440. Program-Level AI Responses

Posted in edtech, educational technology, higher education, learning, pedagogy, teaching
April 8, 2026

When generative AI platforms first appeared on the scene, faculty had to address these challenges alone. In this episode, Kathleen Landy joins us to discuss how program-level collaboration can help educators adapt more rapidly and effectively.

Kathleen is the Provost and Vice President for Academic Affairs at Genesee Community College. Prior to this, Kathleen has served in leadership roles at teaching centers at Cornell University, Queensborough Community College, and Mercy College. She has taught in multiple modalities and also has extensive K-12 teaching experience. Kathleen also served on the POD Network Core Committee from 2022 through 2025, and currently serves as the Co-Chair of the SUNY Council on Assessment.

Show Notes

Transcript

John: When generative AI platforms first appeared on the scene, faculty had to address these challenges alone. In this episode, we discuss how program-level collaboration can help educators adapt more rapidly and effectively.

[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]

John: Our guest today is Kathleen Landy. She is the Provost and Vice President for Academic Affairs at Genesee Community College. Prior to this, Kathleen has served in leadership roles at teaching centers at Cornell University, Queensborough Community College, and Mercy College. She has taught in multiple modalities and also has extensive K-12 teaching experience. Kathleen also served on the POD Network Core Committee from 2022 through 2025, and currently serves as the Co-Chair of the SUNY Council on Assessment. Welcome Kathleen!

Kathleen: Thank you so much! I am delighted to be joining you today.

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

Kathleen: I certainly am. I’ve got a nice mug here from the Ithaca Coffee Company, and it is filled with Irish Breakfast tea because it’s March and it’s approaching St Patrick’s Day. So why not lead with that?

Rebecca: I think you’re on theme. You’re good.

Kathleen: I’m trying. Yeah, thank you.

Rebecca: How about you, John?

John: And I forgot that we’re approaching St Patrick’s Day, so I have English breakfast tea, which might not go as well with Ireland in the same way.

Rebecca: Immediate conflict, [LAUGHTER] there’s immediate conflict.

Kathleen: I didn’t expect this so soon in the podcast, [LAUGHTER] the acrimony. Oh my.

Rebecca:I have a blue sapphire again, John, but it’s in my new favorite mug that I got while I was in Switzerland.

John: Excellent. We’ve invited you here today to discuss your February 28th 2024 article in Inside Higher Ed that addressed institutional responses to generative AI platforms. We recently ran across the article and thought these arguments were just as appropriate today as they were two years ago when you wrote this. At that time, ChatGPT had only been available for a relatively short time, and you argued that institutional responses were generally reactive rather than proactive. Are higher ed institutions still mostly reacting to changes determined by AI providers?

Kathleen: I’d say yes and no. And I think a really important disclaimer here is that anything I say from this point on is from the perspective of a dedicated practitioner, and I’m not conducting empirical study and formal study of many institutions higher ed, right? So this is certainly based on conversations with peers and my experience and my day-to-day goings on. And so I think we are still a bit reactive and I think that also varies greatly by institution and within institutions, by school and by program. So I think it really depends. I think we’re all over the road, to be honest. That said, I think, initially, folks were dealing with this reaction, or this reactive response, in isolation and kind of left on their own to explore or to experiment, and I think that probably remains true, but perhaps more at a broader organizational scale. So I think individual departments and programs are experimenting and exploring, and so I think there’s stronger or weaker degrees of coordination of those efforts across institutions. So yes and no, I know that that’s a pretty broad answer, but I think it varies.

John: And I think that applies to most things involving AI in higher education these days.

Kathleen: That’s fair. Yes, I agree.

Rebecca: I feel like faculty really experienced a lot of transition and having to respond to a lot of different things in the last five years, which is a little tiring. This included things like the shift to remote instruction in 2020, the growth of for-profit companies like Chegg, whose business model basically encourages [LAUGHTER] academic integrity violations, and this proliferation of AI platforms beginning in late 2022, and also the upcoming deadline for digital accessibility requirements. So there’s lots of different kinds of things that faculty are having to shift and rethink around. And faculty in the SUNY system have also faced a transition in their learning management system on top of everything else during this time period. Is it really reasonable to place so much burden of addressing these systemic issues in the hands of faculty? I guess the bigger question is, what role do faculty play, and then what role do institutions and systems play?

Kathleen: It’s a great question. I think it’s a loaded question, and I’m all right with that. I think it’s a fair question. I think, no, it’s not reasonable to put all of that expectation on faculty. And I think the second part of that is, I hope it’s not the case that it is the expectation. So no circle in which I chat, or operate, or work to serve the mission of Genesee, or any place where I’ve been, it’s never been the assumption that faculty need to figure that out, they need to figure that out on their own. So, because it’s such a critical responsibility, because it’s such a mission-critical responsibility, because faculty play this critical role as stewards of teaching and learning at any institution, I think they deserve the support. So if I’m doing my job decently as a provost, I’m trying to coordinate that support. I’m trying to do what I can to encourage a couple of different things. So the day-to-day work that I do as a provost, hopefully, is mindful of three things, assessment, professional development, and, frankly, wellness. If faculty are the stewards of the teaching and learning experience on campus, I work in service of the faculty, so I’m constantly trying to think about the assessment of it all. What do we need to learn? What do the faculty need to be effective in their roles? What do the faculty need? What development do they need? What do they want to learn in order to be effective in their role? And then, of course, the wellness piece, and that’s trying to keep tabs on everything from workload to culture and departmental environments and institutional priorities and the extent to which we’re pursuing them with the spirit of shared governance and collaboration in all the things. So no, it’s not reasonable to put everything on the faculty, and if senior admin are doing our jobs correctly, we’re supporting them in that endeavor and trying to work with folks to figure out what we need to accomplish next and how we can collaboratively do that.

Rebecca: One of the things that you’re hinting at is a little bit of prioritization. When there’s a lot of things going on, everything can seem like it’s in its own bucket, and it’s equally as important, but it’s hard to put attention on everything equally. So it seems like there needs to be some sort of coordination or integration of these different kinds of initiatives: accessibility, AI, and the list goes on.

Kathleen: Yes, I think that’s where organizational structure and communication and collaboration between those and among those structures is so important. And so that gets into organizational health, and how effectively are we keeping each other informed about the things that we’re doing and the priorities that we’re setting? If we’re working in a siloed, isolating environment, it’s so counterproductive, of course, so I think the sense of institutional priority that everyone’s working with is paramount. Right behind it is the health of the working culture, the working environment. So I agree there’s an emphasis on priority there. That’s important.

John: Now you mentioned two terms there that I was just thinking about in your earlier comments. One is culture, and the other is silos. Is part of the issue, where we don’t see as much decisions made at the department or program level, the fact that we do have a culture in higher ed of faculty working in their own silos, working independently, and not agreeing on as many approaches to teaching and learning. From what I’ve seen here, most faculty are addressing the challenges of AI entirely on their own, without even discussing it that much at the departmental or the program level, in part because they’re used to developing their own courses without sharing a lot of information with others. The whole notion of academic freedom and independence is a bit of a culture in higher ed that may actually serve as a barrier to cooperative work on this topic.

Kathleen: I think that’s an incredibly sympathetic question. There is a culture of knowing things and being the authority, and I think it’s built into the fabric and the culture of higher ed in general, and because instructors, because faculty members, are masters of their discipline, they’re experts in the field, it is so wildly disconfirming to not understand the task at hand, which is, “how do I incorporate AI into my disciplinary knowledge, my instructional practice, my assessment practice, my collegial conversations with colleagues, my academic discourse with students, with colleagues, with the field?” I don’t want to get too technical, but it’s super weird. It’s hard sometimes to admit that we don’t know something. And I think, without generalizing, that’s really hard for academics. You don’t want to show that card and say, “I’m not sure how to handle this. I’m not sure what this is all about. I’m totally fearful of this thing.” In the case of faculty, it’s on multiple levels: “Is a chat bot going to do my job in five years?” It might be so disconfirming in terms of the value placed on discipline specific knowledge. “I used to be revered for my encyclopedic knowledge of x, and now it’s a 30 second prompt. So what does that mean for me?” So it’s weird. On an individual level, it can be threatening. On a professional, personal level, I would even say on a deeply personal level, in terms of how we define ourselves and ourselves of identity… I don’t mean to get too metaphysical or philosophical on this… but I think it’s really hard to make sense of this. And I think when things were first unfolding, and there were multiple platforms, ChatGPT comes immediately to mind, of course, but select platforms were rising in such prominence in 2022, 2023, folks were in isolation, which was, in fact, the impetus for this opinion piece to say, “I don’t think we have to figure this out on our own. This is a departmental endeavor. Let’s talk about the things, let’s model learning for our students and each other. Let’s figure it out together.” So yeah, I do think broadly the culture of academia can be a little isolating and individualistic, but in my lived experience, the culture of teaching is collaborative. And “hey, I’ve got a student with this challenge, or I’ve got this module or unit or facet of my course that’s a little clunky or not flowing well, or the engagement isn’t there. What do you think?” In my lived experience as a teacher, it’s always been a communicative, collaborative endeavor, where I try to get better and better and better, because I think the best teachers are lifelong learners. So there’s a humility in that that hopefully combats the resistance that’s built into the fabric of academia that sometimes takes the form of resistance to admitting that we don’t know something.

Rebecca: How do we start to move in the direction of this more proactive, scaled, systemic response to AI platforms? We’ve kind of danced around the idea that there’s these silos, not everyone’s collaborating, there is the opportunity to collaborate, teaching can be really collaborative. What needs to happen to kind of nudge it to this more proactive direction so that we can have a more systemic cultural change?

Kathleen: Well, I think the first part of your question, if I heard it correctly, is, what’s prompting this evolution, if you will, or this progress? Frankly, I think it’s because the challenge or the problem isn’t going away, it’s here. It isn’t going away, and so we kind of can’t ignore this anymore as a sector, so as more and more institutions and individuals notice this and are kind of getting our bearings in the wake of this seismic disruption, I see lots more activity within and across institutions. And I think the second part of that, if I heard it correctly, is, how do we build upon that? Is that accurate?

Rebecca: Yeah or how do we launch it forward a little faster, [LAUGHTER] or really leverage the opportunity to address it in a more systemic way?

Kathleen: I think part of it involves our metacognitive approach to this challenge and our metacognitive recognition that there’s different facets to our institutional and individual responses. So I definitely see some curricular responses, and I think this varies greatly by department. For some disciplines, it makes so much more sense to have AI-specific program level learning outcomes. It just made sense, because AI is impacting their fields, their programs in such obvious ways. That’s not true for every discipline, obviously. So there is reluctance to touch the curriculum or to revise or modify or even review the curriculum in other disciplines, and that’s okay. In other pockets of higher ed, it’s more structural. I’m seeing so many more structures, organizational structures, that are responding to this moment, to these changes. And sometimes it might take the form of an ad hoc committee, it might take the form of a Senate subcommittee, it might take a form of a… you name it. So I think there’s structural responses. Folks are starting to self organize, and I think that’s good. And I think if we’re mindful of that, and in turn, try to be planful about that and thoughtful about the structures we create in our own institutions, that’s another facet. Yet another facet involves some of the policy-related work that’s going on. Lots of institutions are drafting policies and doing so, quite rightly, collaboratively, trying to incorporate faculty perspective and working through channels of shared governance. Those are good things, I’d say, finally, another facet is the cultural piece. I see folks sharing things so much more often. Just this morning, a colleague shared an article with me from e-campus News, and it’s entitled, “The AI-resistant classroom is a myth: Designing assessments that assume AI is present,” and while I don’t know that that’s something I would offer like “this is the blueprint,” it’s thought provoking, and it’s beautiful fodder for conversation with fellow administrators and faculty and the assessment committee with whom I work closely. There’s so much out there, and I feel like folks are sharing so much more. So, I think a notable shift, and something that we can intentionally encourage, is the shift in the tenor of the conversations that we’re having with each other and that we’re prompting with colleagues, that it’s shifting so much more away from, “Oh my goodness, how we’re going to handle this? This is so worrisome, and this is something to fear” to “These are opportunities and what can we achieve? What do we need to achieve what do we want?” So, that might have been a long-winded answer, but I think it’s so multifaceted. I think taking stock of how we’re responding to this moment is an important part of continuing to respond to this moment in a productive way.

John: I may have overstated the degree to which faculty are working in silos earlier, because I know on our campus, we do have a lot of those discussions, but the discussions tend to be among those who are exploring AI as one group, and then we also have a lot of discussions among those people who just don’t want to have anything to do with AI as a different group, and they don’t always talk to each other as much as might be productive. Even within departments, I know of one department where one of our major AI advocates on campus also contains someone who is one of the most opposed to any student use of AI. And so that presents a little bit of a challenge in building some of these programmatic approaches. Do you have any suggestions on how we can bring these people together in a productive way… to at least come up with some shared principles or objectives?

Kathleen: I do, yeah. I think recognizing that there’s, as you just described, there’s such a range of responses to this. Some are logical, some are emotional, some are deeply discipline driven by discipline-specific thinking. But there is variability, and we know that. So I think the ways to bring people together… this is not an earth shattering statement, but… need to be really thoughtful. It can’t be one and done. It’s not an hour with cookies, although I do like a good cookie, let’s not have that come across the wrong way. But I think it’s frequency of meetings or just opportunities for discussion. I think it’s the structure. I think it’s the clear communication about the purpose of getting folks together. There’s a few different pieces to that. If there’s a group of folks who were super recalcitrant, they’re super resistant, I think there’s a number of different strategies to employ there, and one is modeling interesting or effective use of some of these platforms. I think it’s creating low-stakes experiences with platforms to facilitate greater exposure and to generate intellectual curiosity, to take the sting out of that isolating expectation that folks experiment on their own. I think communicating the intent, as I mentioned before, and I think most importantly, grounding these conversations and these experiences in the mission of your institution. There may be such variability among folks on a campus with regard to their perceived value of generative AI, but what they all have in common should be the mission. And so if I can frame conversations about, “Hey, what does this platform or this use or this potential use for AI have to do with your program? Have to do with the students’ experience?” That’s, I think, what connects us all, and that’s what I think should be the impetus for these conversations. What is best for the students, what is best for our viability as a program, to ensure that we’re still relevant, that we’re still accomplishing for students what we say we will. And I think that’s a good starting point. I think framing those experiences where you’re hoping to achieve consensus, I think framing them is really important. It’s a good start.

John: One of the things we did last year was we had 24 faculty participate in a SUNY IITG-funded initiative where they spend a little over a semester developing some activities that they could embed in the classes and experiment with, but the first day of that, we had people together all day, and it was really one of the most productive sessions I’ve seen, because at the start we saw this wide polarization, but then the people who were really opposed to AI got to see how it might be productive for them, and they got to see some examples from others of how it might be helpful for students who might be left behind without some additional support. And at the other extreme, the people who were really enthusiastic got to hear some of the concerns of the other people and see how it could be used in ways that are detrimental. And I think everyone left there with a much more balanced perspective at the end of that day. And then we met regularly throughout the semester, and it was really nice to see people coming together there. The challenge is getting them all in the room in the first place.

Kathleen: So that’s a great example and thank you for sharing that, and it prompts me to share this example: For that list that I just offered, in terms of trying to demonstrate effective use of select platforms, and trying to give concrete examples of how this might be beneficial, to try to model that. And so an example that might be worth sharing is that we are up for our middle states reaccreditation. In fact, our site visit is in a couple of weeks, and we’re trying to get the word out, “Read the study! Read the study!” We want folks to engage. It’s a really thoughtful self study. We’re proud of who we are. We’re mission centered. We do good work. We know what we’re working on. We’re humble, we hustle, all the things. However, I know that it’s a tough sell to get an entire campus community to read a 100-page document that is evidence rich, and perhaps to some, not the most dynamic document. It might not be the page turner we hope it is or that we think it is. However, I leveraged a platform to make summary podcasts for each chapter, and I shared these along with the self study, and we shared them broadly with the campus community, and it is truly trying to model this in the spirit of UDL. Here’s multiple means of representation. You can read this and then you can listen to this, and it’s intriguing and it’s entertaining and it’s good and it’s a decent use of a handy application. And I’m hoping that folks will listen to that podcast and think, “Oh, wow! Maybe I could do that in my sociology class. Maybe I can encourage students to create some summaries of some reading assignments, not to totally supplant them, but to supplement and to reinforce learning.” And so that’s one example that your example has prompted me to share. Yeah, I think there’s so much that we can do. Your example also speaks to the idea that it shouldn’t be one and done. It’s not an hour. We need enough time to engage with each other, with these platforms, to make sense of something before we can even speculate on how to best incorporate it into our curriculum, or not.

Rebecca: Part of what I’m hearing both of you say is, it’s important to have someone who’s watching the bigger conversation on campus and can call these meetings to happen. To use the teaching center to have a whole-day workshop, or have administration say, “Hey, we’re going to dedicate this day to having these conversations. We invite you to all come.” Someone has to take that first step to: A) recognize that the different populations exist, and then B) intentionally bring them together.

Kathleen: And I think bring them together in different ways and different modalities, dare say. I mentioned sharing resources before. At GCC, we have a week of professional development days in advance of each semester. And so I knew just by being, you know, a good SUNY citizen, and by being connected with colleagues and peers, I knew that SUNY Finger Lakes Community College has been doing some fabulous work with their AI hub, The FLX AI Hub. And so we welcomed folks from Finger Lakes Community College to our campus to help us and to give some really thoughtful overviews of some ways to integrate AI into the teaching and learning endeavor, or at least to consider what those integrations might look like. And one of the follow-up activities that full disclosure, I have not yet sent invites for, but I bought the books, and that’s something. So the wonderful Dave Ghidiu from Finger Lakes Community College had recommended to us a text that he loves, The Opposite of Cheating by Tricia Galant and David Rettinger. And frankly, I want to have a provost book club. I want to get folks together with no expectation that anyone show up with the answer to all the world’s problems. I want us to come together, read something thoughtful, listen to each other, and engage in that dialog that you described, John, that some folks, they have really valid concerns that everyone needs to hear and we all need to respond to. And others have done trailblazing, innovative work that everyone should hear and we should all try to emulate and so there’s such a discourse, which I think accomplishes a few things. It helps everyone improve our instructional and assessment practice. And I know this might be cheesy, but I’m a cheesy person, it stokes that fire of intellectual curiosity that drives us. We went into these fields, into this endeavor, into academia, because of that curiosity. What a moment to be in academia. Of course, it’s daunting. Of course, it sometimes feels laborious, perhaps, but we’re not alone, and we get to figure things out together and I think that really is a gift.

John: And I want to include a couple of shout outs, one to Dave Ghidiu, who has been on the podcast in the past, and also to the authors of The Opposite of Cheating. We’ll include links to the podcast with both Dave and with the authors of The Opposite of Cheating in the show notes for this episode. And also, we did a reading group on our campus last semester, and we’ve even had a member of your campus participate in the reading group, a member of your math department who was a really active and enthusiastic participant in that reading group in the fall semester of this year. We’ve been running generally one or two reading groups a semester for faculty for the last eight or nine years now, and they’ve been really productive. And we’ve done them in conjunction with other campuses. For about six years, we were doing them with SUNY Plattsburgh when Jessamyn Neuhaus was there. That broke down after she left and moved to Syracuse, although we did read her book after her move to Syracuse. They’ve been really productive ways of bringing people together and sharing their teaching practices and building this sort of community. We just wish we had more people in them.

Kathleen: It’s how things grow, and I think it’s so multifaceted. I mentioned our accreditation process. I’ve partnered with Deborah Ortloff, also at Finger Lakes Community College, to really explore and learn from her ways in which generative AI can help in the accreditation process. There’s so much that we can benefit from, and it’s really eye opening, and I think that initial exposure is really important, and then that low-stakes period of experimentation and just making sense of things with people and not doing that in isolation, great things can happen. And I’m not trying to sound like Pollyanna, I’m not trying to sound like there aren’t some daunting pieces to this, but we have great colleagues to figure things out with. That’s a joy. So why not embrace that?

John: I wish we had access to NotebookLM when we last did our accreditation, so that we could generate those podcasts, because when we did ours a few years earlier, I had to do some podcast recordings with some of the people leading that and then edit them all and post them to provide the same multimodal experience. But if those documents could have just been uploaded and generated and shared, it would have saved me many, many hours of work. But again, having those audio versions is really good, because we know multimodal presentation helps with learning for faculty as well as for students.

Kathleen: That’s right and dare say, it’s fun. It’s intriguing. I was delighted to hear those because I was just in awe that, within minutes, this was generated, and it was entertaining. I have to say it was great.

Rebecca: One of the things that we’ve hinted at but haven’t directly talked about is perhaps the need for policies to be developed related to AI. Some of this might happen at the institutional level, and some maybe at the programmatic level, or course level. Can you talk a little bit about how you see what kinds of policies should be developed at what level? What kinds of things should be in, like an institutional policy, and what might be more at a programmatic level?

Kathleen: So at the institution level, and I hope this doesn’t sound in any way like evasive, or, dare say, even cheeky, but I think a really good institutional policy is to have a course level policy. I think there’s a danger, as we all know, of the potential for institution- level policies to become overly prescriptive and curtail academic freedom. That’s to be avoided, obviously, but I think having clear expectations that departments, programs, individual courses, establish and communicate clear expectations for the students in those courses and programs. I think that’s essential, because not having a policy renders everybody guessing, and then the rules aren’t clear, and then the expectations aren’t clear, and most importantly, the learning isn’t clear, because part of the, and this is a segue to the course level consideration, for course-level considerations, or even assignment level for that matter. Generally, folks will fall into one of three policies, either we allow AI, we allow selective or limited use of AI, or we don’t allow it. Each of those is fine. I think it’s reasonable for a department to get together and discuss, “what is our department-level policy?” From my perspective, it’s perfectly reasonable to say every instructor of every course within this program needs to have a clear policy and communicate the rationale for that policy to students, because if students understand the rationale behind a policy, if they understand the policy, they’re more likely to follow it and to adhere to that policy and to be guided by that policy. So, I think at the institution level, expectations that are specific yet flexible are prudent, with the caveat being that they shouldn’t be overly prescriptive. At the department level, I think clear expectations that provide adequate guidance to faculty with regard to what they need to include in their syllabus or their course descriptions or their assignment directions, it has to be adequately specific yet flexible. Same principle there.

John: One of the challenges that students face is that they may end up with very different requirements, and at least putting it in the syllabus and describing and having a conversation with students is a start. But you also mentioned that, perhaps, whenever faculty give assignments, they should list those things, because students don’t always go back to the syllabus, and it could be confusing when you’re faced with five different policies in five different courses, or even in a course… AI may be allowed for some types of assignments, but not others. And that’s a lot for students to keep track of unless it’s being made explicit at each stage. So should faculty also include whether AI is used, not allowed, or is used in a specific manner, in every assignment where there may be a potential for use?

Kathleen: I don’t see a downside to that. So a lot of things go back to the mission and what’s best for students. If learning is the goal, then everything we can do to facilitate that learning is worth pursuing. So, with sympathy, if a student is taking five different courses with five different course-level AI policies, why wouldn’t we communicate this clearly and frequently to students? I think there are things that departments, or dare say, even institutions, can do. I don’t think we’re quite there yet, but I think there are things that we can do, such as having a template, let’s say, for course-level policies or sample statements, so that there’s a consistency with what students encounter, even if there’s variability in the degree of use that’s permitted across courses. Hopefully, if things look similar, if things are consistent in how they’re communicated and how frequently, I think that helps. I also think it is up to individual instructors to determine what’s best for their course, and I do think it’s a skill set that students need to develop, to literally or metaphorically read the room and to adapt, because that goes for us too, as professionals, as teachers, we have to learn when it’s appropriate to use or not to use or to use in a limited way, AI. So there’s also the metacognitive skill of helping students practice paying attention to the rules as they navigate their programmatic experience, so it’s layered and it’s nuanced. But to answer your question, I think there’s potential for things to get pretty confusing for students who are taking multiple courses with varying policies on AI, but we have to help them keep track of that and reminders help, presenting expectations in multiple places helps. It’s not enough to say it once in class and expect that everyone retain that forever. We have to document it. We have to share it. We have to communicate it multiple ways.

Rebecca: Your description of templates is making me think about Creative Commons licenses and the human readable versions of those licenses, where you can kind of mix and match permissions [LAUGHTER] or levels of what’s allowed or not allowed, and I could imagine some AI policies following a similar format. “You can use AI as long as you provide documentation that you used it,” or like whatever the rule might be, but I could imagine something like that being really useful for students to keep it at that human readable clear way.

Kathleen: And I would never use the word “Mad Lib”, but it’s kind of, you know, “for this course, it is my expectation that generative AI is used to: fill in the blank.And this is why.” I mean, we might want a more sophisticated term than “Mad Lib”, but template works, I think, if students know where to find this information. We’re actually doing this now at Genesee. So I meet routinely with one of our student government association officers, and he and I are collaboratively working on a draft of guidelines for conversations about breaches in academic integrity that involve AI, how to handle it. And so our hope is to, hopefully in the next three weeks,… well let’s say four weeks, hopefully in April… to gather a group of faculty and students so that we can learn from each other and refine these guidelines and socialize… I don’t love that verb… but socialize these guidelines. We’ve been engaging with folks from our Academic Senate Committee on academic integrity, our standards committee, and so we’re collaboratively trying to create a resource that will help folks engage in conversations. What to do if you suspect a student has used AI when they shouldn’t have? What should students ask at the beginning of a class of a faculty member if they’re not sure what the rules are? And so having this resource and the associated conversations that will hopefully be empowering for both faculty and students, that’s part of the cultural piece, it’s part of the resources piece, it’s part of the sharing piece. It’s all these facets of how we can make the student experience more enriching and more in line with what we’re hoping to accomplish. It was a bit of a tangent, but I think the idea of bringing more folks in to consider how to mitigate the confusion that students might experience. That’s a good thing, including the students, especially the students.

Rebecca: One of the biggest challenges associated with generative AI use in the classroom, or with student use of it, is the reliability of online assessments. And really, this is for courses, really in any modality, because we all use online assessments for things. What approaches should faculty and institutions take to ensure that faculty and students are able to reliably assess the progress of student learning?

Kathleen: It’s an excellent question, and it’s one that has prompted so much conversation on my own campus, I know on other campuses. I have a few responses to this. One, what comes to mind immediately is the prominence of tech responses to this challenge. So whether it’s proctoring and anti-cheating tech platforms, I think they have their place. It depends on the day. I have some mixed opinions on the ultimate efficacy and effectiveness of one upping, I think. Sometimes I’ve heard the term “arms race” used, and I don’t love that for a couple of reasons, but I can see how if there’s platform A, platform A-Prime comes out three months later, and then it’s a race sometimes. So I think there could probably be, and likely is its own podcast to address some of the tech platforms and the responses to that, but if I may focus on what also comes to mind… For me, surprisingly, I think there’s a personal approach to mitigating the likelihood of cheating. That comes down to relationships and communication. So I think those are at the heart of my philosophy, philosophy of teaching, philosophy of assessment. Whether there’s a tech intervention or a tech solution or no tech solution. I want students to appreciate why they shouldn’t cheat in my class, and if I’ve done my job well, if I’ve communicated that well, I want students to second guess that decision. I want students to be less willing to cheat because I have already done a good job of communicating why this learning is essential and why they shouldn’t circumvent the learning by cutting a corner or by using an AI-generated form of assistance when they shouldn’t. The other piece of that is the relationship. I want my students, and sometimes, I grant you, this is really tough in a larger lecture, I get it. So class size is a factor here, but I want my students to feel the faith that I have in them. I want them to not want to cheat [LAUGHTER] in my class, and I’m not trying, once again, to sound like Pollyanna or that I’m the eternal optimist here, but I know students cheat. I know there will always be a few who try to cheat, but I want my relationship with the class to be an impediment to that instinct. [LAUGHTER] I want folks to second guess like, “Wow, Kathleen has such faith in me and I don’t want to compromise that.” I want the relationship to be its own disincentive. And I want the communication of, “Hey, this is why I’m asking you not to cheat on this.” Can you cheat? Probably. Sure. But should you? No, of course not, and here’s why. So I think there’s a surprisingly personal part of my own teaching philosophy that informs this part of that answer. I also think there’s a real place for authentic assessment, and of course, modality may play a factor here, but to the extent to which learning is applied, authentic, that can mitigate the likelihood that students would outsource it to an AI platform. The same goes for metacognition and the assessment thereof. If I have an assessment or an assignment that takes a look at process over product, or if I have something that takes a look at students’ own metacognitive reflections on how they did, it calls for more self assessment, calls for more personal reflection. I think that also mitigates. It could be a whole ‘nother podcast to get into. I’m not going to outsource to a platform an articulation of how I feel about how [LAUGHTER] the process went. So I think there’s the communication piece, the assignment design piece, and the potential for a tech solution as well. I think those three things are relevant and connected to my pretty long answer to that question. I think it’s multifaceted.

John: And those are all great solutions. One of the things, though, that many people are increasingly concerned with is the growing availability of agentic platforms that can log into a course management system, check to see what work is due, complete that work and log out without any intervention other than the student giving it the URL, the login ID, and the password. And at the other extreme, there’s also concern that faculty could do the same thing. That these same platforms could allow faculty to set up an agent that would go in, check within the learning management system to see if there’s work to be graded, grade that work, provide notes in the feedback column in addition to a letter grade, and then log out again without any intervention from faculty. And we’re starting to see a little bit of that happening, and that’s an area that the learning management platforms, as well as the AI platforms, should be working on a bit more actively. In the meanwhile, we’ve got a lot of concerns coming up and having discussions with students, working with colleagues, and coming up with the best institutional responses, but sometimes it’s going to have to go a bit further than that, I think, to resolve some of these new challenges, because both sides of this are racing. and I work with AI quite a bit, and every time I go on social media, I get ads for these things that can do all the work for me in my courses, where I just give them some money each month, and it will do everything and guarantee that I got a high grade with an extremely low chance of being caught doing that. So students are faced with a lot of temptation, and to the extent to which we can build relationships, that will help.

Kathleen: Yes, and I think to your point, our committing to staying on top of how things are escalating and how platforms are becoming increasingly sophisticated, or, dare say, elusive when it comes to detection, etc., I think our commitment to staying on top of that is paramount. We have to know what we’re dealing with here in order to even have a hope of designing assignments and learning experiences that can circumvent that. So it’s daunting. I think I would find me tedious if I came on here [LAUGHTER] and I thought I had all the answers. I do not, but I do know that groups of folks getting together who are committed and passionate and knowledgeable and curious, I think us figuring it out together, we have a far greater likelihood of achieving what we’re hoping to, but it is daunting, it is. I would never suggest otherwise. For me, the relationship piece is huge, and the way I can structure reminders about what’s expected, whether it’s reiterating PLOs up to and including ethics. There’s a lot of talk about ethics and our shared responsibility for instilling a deep sense of professional ethics, research, methodological ethics, personal ethics, personal integrity. Where does that live in our curriculum? Which goes back to the original thought here that we have to take a look at the ways in which AI is impacting our fields, our teaching, our assessment, and do what we can to be proactive and review, refine, revise our curricula, our assessment practice, in a manner that will empower students, because if we don’t, they are vulnerable to what we didn’t teach them, what they didn’t get out of their learning experience during their time with us. And so it’s mission critical. There’s nothing more important than saying, “Okay, I need to know, as a teacher in field X, I need to know how AI is impacting my role as a teacher, but also my field, my discipline,” and if I can’t communicate that to students, if I’m not helping them navigate that with me, that’s awkward. That’s a failed promise, and nobody wants that. I have such great faith in my colleagues. I’ve never worked with a more committed faculty, a more committed campus community. It’s beautiful. And so this is mission critical. We got to think about how we can best serve students by responding to this moment in a multifaceted, planful way.

Rebecca: One of the ways to respond that we haven’t directly addressed, but might be worth mentioning in this context is we can get insulated in departments, get insulated within our institution. But there’s a real need for the higher ed sector to work together to promote state/federal regulations around some of these things too. We talked about incentive for students to leverage [LAUGHTER] these tools to skate by, but there’s also a real incentive for these companies to keep finding ways to circumvent [LAUGHTER] any of the security in place. So I think we don’t talk about that enough, our role as educators and the ways that we can participate in moving potential regulations forward. I think that what I’m suggesting is like, we don’t really talk about that much, but it seems as though there’s a role we could play.

Kathleen: I think so. And I know I’m using this word a lot in this conversation, but I think it’s a multifaceted approach. So one of the appeals I would make is that this is an equity issue. Increasingly, some will have great access and exposure to AI platforms, and will benefit from that, and will learn how to use these platforms ethically in a manner that serves themselves and society well. Others will be boxed out of that for any number of reasons, up to and including socioeconomic status. So if we can have value laden conversations, in the best sense of the term, we can keep this personal in a weird way if we cannot lose sight of the human impact of technological advancement here, I think that’s the name of the game when it comes to advocacy. And then we all just work at scale, whether it’s organizational or societal. We influence the circles in which we operate to generate smart conversations about, “Hey, what do students need to learn? What do students need to be empowered to be able to do or not do with or without AI?” [LAUGHTER] For me, the advocacy piece takes the form of what can we do to stave off vulnerability, either at the individual or organizational level, and for the individual level, it’s often on behalf of the students. How can we advocate on behalf of students? So, I don’t know if that fully answers your question, but I see a lot of things structurally, and I see different scales and different facets, different points of entry, if you will, for the conversation. And so equity would be one of my points of entry for the advocacy piece.

John: And as we’re all wondering, when we’re talking about AI, what’s next?

Kathleen: That’s a great question. I’m going to just pop that into ChatGPT, if you don’t mind, I’m just going to “what’s next.” [LAUGHTER] Oh, boy. Honestly, I think connecting with more and more folks and staying connected and having the conversations and taking a deep breath and figuring it out together. I think that’s what’s next. I think it’s about incremental change and incremental responses and just huddling up and doing what we can to honor the mission of the institutions we serve. That’s what’s next, I think.

John: It sounds like a great approach, and I think we need to do a lot more of that. Thank you for joining us. And we’re looking forward to that next conversation.

Kathleen: That sounds lovely. This was so delightful. It was really a treat to talk with you both. Thank you.

Rebecca: And we’re looking forward to the next time to huddle up, for sure. [LAUGHTER]

Kathleen: Likewise, thank you.

[MUSIC]

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.

[MUSIC]