Generative AI has disrupted familiar approaches to teaching and assessment, but also created new opportunities to reconsider what, and how, we want students to learn. In this episode, Travis Thurston and Beth Buyserie join us to discuss ways of using AI productively in our classrooms by shifting the focus of learning assessment from product to process.
Travis is the founding Director of the Center for Empowering Teaching Excellence at Utah State University. Beth is an Associate Professor of English and the Director of Composition at Utah State University. They are co-editors of Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions.
Show Notes
- Buyserie, B., & Thurston, T.N. (Eds.). (2024).Teaching and generative AI: Pedagogical possibilities and productive tensions. Utah State University.
- Mollick, E. (2024). Co-intelligence: Living and working with AI. Penguin.
- Keller, J. M. (2009). Motivational design for learning and performance: The ARCS model approach. Springer Science & Business Media.
Transcript
John: Generative AI has disrupted familiar approaches to teaching and assessment, but also created new opportunities to reconsider what, and how, we want students to learn. In this episode, we explore ways of using AI productively in our classrooms by shifting the focus of learning assessment from product to process.
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John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.
Rebecca: This podcast series is hosted by John Kane, an economist…
John: …and Rebecca Mushtare, a graphic designer…
Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.
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John: Our guests today are Travis Thurston and Beth Buyserie. Travis is the founding Director of the Center for Empowering Teaching Excellence at Utah State University. Beth is an Associate Professor of English and the Director of Composition at Utah State University. They are co-editors of Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions. Welcome, Beth, and welcome back, Travis.
Beth: Thank you.
Travis: Thank you so much. Pleasure to be here.
Rebecca: Today’s teas are… Beth, are you drinking tea with us today?
Beth: I am. It was accidental. I was like, I need some tea this morning, and then I remembered that I was having a tea with you. So yes, I’m drinking a vanilla almond, and it’s delicious.
Rebecca: Nice, bonus points for having tea. How about you, Travis? [LAUGHTER]
Travis: I am having a peppermint herbal tea this morning.
Rebecca: Nice.
John: Very good. I’ll be having that this afternoon. I am drinking one of Rebecca’s favorites, spring cherry green tea. [LAUGHTER]
Rebecca: That is not my favorite, John. [LAUGHTER] It smells good, but I do not like how it tastes. I have a nice, simple English breakfast tea, John.
John: Very good. So, we’ve invited you here today to discuss teaching in generative AI. Can you tell us about this book project?
Travis: This project really began less as a response to maybe a piece of software than a response to the conversation that higher education was having about it. So, in early 2023, a lot of that conversation seemed to be moving towards somewhat two extremes. Either generative AI was going to transform education or destroy academic integrity. There was a lot of that conversation happening, and Beth and I felt there was a need for an open, interdisciplinary, pedagogy-centered space where people could really wrestle with some of those ideas and those concepts, where we could also think about the excitement, the concern, the experimentation, maybe the resistance, and also the uncertainty that surrounded it.
Beth: I direct a composition program for all of the first-year composition teachers, and within that space, there was definitely a sense of like, “Oh my goodness, what do we do? Like, writing is our bread and butter. It’s what we teach, and is this completely going to take over and make it so that there’s nothing to teach, or that students won’t be able to learn anything?” And so, like Travis said, I could tell, since I work so closely with teachers, that teachers definitely needed a space to be able to think about pedagogical possibilities for this technology, rather than this is going to be the end of the world kind of thing.
Rebecca: This book is pretty robust. It’s got 35 chapters organized into six sections. Can you give us an overview of the topics addressed in each of the sections?
Beth: So, the way that we did this particular collection is because AI was so new, and because we wanted to get this resource out to teachers as quickly as we could, rather than solicit proposals, we solicited full chapters, and we weren’t expecting them to be super, super long, but we did want people to send us the full chapters. So we left it, Travis, if I remember, fairly open ended, and then based on what we received, then we organized the book into the six sections. The first section is framing concepts because we wanted to make sure that teachers had, before we went right into some pedagogical applications, some sense of like, how do we even think about this? What’s the language that we need, terms, concepts that we need in order to think about this? And then our second section was research studies. Since AI was so new at that time, at least in education, there weren’t a lot of research studies, and so we had a few people who had actually done some IRB research studies, and we wanted to have those in there to showcase the kinds of research that people could do. We knew that this book, since it was early on, wasn’t going to be the definitive; that it was going to be the starting point, and so we wanted to give people an example of what could happen. And then our third section on race and Indigenous studies, we created that one under the model of a symposium, where it was quick, shorter chapters that really sparked questions and concerns and possibilities that people could continue thinking about and wrestling with later on. So with that section, we were able to get a lot of authors bringing up a lot of different ideas to spark a larger conversation.
Travis: Yeah. So the fourth section, humanizing technology, we really wanted to keep and really focus on the human experience that’s at the center of learning. So those chapters really center around some of the authors’ experiences in their own classrooms and how they wrestled with some of those concepts with their own students. And then moving into the fifth section, teaching resources, those are focused more specifically on assignments and activities and the way that we can frame and construct those for our students. And then the final section on disciplinary approaches, it demonstrates that AI can’t be separated from disciplinary ways of knowing. For example, like what responsible AI looks like in a chemistry course would be completely different than maybe like a music history course or creative writing, or with grad students rather than undergrads. So that last section says AI is never merely a tool to question; it’s more theoretical, empirical, cultural, relational, practical, and disciplinary.
John: Most of the books out there on AI that came out in the first few years focused either on a very pro AI in terms of all the wonderful things that AI could do, or something that was somewhat more opposed to AI use and focused on some of the harm that can be done when AI is used excessively or used as an alternative to learning. But one of the nice things about this collection is that you do have those 35 different essays in it, providing a pretty diverse set of viewpoints on quite a few different disciplinary approaches. How did you select the authors? You mentioned that you were gathering whole chapters, but how did you select the authors of these chapters?
Beth: Well, one of the things that we wanted from the beginning was that we didn’t want a binary yes/no approach to AI because it was so complex. That was one of the things that, for me as a writing program administrator, that I was grappling with with my teachers and we were grappling with with our students is that we couldn’t just frame it as never use this or stop learning how to write altogether. And so we were fortunate that we received so many possible submissions, but I think Travis and I looked at all of those to be like, “How are we putting these together so that we are furthering the complexity of the conversation around AI?” And I think one of the reasons we did that is because we knew that in terms of technology, the book could quickly become outdated because AI was going to keep changing, but we knew that the complexity surrounding the conversation would be more permanent or long-lasting. Travis, did you have anything else that you wanted to add to that one?
Travis: Yeah, no, that’s a good point. In our early conversations, we did want to try to somewhat elevate or replicate maybe the conversations that are happening on a campus-wide basis across disciplines, including librarians and instructional designers, and bringing in a wide host of voices.
Rebecca: I know one of the things that has come up, at least in our campus conversations and in many of our podcast conversations around AI, is the struggle [LAUGHTER] educators are having with assessing student learning. What are some of the most effective strategies for assessing student learning that you’d recommend?
Beth: Well, this reminded me too of more of how we selected the authors, because we realized that it’s easy within each discipline to sort of be siloed in our perspective on AI. I mean, that’s part of what creates the polarity in terms of the perspectives is that, for this discipline, AI is wonderful and we should only use it, et cetera. And for this discipline, it appeared to be the end of the world as we know it. And so, creating a collection where we intentionally had authors from multiple, not only academic disciplines but also librarians and instructional designers talking about AI, really helped people get more perspective on the complexities of AI and how it could be used in the classroom. And so I think then that transitions into how some of the most effective strategies for assessing student learning is, I’ll take our example of writing, we talk a lot in writing about how the process is so important for learning. That it’s not necessarily the final product that we are concerned about, and in some weird way, AI has become a silver lining for folks in writing studies to be able to have interdisciplinary conversations with those outside of writing studies because it was often in the past that folks outside of writing studies would be like, “Why can’t students just produce the perfect piece of writing the first time? They took one writing class and then surely it should have applied to the rest of it.” I’ve now had a lot of fruitful conversations with people in other disciplines that have said, “Oh, I actually don’t care as much about the final product because AI can make that look perfect. I care about the students’ learning.” And so the fact that AI can now create the final product so quickly has allowed us, and the ETE program that Travis directs at USU has been a wonderful space for faculty from all disciplines to get together and be like, “How do we start assessing and making more explicit the learning process in the classroom? How do we emphasize that more rather than necessarily the final product?” And so the fact that AI came about has allowed us to have more of these conversations about what is it that we’re actually wanting to assess in terms of student learning. So it’s allowed us to be like, “Oh, we actually care about X in this discipline,” rather than “Please turn in your final report.” So one of the most effective strategies that I have is when talking with interdisciplinary faculty, especially, is let’s start naming explicitly what you want students to learn. Which, I mean, I have two degrees in education. I know Travis has background in education. For faculty who don’t come from education, sometimes that’s new.
Travis: That’s a good point. We really emphasize the learning process and how to make that explicit, to make sure that different types of learners are able to demonstrate their learning process and demonstrate how they get to that final product. I think I would add too that one of the goals is not to create an AI-proof assignment. Like that can very quickly become an unwinnable technology arms race, [LAUGHTER] and really detection of AI use is not the same thing as learning assessment, right? So I feel like our task is to design assessments that give us rich enough evidence to really understand what students know, how they reason, and maybe how their thinking has changed through that process. For example, Maha Bali’s chapter, the cake-making chapter, is so great for this purpose, right? Like thinking about when there are times that we ask students to bake a cake from scratch versus when it would be appropriate to use maybe a prepackaged box mix, [LAUGHTER] and how AI can be a scaffold for learning in that process, and when it’s appropriate and maybe when it’s not, and how we can be explicit with students about those determinations. I think another great point that I like in the book is that students might use AI during that process to help them practice and to help them refine the skills that they’re developing. For example, Marc Watkins, his chapter on reading assistants really makes that point where students might otherwise stop or skip reading a text and not allow the AI to simply summarize a reading. Use it more as scaffolding in the process. And that, to Beth’s point, means that we have to be thinking about that learning process when we’re designing these types of learning activities, and ultimately the assessments to measure that learning.
Beth: I’ll add briefly just another chapter that I think really helps with assessing student learning is Michael A. Christiansen’s chapter on using AI in chemistry classrooms. I have my students, both grad students and undergrad students, read at least a section of that chapter, and they very quickly realize that, in order to be able to evaluate the output that AI gives them, they still have to be experts in their content. And so we use that chapter as a way to help them create their own learning goals. What is it that you want to learn first? So that is one way of doing more effective learning assessments: is really involve the student with their own learning and goal setting, because after that chapter, they can understand very quickly, like, “Oh, if I don’t become an expert in my subject, then the AI really will take over my job, or that I won’t need to be there. Like, I have to be able to contribute something too.” So, having them really set their own learning goals helps as well.
John: And I think everyone most faculty do agree that it’s important to be able to evaluate the process of learning. What are some strategies that people can use to observe the process in which the students are learning? Because that’s, I think, a question that a lot of people are struggling with right now because it’s ultimately what we’ve always wanted to do: we want to observe the progress of student learning. But up until recently, most of the focus, outside of some areas where you could easily observe that, most of it was based on the final product that students produced in some way.
Beth: I think it has required faculty from all disciplines to really think more about why your actual students are in the classroom, either physical or online, with you. Why are you all in that space together rather than as a teacher, it would be really easy just to lecture and give out the information. And granted, this works better in smaller classrooms, but I think it’s transferable to larger classrooms as well, is to have the classroom space, whether online or physical, be more of the place where the students are engaging in the learning process and using their bodies as a way to do that. So, like I have my students use sticky notes where they’re processing their learning. We draw things. Not well. I mean, my students draw well. I don’t draw well. This last semester, I’ve actually had my students draw a concept from something that they were reading, so it required them to use a different mode, a different way of thinking and showing their learning. And because I could no longer just say “Summarize this,” because AI could do it for them, my students then learned the concepts from that chapter better than they have for the last 20 years that I’ve been teaching that chapter. So one strategy I think is to actually engage in the embodied, multimodal, physical aspects of learning, even in small ways in the classroom.
Travis: Yeah, I think another point is, for example, in Catherine Denial’s chapter, she talks about the importance of starting by trusting our students, and that really opens up the opportunity for us to have conversations about the roles that AI is playing in that process. So, for example, also Ethan Mollick’s book on Co-Intelligence, I think, is really fascinating, and one of the things that he points at is what role is AI playing? Like, are we using it as a tutor? Are we using as a mentor, a teammate, a simulator? And being able to identify how or what role that we’re utilizing AI, I think, also can help us to be more explicit, and also help students be more intentional about the ways that they’re engaging with that type of technology. I also really like the example in Julia Gossard’s chapter where she’s having her students write their own constitution, and she has them start out by writing their own, and then the next step in the process is to have AI write a constitution, and then the students can be critical of the output of AI, and think more specifically about what ideas are being replicated that maybe we want to let go of from past patterns, and have more of a critical lens.
Rebecca: Sounds like a fun project.
Travis: Oh, absolutely, right? [LAUGHTER]
Rebecca: We kind of started by saying there’s a lot of almost like debates, right? Pro, against AI, and you’ve talked really nicely about challenging that and making it a more complex conversation. How have you seen the conversation in higher ed around AI evolve?
Travis: Yeah, I really like that question. I think higher ed will gradually shift, maybe from asking how do we prevent AI from entering an assignment, to maybe asking what is the intellectual work that students should continue on their own, and how can they demonstrate that ownership? For example, when information and the text or these outputs become so abundant, I really think the distinctive value of education moves toward framing questions, evaluating evidence, and really getting into more of the deeper educational work, and then also moving into applying that knowledge into maybe unfamiliar contexts, collaborating with other people, making ethical decisions about how we’re using certain technologies. Our students are confronted with these technologies on a daily basis in the real world, and we’re seeing more and more how AI is playing a role in the workplace. And so if we are really preparing our students to go out into the world and into a new workforce, these are questions that we should probably be helping them to think about along the way.
Beth: I think Walker P. Smith’s chapter that he co-wrote with seven of his undergraduate students illustrates that in a really nice way, where it’s clear that they’re doing this research project together and that they’re evaluating what they learn from AI in different contexts together. So I do think one of the shifts is it’s requiring students and faculty to more intentionally be like, “How can we foreground the lived experiences of the humans who are in this classroom space with us,” making sure that it really is student focused, not in a way that devalues teacher expertise or devalues what AI brings in, but that we’re saying like, “You bring something to this puzzle. You bring something to this conversation. This conversation is not the same without you,” because I think it’s easy for students to feel sort of like they’re a number in a big classroom or in a big university, or that there’s so many other people who are perhaps competing for the same job, and a lot of that is true, but really taking the time to figure out like what value do you bring because you have that value. In an odd way, I’ve seen the conversations around the dehumanizing aspects of AI allow people to emphasize more of the “Why did we get into education to begin with? Why do we really care about the students in our classroom? How do we help them care about their education in even more concrete ways?” Which I think is also helping to address some of our concerns about retention and student enrollment as well. So that’s one of the conversations I’ve seen. And then the other thing that I’ve mentioned earlier is just that the biggest shift has been people like, “Oh, it’s no longer just the product. The product used to be the only way I assess student learning, or that I really focused on,” and now it’s much more about what they’re learning throughout the course. And in some ways, I don’t think we would have been able to have that interdisciplinary conversation so quickly without AI.
John: I think you’ve already addressed this to some extent, but what are some new possibilities that have opened up in terms of supporting student learning that AI provides that we didn’t have before AI became available?
Travis: Yeah, I think this is a great question too. One of the things I’ve been working on lately is what I call the ARCS reactor, and it’s this idea that we can use a particular prompt and help us as instructors to find ways to support students through learning barriers. And that ARCS model was originally proposed by John Keller back in the 80s, and it’s an acronym for thinking about the way that we design for student motivation, for attention, relevance, confidence, satisfaction, and then also supporting them through the volition to keep going. And when Keller initially proposed this framework, it was designed to be less of a bag of tricks that a teacher might pull from, and more to specifically diagnose the motivational barriers that students experience. I feel like that’s an important role that we as instructors should be able to continue to do. We will see, or we will be able to know ahead of time, when students are going to be hitting certain barriers within our disciplines, within our courses. So, for example, the ARCS Reactor is a way for AI to support instructors to either remix or find different ways to help students through those motivational barriers. A classic one in the ARCS Model, is that my students aren’t doing the pre-class reading. [LAUGHTER] How can I reframe this in a way so that students see the value in that pre-class effort before they get into the classroom and don’t have something to add to the conversation? So I think one opportunity that we have with AI is to help us continue to see different ways of navigating through student motivational barriers.
Beth: I think, especially in my discipline of writing studies, and I think in all disciplines, AI has forced us to question some common types of assignments, some common approaches that have worked well, but because they’ve worked well, we haven’t really, within a classroom space as an individual teacher or as a discipline, we haven’t done a lot of perhaps innovation or questioning or thinking about how can we completely change this. And I don’t want to say that that applies to everyone, but it really has required each teacher to look at their curriculum and be like, “Oh, I’ve been doing this for 20 years, but what are some other ways that I could do this?” I think the revising a bibliography assignment by Lillian Campbell and Jenna Green and Nicole Bungert, they’re like “This has been a very common assignment in writing studies for a long time, but it’s so easy for AI to do it, but we still care about the learning. So how do we revise this assignment in order to be more effective? Not AI-proof, but in order to provide students with other opportunities to engage in the learning process.” And so it’s been exhausting, and a lot of work, but I’ll just speak for myself. I really have been forced to think very critically about what I’m having my students do and why. And since I direct a writing program and create curriculum for incoming graduate instructors, I’ve had to be thinking about that at the program level too. So there are, I know, some teachers who are still at the like, “How do I make this AI proof?” or “I’ll just get rid of that assignment because AI can do it.” But I know it’s also created openings for teachers who are like, it allows us to be like, “Oh, this was the essential core of why we were doing this.” How do we keep this learning and elevate it in some way and make it new? Education, we have to be able to continually update and revise and if we’re teaching our students that they have to do that, we have to do that as well.
Travis: And to build on that, I was at a conference a couple months ago, and Claire Major Howell gave a keynote at this conference, and she introduced me to a new term of the locus of cognition. So, for us to be thinking about what is the locus of cognition in this particular assignment, am I asking students to do all of the brainstorming, all of the APA citations, or is that just secondary to the actual learning task that I’m hoping that they’re going to be engaging in, and how can I remove some of the cognitive barriers that I don’t necessarily need the students to wrestle with, and help them to focus on what is needed, and what are the things that I’m okay with them offloading to AI or some other tool?
Rebecca: It’s interesting how it takes a disruption like AI to kind of reconsider what we’ve been doing, and also to remember what the value system is, maybe of a discipline or the ways of thinking within the discipline, and to make sure that those are the things that really come out in our curriculum.
Beth: This disruption has forced us to have new conversations, which I think is why even three years after the book is published, Travis, I don’t know about you, but I’m still getting comments from people saying how helpful it still is. Not because the AI technology is the same, but because the approach to thinking about teaching with this technology is still helpful.
Travis: Absolutely.
Rebecca: So, is there going to be a second edition of the book? [LAUGHTER]
Travis: That’s also a great question. [LAUGHTER]
Rebecca: We’re full of them over here.
Travis: The immediate answer is not yet. [LAUGHTER]Our center is currently thinking about a new project that we’re going to be asking about some chapter proposals around, and probably asking folks to contribute to another edited volume. And this one would focus more specifically on AI and the scholarship of teaching and learning, and thinking about how that raises another level of questions. For example, what happens when AI isn’t only part of the classroom, but it’s also part of the research process now? And when can we appropriately use it for maybe a literature review versus an instrument for development, or coding, or analysis, or our writing even. So how do we address consent? How do we address student privacy, authorship, and of course algorithmic pattern bias, and who owns that data? So I think those are some real questions that I know I’ve started to be thinking about, and I know a number of the journals that I’ve been publishing in are also wrestling with. So I potentially see that as our next upcoming volume.
John: And we really do appreciate the fact that you’ve released this volume and others in the past as OER resources that are available online for free for faculty because that’s a tremendous resource for people who want to explore things but face challenges in their budgets.
Beth: I think that’s another reason why the book is still being downloaded and used.
John: And the topics are still evergreen. All those issues that were raised there haven’t gone away. While the technology has changed a bit in terms of its capabilities, the fundamental issues haven’t changed all that much from the initial releases.
Travis: No, I agree, and I think because of the timing of when we did publish this volume, there were a lot of folks who were just first diving in to seeing what AI looked like in their classrooms and with their students. As more instructors are starting to engage in that first step as well, I think it’s a really valuable volume for asking these types of questions. And you know, how do we navigate this new world with AI?
John: And SOTL research on that is a very good follow up.
Travis: Oh, I agree. [LAUGHTER]
John: So we noticed that you had a very nice visual on your cover image for this book. Could you tell us a little bit about the origin of this?
Beth: In the introduction, all of the visual images revision to the book cover that Travis had done, that was an unexpected addition to our introduction. I didn’t know, like Travis just sent me the final cover of the book, and I was like, “That looks great!” And then as we were talking about the introduction and in the process of writing it, he’s like, “Yeah, it took me like 40 iterations.” And I was like, “We are publishing that.” And I still have my students read that, and that visual demonstration of what revision looks like has been so helpful because, especially in writing, it’s still so easy, and even for me as a writer to see someone else’s final product and to be like, “Oh, they’re such a good writer!” Like it just came out that way, or maybe you had to make a few tweaks, but to have the visual iterations that Travis went through to create that final product, and the kinds of questions that we can ask students in terms of, like, why did he keep going with that? Why didn’t he just stay there? What was he thinking? How was he thinking about his audience? How was he thinking about his purpose in terms of still asking Midjourney to continue revising that? That has been really helpful for students to think about revision as something other than just making a few edits. Maybe they’re not yet at the point in their learning where they can make revision at that scale, but seeing that as a goal and the kind of time it takes. For me, that is still a really good example, even though the technology has changed, of how we still want humans to be partnering and collaborating with the technology, but ultimately still responsible for the output. So I’ll just add that, Travis, I’m so glad you did that and that we ended up publishing it.
Travis: Yeah, thank you, Beth. I appreciate you bringing that up because it does really highlight not only that revision process, but also like the learning that goes into a tool. Like it’s not giving me the output that I want. I need to adjust the way that I’m asking this question, or I need to adjust the way that I’m approaching this to really get at what I’m trying to accomplish. I do think it’s important for us to teach our students or to model for our students what that process looks like. To your point, it’s a great cover image that came out as the final product, but there was a bit of a struggle along the way to get it to be the final product that I wanted it to be.
Rebecca: So, although you’ve already hinted at this, we always wrap up by asking, “What’s next?”
Beth: Well, Travis, I’m so glad that we did this podcast together because if we’re doing another volume, I’m really excited about that. The conversation’s not done. It’s not that we haven’t figured out the technology as a tool; it’s that we are still wrestling with what it means to incorporate this new technology into our learning, into our lives, into the work that we do, into the classroom. So I think there’s a lot still to be said. For me, I’m also looking at and continuing to try and publish and research like, how then do you also think about leading a writing program or a larger learning program, perhaps even an interdisciplinary program, where people can still have these productive conversations around AI? And also, like I really appreciated, Travis already mentioned it, the chapter where Catherine Denial was talking about how you have to start by trusting the students, and that’s something as I teach the new teachers how to teach first-year students that we emphasize and we talk about a lot. But I also recognize that when I am in my own classroom, teaching first-year students, even though trust is my foreground and how I start it, there’s still the wrestling of like “This is clearly just AI. Even though we’ve talked a lot about like, what do I do with that? How do I make this a learning opportunity rather than a now I caught you opportunity?” And I think that there’s still interdisciplinary conversations and research to be had about that, even if students are using it in a way that may be intentionally not helpful or unintentionally, how do we still create that as a learning opportunity and recognize the extra workload that faculty and teachers legitimately have to engage in to have those conversations? So those are things that I’m thinking about.
Travis: Yeah, that’s great. And for me, I’ve also been looking at this from a faculty development lens. How do we, in a center for teaching and learning, continue to help instructors think about these topics? How do we help those who are already engaging in AI in deep ways to think in additional contexts. How do we think about it in really measuring student learning? How do we know that our students are learning in that process?
Beth: Travis, I think that is an excellent focus for our next book. [LAUGHTER]
Travis: I agree. This has been a great conversation.
John: Which has long been a question that we haven’t really addressed, other than just doing things as we always had up until recently, and AI, as you’ve noted, has encouraged us to go back and revisit a lot of our old assumptions. So, that can be really productive.
Beth: Yes.
Travis: Yeah. I mean, we still continue to see there are certainly different camps of instructors. Folks who are absolutely against the use of AI, and looking for ways to prevent students from using AI in their classroom in ways that are maybe not ethical. There are folks who are still just starting to experiment and learn about what this new tool means for the way that they teach, and there are others that are really trying to innovate and push the envelope for what we can do in our disciplines.
Beth: Yeah, and I think for both of us in our positions as director of the CTE and director of the comp and writing program, sometimes just by talking about AI, I know that the faculty that work with me sometimes feel like I’m saying you have to use AI in a particular way in the classroom, and I really have to help emphasize that our goal is for you to wrestle with this the way that everyone is wrestling with this, whether you choose to like use AI in a particular way or not. But we can’t ignore the conversation. So, how do we create a space where we can still be having these conversations and learning from the process and the context, regardless of how each individual person engages.
John: Well, thank you. It’s great talking to both of you, and this is a really good resource. And we look forward to future conversations. I think the last time we talked to you, Travis, was five years ago, roughly [LAUGHTER] so it’s been good to get back in contact, and it’s great talking to you as well, Beth. Thank you.
Travis: Yeah, it was. It’s been a minute. [LAUGHTER]
Beth: Thank you for inviting us.
Travis: Thank you so much.
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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