technology. And since ChatGPT doesn’t require users to make an account, there is a free version, a lot of faculty are leaning toward those sorts of tools for this. So anyway, one of our faculty here is asking her students to have ChatGPT generate an essay on one of the books that they’re reading for the course, and students are then going to look at the essay and evaluate the output that ChatGPT has created. So they’re going to look at things like whether or not the material is accurate, if there are hallucinations, if it is in fact doing what it was asked to do. So I think they’re being asked to analyze, “So how is the quality of ChatGPT analysis?” And this faculty member also has a series of follow up prompting questions to have the students continue to engage with this essay that was generated and see if they can make it better, or see if that’s it, if it’s as good as it’s going to get, and if there are still holes in the analysis or with the argument. As a writing teacher, I thought that that was a really cool way to look at what this technology can do, really consider its limitations as well, and think about how are we going to do writing, teach writing as we keep going, as more students rely on these sorts of tools.

Racheal: I love that you were emphasizing the inclusivity piece there, Stephanie, which has come up in our conversation where you were mentioning especially how you emphasize TILT on Oswego’s campus. On Oneonta’s campus, we talked a little bit about UDL, universal design for learning, but those were frameworks that we wanted to keep in the mix as part of the conversation, we didn’t necessarily have time in our progression to really dive deeper focus. So we set some basic guidelines for inclusive activities that we encouraged everyone to meet. And those were, as you said, making sure that whatever AI tools you’re asking students to use they have equal access to. So that would look different on different campuses as well. Here at Oneonta, we’re a Microsoft campus, and that means that we have Co-Pilot for all students over 18. And if you’re thinking about, when you’re introducing students to AI tools, not only making sure that everyone can access it, but also making sure that you’re thinking and talking about who might have access to paid versions, because students are sometimes subscribing to ChatGPT at that higher level, and just making those conversations about what tools you’re using part of the conversation. The other inclusive piece that we really emphasized was just that providing instructions to make sure that you’re aware that people… students, specifically… are bringing different levels of knowledge of technology to the classroom. Some students are using AI all the time, others have never used it and don’t want to. I think we’ve had those conversations in our group. So just being aware of the diversity of student capabilities as part of that inclusivity and accessibility piece was really important to us too.

Stephanie: Just to kind of tag on, I thought it was really interesting that, as we talk about access and who has access to a paid tool versus a free tool, I’m sure you have seen, I know John and I have spoken about this a little bit that, right now ChatGPT has a free trial of their paid version that they’re marketing specifically to students. So it’s ongoing from now until the end of May, which I thought was really interesting. And I know that they’re not the only tool that has done that. Is that, right, John?

John: Gemini and Claude have also done it, and one of them I forgot, which one actually is extending it until next year. So there’s a little bit of competition going on to get more students using their particular models, because student use does make up a remarkably large share of the use of all the AI tools now, for reasons that perhaps are not always as positive as we’d like.

Stephanie: I think it will be really interesting to see how that continues to develop as we move forward. They’re doing this two month free trial. Now. What does that mean for the beginning of the fall semester and beyond that?

John: One thing we should also note is that this podcast is coming out in June. We’re recording this in late April, which is why snow was as recent as last week, and why many of the references are towards the last stages of the semester. This is a rare occasion for us to be this far ahead in our recording. We often record things a week or two before their released, but we’ve happened to be able to get a lot of podcasts scheduled within a fairly short period of time. Along the lines of the assignment that Racheal mentioned in evaluating sources, Mike Caulfield has been developing a prompt for SIFT, which is his approach for analyzing the veracity of online claims, and as of last night, he’s developed an expanded version of that prompt, and we’ll share a link to that in the show notes as well, because we can use AI tools to try, not only to create false information, we can also use it to try to verify claims.

Rebecca: One of the things I’ve heard you all talk about quite a bit, and I’d like to hear a little bit more about, is the rubric that you were using to evaluate the assignments and things that faculty were creating.

Racheal: Yeah, the rubric that we developed, as I mentioned earlier, we really thought of this more as a tool to guide our conversations, rather than as an assessment piece. But we’ve run at SUNY Oneonta, similar cohorts devoted to different learning activities and aims in the past, and we knew from those experiences that being really clear about what the deliverables are and what elements we’re privileging and would like to see, that’s going to help faculty design learning activities that then can move as we’d like to move them, to our public facing repository. So we focused on three categories of evaluation on the rubric. First, we were asking folks to develop an AI policy for their syllabus. That was a big part of this project, too. As we are thinking about helping faculty and students navigate the emergence of AI, students are coming into a range of classes that all have different expectations for them. So some faculty might be saying, “if you use AI at all, you are cheating.” Others might say, “I want you to use AI. You have to use it. And here are some of the ways that I’ll be evaluating your use of it.” So just thinking about how we as a leadership team could support students by encouraging AI policies through this experience was important to us, and those policies would capture a wide variety of approaches to AI in the classroom. Some policies that we’ve seen have given guidelines assignment to assignment, where some assignments require AI use, some ask students not to use it. We’ve seen policies that outline the specific ways students might use AI throughout the semester, and we’ve also seen policies that said you can’t use AI except in these very specific ways that I ask you to on this learning activity. So we’ve really seen a range there. And the rubric begins with that AI policy element and with the course objective. So one of the elements that we’re collecting as part of the deliverable with the learning activity is the syllabus with an AI policy and a learning objective.

Stephanie: And I think it’s important to emphasize as well that regardless of how faculty feel about AI, like whether they’re embracing it or they’re saying, “I really would prefer you not use AI in this course.” It’s really important for us to be transparent with our students about what the rules are. As Racheal said, our students are generally taking five different courses or more with five different faculty who have a whole bunch of different feelings about this. So some people might think I don’t want them to use AI in my course, so I’m just going to not put anything on my syllabus. It’s so important for our students that we are transparent, regardless of how we feel about it.

Racheal: Yes, and that was one of our aims with including that syllabus element. So the second rubric element focused on the learning activity itself. And again, that had the critical, active, and inclusive piece. I think we’ve done a good job talking about how we define critical as evaluative, and how we define inclusive as tool access and instructions. And I guess I can just briefly define active as students are using the tools themselves. So we’re not just asking you to demo AI in a lecture. We’re saying, “Get students in there evaluating outputs for that AI literacy piece.” And I do think it’s important to mention here that we had many conversations about students who want to opt out of AI tool use, students who might have environmental reasons for not wanting to use the tools or other ethical reasons, and we did talk about strategies for that, including alternatives that would allow students to maybe engage with an output that the instructor created from AI or alternative assignments that aren’t actually asking them to engage with AI, but might be asking them to evaluate AI in a broader conceptual way. So those alternatives did come up for us. And Stephanie, maybe you could talk about that last rubric piece, the assessment and feedback element.

Stephanie: Sure. So we thought that it was important for our faculty, as they are designing this assignment, to really think about how this assignment or learning activity would be assessed. How are they going to give feedback to their students as they experiment with these tools, as they evaluate these tools, and what does that assessment piece mean? So we have some participants who are just doing one assignment or one learning activity, so they have to just think about that one piece. But we’ve had several participants in this project who have become inspired by the one part and have decided to make lots of changes to their assignments or their learning activities, and then think about what assessment means for them and for their students. So as we talked about earlier, the feedback piece is very important, as our students are critically and actively engaging with these tools, because we have to guide them through what they are learning. If we are really focusing on AI literacy and helping our students understand or begin to understand how these tools might impact, not only the work they’re doing in the classroom, but their future jobs. Every career is going to look a little bit different. Some are really going to be embracing these tools and expecting our students to understand how to use them, where others perhaps not so much. An example, I teach creative writing courses. Right now. In those courses, I am not emphasizing AI use because, for many of my students, they’re looking to submit their work to different journals, and most creative journals at this time are not accepting AI-generated work. So it’s important to acknowledge that, and it’s also important to focus on why we’re going to continue to do things the way that we’ve done them a little bit for the past, right? But other courses, if I’m teaching English composition, and I’m teaching them different ways that they can outline or draft or brainstorm, how they can use AI to get feedback on their own work and their own writing. Those are important skills that will come in handy for them later on, as we talk about things like efficiency and making more polished drafts and getting feedback before you turn something in. Those skills will come in handy more so for those students than for some of the creative writers.

John: We have a student advisory board for our teaching center, and they met last week, and one of the things that came up was the topic of AI, and two concerns that students discussed pretty actively was that a lot of faculty have not made clear yet what their expectations are about whether AI use is allowed, when it is allowed, and so forth, and they noted that it was more rare than the norm for people to actually have made those policies. We’ve obviously been encouraging faculty to do that for a couple of years now, but it hasn’t made it out to all of our classes yet. But our institution will be requiring an AI policy statement in the syllabi beginning next fall. So this transparency issue and being clear about your expectations is really important. The other thing they were concerned about is being falsely accused of AI use, and this is something that is very concerning for students, and we have a lot of cases where some faculty are just completely ignoring AI, and others are assuming that AI is being used by all students all the time, and accusing students of using it without any real proof in many cases, and it’s a somewhat troubling environment.

Racheal: Yeah, I think that’s such a good point, John, about the range of approaches that students are taking to AI, as well as the different assumptions that faculty members are bringing. And I think that there’s sort of a spectrum of assumptions, where, on the one hand, you have the idea that AI should be completely left out of the classroom and any use of it is cheating. On the other end of the spectrum you have we should embrace AI and use it for everything in the classroom, and if we’re not teaching that, we’re being irresponsible. So we are seeing very polarized views on our campuses, and I think that a professional development opportunity like the one we’ve been running needs to respect and honor and speak to both of those positions, while also making the conversation a little bit more nuanced. And I think one of the ways that we’ve done that is to show that bringing AI into the classroom actually addresses the problem of students using it for cheating and plagiarism, because students are: A) aware that the faculty member knows about and is actively engaged with those tools, so they might be less likely to just copy and paste, which is the use that most people do regard as cheating or plagiarism. And in addition, it not only shows that faculty members are engaged with and knowledgeable about these tools, but in my experience, students are really hungry for guidance from faculty on how to engage with them in an ethical and informed way. So we’re helping them build those AI literacy skills by confronting and talking about these tools in our classes. And to me, that might lead to them using the tools more in specific ways that we’re guiding them to, but it can also lead to them using them less in situations where we want to protect those learning objectives. Like Stephanie, I teach in the writing classroom, and I have been devoting this semester to engaging with and thinking about AI, but that has meant we have plenty of writing and activities where I’m asking them not to use it, and then we have activities where I’m asking them to use it in specific, guided, critical ways. And so I think that that more nuanced approach than we need to totally keep it out or we need to totally embrace it, that’s the area that a lot of our faculty are working in, and I think it serves our students really well.

John: And Stephanie and I have also, in a series of workshops here, encouraged faculty to co-develop their AI policy statements with students, so that students are involved in that conversation, and if they help develop the guidelines for the class, they’re much more likely to abide by them and to buy into them.

Stephanie: To piggyback off of that as well, in our cohort, especially, I mentioned earlier that we had a range of experience levels with AI tools. And what’s been really interesting is that for participants who are trying these tools for the first time, they’re having some important realizations about what these tools are or are not capable of, and I think that’s really important. If we are teaching and we’re saying, here is my AI policy, and here’s why, if we are familiar with what the AI tools can do, I personally feel that our students are going to be more receptive to that. If we are making assumptions about what the AI tools can do, or how well they can complete a task, but we don’t really know, then how are we going to recognize an AI’s work when we come across it? So for example, this is a dated example, but I think it works still really well, in a writing classroom, for a little while after ChatGPT came out, the solution to avoiding AI-generated work was to ask students to write reflectively, because they’ll be writing from their own personal experience, and an AI tool can’t replicate that. And I remember leading a workshop, and I showed an example of a reflective essay that was generated by AI, and this was like two years ago. All I had to do was prompt it a little bit. I asked it in a revision of the reflection to include a sad story about how the tool had a childhood dog that they really loved, and that relationship with their dog is the reason that they wanted to pursue veterinary studies. And it generated this beautiful reflective essay that you would have never known from anything else. As I continued to prompt it, I asked it to include volunteer activity, and it made up a name of an animal hospital… at the time that didn’t exist, but now it would, because AI tools have gotten so much better. So it’s just really interesting as we think of what the limitations are, and how there are not as many limitations in so many different fields. That is, of course, not the case for all fields, but the tools continue to evolve so quickly. So I think it’s important for us to recognize what those limitations and capabilities are, and then if we know, I feel that our students are more likely to trust our judgment, and then believe us when we say, “Yes, perhaps you could use AI for this, but let’s not because I want you to develop this skill that will help you as you continue taking more classes here, or as you graduate and start looking for a job.”

Rebecca: We’ve talked a bit throughout the conversation about how faculty have engaged with the program over the course of the year so far, in creating assignments and in the discussion boards, et cetera. Is there any additional things that you want to share about how faculty have responded to participating in this program.

Racheal: I think that some of the feedback we’ve received is that it’s been really nice to have institutional support to help faculty do work that they felt the need to do in their classes. Faculty want to respond to AI’s emergence, and oftentimes we know faculty are overworked. There’s so much to do, and so being able to get that institutional recognition through a stipend to say “this is important, and we’re offering support for developing these activities.” We’ve heard faculty report that they’re happy that the opportunity is available to do things that they’ve already wanted to be doing, but here they can see the institution supporting that.

Stephanie: I agree, and it’s also been very helpful as we have these conversations with our participants to think about effective practices on the whole with AI. So for example, there has been conversation everywhere about using AI tools to grade student work and to assess student work. So we’ve had opportunities to have those sorts of conversations about why, perhaps, it’s not in our best interest as teachers to use AI to give feedback to student writing, for example. And it’s been nice to hear what other people are doing, like Racheal said, with that support aspect, but also the conversations allow us to sort of come across those topics that might just be neglected otherwise.

Racheal: And I’ll just jump in, Stephanie, because your comments about using AI to assess and grade has also been an important part of our conversations at Oneonta. And again, we’re seeing really polarized views, where some people are saying, “I would never do that.” Some people are saying, “I’m already doing that in these specific ways.” And we’ve talked there, for example, about the Student AI Bill of Rights, which circulates online. And one of the elements of that AI bill of rights for students is that students should have the right to know when faculty are using AI for feedback or other forms of engagement, and so I agree that this has been a really great place to have those conversations which might be taking place informally or in departments, but in this interdisciplinary setting, people are able to talk about those issues and share their views.

John: So we always end by asking, and this is something particularly appropriate with AI, what’s next?

Racheal: As we’re wrapping up this project, we are looking to move the learning activities that our faculty will be submitting to Brightspace into an open access repository that my colleague Ed Beck here at SUNY Oneonta is helping to develop, not only for our grant, but for another IITG-funded project out of Albany that is generating some K-12 resources. So we are creating that repository space via SUNY Create and after we collect our faculty learning activities on Brightspace, we’ll be guiding them to move revised versions of those activities into the repository. So we also have our presentation at CIT, which is the Conference on Instructional Technology that SUNY convenes every year across the system, we will be presenting at CIT on this project, and by that time, we will have more information on the website and how to access it, so that faculty, not only across SUNY, but across the internet, will be able to view the learning activities that we’ve been talking about today.

John: And we should mention that SUNY Create is basically an instance of a system-wide license for Reclaim Hosting’s domain of your own.

Racheal: And after CIT, we’re hoping to extend this work, maybe in new ways. We’re thinking about ways to bring campuses together for communities of practice. And one thing that’s been really important to me about this project has been the value of working with colleagues in educational development, whether we’re talking about faculty or staff at all of our six campuses, all of whom have brought different skills, knowledge, expertise, passion to this project. We’ve had leadership team members who work in the social sciences and have brought that qualitative research and assessment knowledge to the project. We have created pre- and post-assessment surveys that are helping us collect data on the success of the project, and we couldn’t have done that without Deepa Deshpande, who’s at SUNY Alfred. We also have leadership team members who are bringing expertise in the classroom, that includes Stephanie, John, and Laura Pierie, who is with us from Morrisville, and having that faculty insight from a range of disciplines has really grounded the project in the needs of our participants. We’ve also had Dana Salkowski and David Wolf join us from community colleges, and having their input as directors and leaders in teaching and learning has been really crucial to helping us think about how our project could serve faculty at all of our different campuses that we’ve been working with. So I think that it’s been a really amazing group effort, and I’m so grateful for the leadership work of that entire team. I think together, we’ve been able to really support faculty in some of this work, and I’m excited to think about the collaborations that we might be able to pursue going into next year.

Rebecca: Well, thank you so much for joining us. It’s been a great conversation, and it’s a great project to share with others that they might want to implement something similar on their campuses.

John: Thank you. It’s been great working with both of you and I’m looking forward to future collaborations.

Stephanie: It’s been wonderful chatting with you all.

Racheal: Thank you for having us, and it’s been wonderful talking with you today.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on iTunes 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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