Category: peer review

425. Authentic Voice in the Age of AI

Posted in critical thinking, feedback, grading, higher education, learning, metacognition, pedagogy, peer review, reflective practice, teaching, writing
December 24, 2025

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

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

Show Notes

Transcript

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

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

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

John: …and Rebecca Mushtare, a graphic designer…

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

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

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

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

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

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

Rebecca: A classic.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Rebecca: Or podcasts for that matter, John.

Anna: Yeah. [LAUGHTER]

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

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

Rebecca: Thank you.

[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]

398. Multicampus AI Initiative

Posted in active learning, artificial intelligence, higher education, inclusion, learning, pedagogy, peer review, professional development, teaching, writing
June 18, 2025

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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342. Infographics

Posted in attention, authentic learning, engagement, feedback, higher education, learning, pedagogy, peer review, rubrics, teaching
May 22, 2024

When papers and projects are due at the end of the term, students often procrastinate even when the projects are carefully scaffolded. In this episode, Michelle Kukoleca Hammes joins us to discuss how a series of infographic assignments, combined with peer and instructor feedback, provide an engaging and productive learning experience. Michelle is an associate professor of political science and a CETL Fellow for the Center for Excellence in Teaching and Learning at St. Cloud State University.

Show Notes

Transcript

John: When papers and projects are due at the end of the term, students often procrastinate even when the projects are carefully scaffolded. In this episode, we discuss how a series of infographic assignments combined with peer and instructor feedback provide an engaging and productive learning experience.

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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.

[MUSIC]

Rebecca: Our guest today is Michelle Kukoleca Hammes. Michelle is an associate professor of political science and a CETL Fellow for the Center for Excellence in Teaching and Learning at St. Cloud State University. Welcome, Michelle.

Michelle: Hi. Nice to be here.

John: We’re looking forward to talking to you. Today’s teas are:… Michelle, are you drinking tea?

Michelle: I am. I don’t always drink tea, but I made sure this morning I did. And so I’m actually just drinking a green tea, a milk tea. It’s not quite as good as others I’ve had. I have a colleague from Wales who makes the best green tea, but it’s good this morning.

John: …and Rebecca?

Rebecca: I have a Hunan Noir Tea, which is very tasty.

John: You left out the Pinot.

Rebecca: I know. [LAUGHTER]

John: Okay. And my tea is a black raspberry green tea.

Michelle: Sounds nice.

Rebecca: So we’ve invited you here today, Michelle, to discuss how you’ve been using infographic assignments in your online comparative politics classes. When you first started teaching this course, what kinds of assignments did you use?

Michelle: I use a variety of assignments. I’ve been teaching this course for 27 years now. And then this is mainly for my intro level comparative course. But I’ve adapted it for upper levels too, so mainly traditional assays early on. As I moved to the online environment, I would still have them do a major paper of a country study. And so pretty traditional assignments for the most part, but I wanted to make some changes for the online environment especially.

John: So what made you decide to change to infographics?

Michelle: Well, a few things. One is I felt that students were doing these papers that were quite lengthy in their country studies, and they would turn them in at the end of the semester. And we never really had a chance to talk about the elements of them in depth prior to that. And so I really wanted to change that. So it wasn’t just the end of the semester. And they weren’t sharing them with each other, because of the format of them as just being a lengthy paper, it wasn’t the kind of thing that they would be interested in exchanging with each other. And then frankly, I was getting a little bored, [LAUGHTER] the format, not so much the content or their insights, but the format just became very boring. We all know that when you’re working with one individual paper, it can be really exciting to have that dialogue with the student. But when you have a stack of papers, it suddenly becomes too daunting. And so I was just looking for a way to make us all happier, actually.

Rebecca: That sounds like a good motivation. [LAUGHTER] Can you talk a little bit about what the infographic assignment is?

Michelle: So I will talk about in terms of that intro level comparative politics class. One of the things that there’s not time to do in any semester is to really cover every country in the world, obviously. First of all, I wanted to make sure that students could pick their own country that they have an interest in, and that would broaden the number of countries we would cover. And then I switched it to infographic for the online component because there were a few things I wanted to do with it. One is to retain the goals I had in mind for them learning about a country. But also then I wanted to tie in some best practices for online presence in the classroom, for example. My discussions, I was also unsatisfied at the beginning, we learn as we go along, and my discussions tended to also be maybe a little stale, or I’d throw out a question and I would get very similar answers from everybody. They were going back to the textbook and giving me something rather than being creative or energetic in their own answers. And so the way the infographic works is I have about 10 through the semester that they have to do. And so it’s basically breaking up the country study into smaller pieces, so that they have different topics as we go along the different topics in the class. And then before they submit them to me, they submit them into the discussion for the appropriate week. And that way, all the other students can see them, it gives them a lot of different skills. One is that they are able to critique other people’s work, they are gaining more knowledge about a variety of countries. It allows them to practice skills on their own in terms of working together. So I don’t see these always as only individual assignments, although that’s how they’re graded, but kind of a group work assignment that they can all enhance each other. And so there were a lot of ways in which I wanted to bring different elements into the same assignment, particularly because I didn’t want to have to think of how to bring these elements in and do different assignments for each thing I wanted to bring into the classroom. Having one assignment that was consistent really helped them.

Rebecca: It sounds like by having a consistent assignment, you do have to learn how to do the assignment over and over again, that part of it is covered. You do it one time and then it’s clear how to do the assignment and then you can focus on the content and the material.

Michelle: Yeah, exactly. And one of the other things is, unlike just having one country study at the end, there was a lot more formative assessment that could go into it. So exactly that, where we’re able to help build skills up early on. Some of the content early on is pretty simple. The first one is making an infographic about the demographics of the country. And so it’s pretty simple, but it allows them to get the first skills if they’ve never used, for example, Canva, or other programs that allow them to make an infographic. So they get their feet wet with that. It allows them to think about how to communicate in a different way other than a paper, because I also began to think about my intro level students who many would become political science majors, but many wouldn’t. And so in a course that they were using for general education, they’re not going to be writing political science research papers. And so allowing them in different ways to express themselves I felt was another skill that they could bring.

Rebecca: It was nice to see this scaffolding, separating out some of the technical skills from the content, because often we’re doing all of those things at once. And it sometimes can be unclear whether or not it’s a technical skill that is lacking or the lack of understanding of something. So separating those out of it really does seem like it would help have a clearer sense of where students are. And, as we know, [LAUGHTER] if you try to ask students to do too many things in one bigger assignment, then certain things end up edited out. And that might be the thing that was really critical. So it’s nice to have those separate assignments to focus in on those.

Michelle: Yeah, exactly. And I really enjoy the fact that, rather than focusing on grading a long paper, I’m able to go through that process with them. And then also in discussions, it gives me so many opportunities to re-teach something. So if I’m noticing in someone’s infographic that they’ve made an error, or they have a misunderstanding of a concept we’re working with, then all the students in the class can see me correcting that and doing it in a gentle way, that seems a little bit less high stakes for students that because it is just this one assignment, and I’m making this one little correction here, but I think it benefits everybody.

John: So for those students who haven’t done much work with infographics or with graphics in general, do you provide any instructions or recommendations to them? You mentioned Canva, do most students use that? Or is it a mix of tools?

Michelle: They usually do end up using Canva, because that is one of the tools that I put in the directions. And so I have quite lengthy directions in terms of guiding them through accessing technology, allowing them to know that they can use various different technologies, depending on their accessibility to them. There are some really great programs that of course, cost money or pro versions. But I let them know that for our purposes, they can certainly just download a free version of a program. They could really even do it in the Microsoft Office Suite in a variety of ways if that’s where their skills lie. And of course, that’s something that all the students have access to on campus. And so I do get them lengthy directions, they’re not all my directions. I don’t feel like I need to reinvent the wheel. So I just include several videos from YouTube that talk about how to use Canva. And those are sufficient and actually probably better than I can do. And so I put those out there with other directions, I give them alternatives. And then of course, if they’re still stuck, then they can always work through it with me. We can have a Zoom meeting where they pull up their materials, and we work on putting it together.

Rebecca: You mentioned that students were sharing infographics with each other, it sounded like maybe through a learning management system or something. Can you talk a little bit more about what that sharing looks like and the kind of feedback that students are giving one another.

Michelle: So the learning management system we use here is D2L Brightspace, and there is of course, like in most learning management systems, there’s that built in discussion feature, so I just use what’s already there. I don’t mind using third-party applications., but it just tends to make things trickier. So I just use the discussion. Have them post it right to the discussion, and have them use the discussion tools, whether it be just typing in or using some audio or other tools to give feedback to each other. And I find that the discussions are so much more lively. Even more than I expected. The first time I used it, it was for a summer class and I thought “okay, I’m gonna try this now because it’s summer, and no one wants to be sitting writing a 20-page paper in summer. What am I going to do?” So I put it out there for the summer class and I thought, Okay, well, maybe they’re busy with other things or they’ve got excitement in their own life over the summer or they’re working a lot of hours. I had never had such lengthy discussions go on in any class before that. They really enjoyed looking at each other’s work rather than just from me. Many of the students chose countries that reflect an interest they have, maybe it’s somewhere that they’re actually from, although I usually try to encourage them to broaden a little bit. But that also brings such a benefit to the rest of the students. So we do have students who do that. I have students who maybe have gone on vacation somewhere or have a family connection somewhere, I’ve had some that have been stationed in the military, and come back, and they want to talk about what they noticed when they were there, so they put a lot of that in the infographics. I found just use the basic tool, but allow it to be opened up for them. And I also try in discussion, and I do this even in other discussions that I have online, because I usually try to hang back for the first bit, quite a bit. I allow that to be organic. And then I’ll go in and make sometimes just a summary of everything that’s been said or things that I need to correct. So that it’s not simply another lesson from me. But I have that ability to correct when I need to.

John: You mentioned the first infographic assignment had them work with demographic data and display that, to what extent are these assignments tied to the specific course work that you’re doing during each of the 10 assignments?

Michelle: For most of those 10, they’re directly tied to the material for the week. So for example, the textbook will have a chapter where we’re talking about government structures, presidential systems, parliamentary systems, et cetera. And so the infographic for that week will be directly related. So they’re going to do an infographic laying out the government structure. And then another module will be on political parties and how they work in various parts of the world. And so it’ll be directly tied to: “So show me your country, and show me the main political parties in your country. Tell me what their main ideology is. Tell me who’s in power now… which party? And what does that mean for the policies that get put forward?” Another one would be one on current events, the last one that they do is: “Well, what’s going on there now? …and “With everything you know about your country now, what do you predict for the future for them? How do you think they’re gonna solve this?” So a little bit more policy oriented… also, “look at what they’re actually doing in terms of legislation and implementation.” And I also do one… I don’t use the textbook anymore, but it’s something I used years ago that I retained the content in a module, which is to look at the concerns of any country in terms of every country’s concern with prosperity, security, and stability. And so I asked them to just put out an infographic that asks them to assess threats to those things, and looking at their country. And so it’s a little bit on the current events side, but it’s focused for them in terms of every country needs to be aware of its security… what’s happening?

Rebecca: You’ve hinted that students are really engaged in these assignments because of discussion forums. But can you elaborate on just the general sense of what students have been able to accomplish by shifting to this format, and also their engagement with one another?

Michelle: I think, several things, some of which I’ve mentioned. So the formative assessment part, I think they gain a lot from not just getting comments at the end of the semester, oftentimes handed in that last week. And then, frankly, I don’t expect that every student has even read my comments at that point. So just having the opportunity to fully engage throughout the entire semester, and so we’re not also dealing with, “Oh, I have three days before this paper is due. So let me think about it today, make a plan to think about it more tomorrow, [LAUGHTER] and then maybe I’ll do it the next day before it’s actually due.” And not that that’s all students, but I know when I was an undergrad that happened to me. So I think that just slowing them down, allowing them space to actually think about their country in multiple ways, and not have it be “Okay, we’re all working to doing your own country study at the end of the semester,” but having them actually engaged the whole time, I think is of great benefit to them. And I think they learn a lot more. I think that taking it in these smaller chunks, actually has made them research a little bit deeper on each of the topics because they’re not overwhelmed with “Okay, I’ve got all of these topics now to cover in this country study.” And so they tend to just do it in a way that is most efficient for them, let’s say. And so I think this gives them that way to back up a little bit and actually enjoy the process of learning rather than the process of an assignment only.

John: I think you’ve made a really good point there about students’ tendency to procrastinate. I was thinking back to when I was an undergraduate, and actually a graduate student as well. And I can’t recall a single time when I didn’t start writing the paper the day before it was due. I spent a lot of time gathering materials, taking notes, and organizing and putting an outline together, but the writing generally took place in just one day. So, [LAUGHTER] this is forcing students to engage in a little bit more spaced practice, to engage with the material regularly, and I think there’s a lot of benefits over that rather than the traditional way of just rushing through and getting it done right before it’s due.

Michelle: Yeah, exactly. And I’ve adopted that in other classes too, the idea of doing a lot of prewriting throughout the semester. So I’m a big believer in the prewriting process, and it’s very valuable, and it is doing work. But when I mentioned earlier, sort of they spend the day thinking about it, I’m thinking more of myself as an undergraduate thinking about not the assignment, but thinking about the fact I have to do an assignment.[LAUGHTER] So it’s more anxiety, not so much productive work on the content of what I’m doing. And so yeah, the prewriting work for any assignment is really great. So I’ve been trying to shift that mindset. I also teach a research methods course in my department and I teach our senior seminar. And so the senior seminar, especially, just making it about an entire semester-long process, I think, has taken a lot of pressure off of them. They seem a little relieved by the end, it’s not as daunting,

Rebecca: It definitely seems like it deepens the study and the engagement, not just because it’s broken into smaller pieces, but because it’s a consistent country the whole time and because it’s a consistent kind of assignment the whole time, it really provides a structure to be able to do that. I’m curious whether or not any of your other colleagues have also shifted to doing assignments in this format, or other similar kinds of strategies.

Michelle: I can’t really say. I know that in my department, we’ve talked about them a lot. And so I think people are adapting parts of them. I don’t think anybody does it the full way that I’ve described in terms of the full semester, but we have talked about just different ways of communicating. I think that’s going to shift again, obviously, AI is something that everybody is grappling with how to best use. So I think there’ll be some shifts coming. But I can tell you that I was Interim Director of the Center for Excellence in Teaching and Learning, and now I’m a faculty fellow there. And I’ve talked to a lot of people across campus, and some of our off-campus partnerships about this assignment, because I get really excited about it. If you haven’t noticed, this is my favorite thing. And so I’ve talked to a lot of people, and I just can’t be sure how many have actually applied some of these things. But they certainly seem interested, and they’re certainly searching out the same types of things I was searching out: how to make things, first of all, more authentic for the students, how to engage the students more with themselves in an asynchronous class or with other students. Even in a synchronous class or on-the-ground class, you could still have the same assignments. You might want to introduce them differently, you might want the discussions to take place in the classroom, rather than obviously online, because you could engage in person that way. So you can do kind of a round robin around the room, maybe… any of those techniques. But I also let the students know that, for them, they will become a mini expert by the end on that particular country. So they should all understand the topic areas but. becoming an expert on the individual countries that’s all their own. And so I hope that colleagues, no matter what field they’re in, can find some way to utilize that. Otherwise, they’re probably just a little sick of hearing me talk about it. [LAUGHTER]

John: Speaking of fellow faculty, has anyone else shifted to using infographics at your institution as a result of your work with us?

Michelle: Yeah, I can’t be quite sure. But I do think that in talking to a lot of faculty that they are shifting modes of communication for students, and I have talked to people who are much more expert at creating infographics themselves. You can do it very, basically, but you can also get really creative and high level with it. And so I oftentimes collect infographics I see for other fields, so I share those with faculty. I’m not sure. It’s one of those things that I so appreciate a podcast like this because sometimes teaching can be a lonely endeavor. We’re a little bit maybe shy about sharing what we do, because we’re afraid it’s not measuring up necessarily to what others are doing. So it’s a good question, though, and I think that that’s something that maybe I want to engage… maybe in a workshop in the future on campus and bringing faculty in and seeing who’s doing this type of thing, or maybe another brand new idea that’s even better.

Rebecca: I think one question that often comes up from faculty when we’re talking about kind of alternative formats or unessays or other [LAUGHTER] ways of not necessarily doing a more traditional essay kind of assignment, is how you evaluate the work. So I wonder if you can talk a little bit about how you’re evaluating these assignments, just to give faculty a sense of what that might look like and what that feels like in comparison to a more traditional kind of assignment?

Michelle: Yes, that’s a great question. I’ve talked in many ways about why I did these assignments in terms of just making it a more engaging process. But in terms of measurable, assessable goals, I do use rubrics, and have laid out exactly what skills I’m assessing. So I do have a section of the rubric that is based on the skills of communication. I have a very low stakes, low number line for things just like visual engagement, because I don’t want it to be students worried about the visual engagement when they need to learn the political science concepts. but I want to let them know that that’s important. So I have in the rubric descriptions in each of the columns about visually engaging: Does it bring the reader in? Is it easy to read? So I have that skill section, I have a section specifically on the content itself: Do they understand the concepts? Are they using the language that we use in political science? And then I also have sections for pretty typical traditional looking at their writing and seeing if it’s appropriate writing levels, tell them take a look at other people’s infographics… go out in the world and look at the bottom, there will be the sources, you need to include those, you need to not leave those off. So even practice that they would get in papers about citing things, if they put a graph in, you still have to say where this graph came from. So the practice that they would normally get are the same skills. And so I just lay it out in a rubric for them, as I do with most of my assignments. Of course, the rubric is not secret to the students, that goes out with the very first assignment: this is what you should be looking for. I’m going to use it for grading, you can simply use it as a checklist. You can use it to evaluate other students and give them feedback and tell them where you think there could be improvement. And so I think that the assessment itself is pretty typical to what I would normally do when thinking about an assignment: What are my goals for the course? How does this assignment help to meet those goals? And then how do I convey to the students whether or not they’re meeting those goals? Or essentially where they’re at in the process of becoming expert in that goal.

John: Do you use one rubric for all the assignments? Or are there separate ones for each of the infographic assignments?

Michelle: So I’m a big believer in constructing the outline once and then using it, but having different content in it. And so I use the same basic rubric for each of the assignments. And then I give the feedback in that content area really is the main portion. In the content area, I will make some changes to what they need to do specific for that assignment. So if it’s political parties, I would have expectations about what types of things they’d understand about political parties. And so that will change but my basic outline of what that looks like. And I’m a big believer in that kind of consistency in everything. So I can tell you I do that also with just how I set up the modules. And so my modules always have the same outline. There’s an intro with the learning outcomes for that specific module: what can you expect to learn this week, then the section with textbook readings. So this is what you’re going to dig into and read in a very traditional way. Then there’s a whole separate content section, which might be a video I’ve created, it might be videos that I found online that they should be engaging in. And then I usually have a pre-test and post-test quiz, and then the assignment for the week. And in this case, always associated with that assignment discussion. And then always at the end, I have a section, another discussion, which is I just call “Ask the professor,” And it’s another space for it. I always have to tell them every time I put the same heading and I say, “Don’t ask personal questions here, [LAUGHTER] but if you have a question that everyone can benefit from on the topic this week.” So that’s just an example of how I feel that it just makes it easier for me to focus on what’s in each of those boxes, rather than recreating the format. And I think students really take a lot of comfort in knowing after a couple of week: this is the rhythm, this is what I’m expected to do. And so now they’re focused on different content, but they don’t have to relearn a different set of skills. So going back to your question about the rubric, and is it the same? I do that because then they get used to what skills they’re bringing, and then they can hone that same skill over time, they can go back and look at their rubric grading and see their improvement over time. And I’m also a liberal user of the comment section on rubrics. I know that a lot of people tout rubrics for the time-saving part of it. So oh, you can check these boxes, and it makes it easier. And there’s some of that. But I can’t stop myself from also than opening up that box and just writing a ton of things to them. So I think that, again, it’s one of those things where the rubric itself becomes a really good outline. And then it’s just a matter of what I choose to put into my grading when I do the rubric.

Rebecca: High structure is always helpful for folks, that’s for sure.

Michelle: That’s very reassuring.

Rebecca: I feel like there’s a story behind why infographics as opposed to something else. What led you to infographics as opposed to some other alternative format?

Michelle: So like I said, I’ve been at this 27 years, so maybe I’m just getting old, but I feel myself saying in my head: “Well, kids nowadays don’t have the attention span for writing a 20-page paper.” And that’s not necessarily true. But as I talked about before, maybe they just have to be taken through that process. It can’t be just one big thing. And that was long before the internet, people talking about decreased attention spans. But I do think there’s something to matching up with the way in which they use visuals in their daily life. So they utilize memes, they look at infographics in various formats, I find a lot of them in social media in different areas. So I try to structure discussions in a way that is very much like the way they’re already engaging on social media. So yeah, the story is really how could I find a way that it’s something that their mind is already used to, that they can then apply it in this way. And also the other story, my husband is the County Administrator here in our county. And so I was in his office one day, I was the Assistant County Administrator and I was in his office one day and from the League of Minnesota cities, they had an infographic, a stack of them on the front table about: what does county government do? And that was really the last push I needed because it was so well done. And I thought, “Okay, this is what we need, people need to understand what levels of government do.” People often come to the county with situations that have nothing to do with what counties are actually able to do for them, and so what does your county do? What can it do for you? And I just thought, “How brilliant is that? To put it in that way.” And so I just really was sold on on the idea that infographics can really be fairly deep teaching, if they’re done well. We get so used to the academy and everything has to be these long papers, but rather than bemoaning the fact that the attention span has gone away, I’m just finding a way to adapt to that. So how do students engage with each other? And let’s make that happen.

John: And I would imagine students are more likely in the future to be creating infographics than they are to be writing 20-page papers unless they’re going on to graduate study.

Michelle: Yes, absolutely. That was the other part of it is that this wasn’t really authentic… the 20 page paper… particularly in that intro, liberal education, general education course. That was not authentic to what they were going to be doing. And I felt that in political science, if they were going to be political science majors, they would get that deep research component in other courses and upper-level courses in their capstone. And so I felt that it wasn’t necessary to put them through a 20-page paper. And the reason I keep saying 20 pages is because I realized that the amount of content you need in the country study is pretty lengthy to really understand the country, and so it really was about 20 pages, I just felt that that wasn’t engaging to people, no one was engaging each other with a 20-page paper. And so it had to be something that you could look at more quickly, you have to have the skill of condensing the material, you really had to be really pithy with how you were presenting the material so that someone can get it in a very quick way. And so I feel that allowed them to engage. And then even if you assigned a student to engage with another student’s 20-page paper, pair them off, have them do peer evaluation, they weren’t going to do more than one at that length. Maybe if you cut it down to a 10-page paper, you could have them have a group where they exchange it among three or four people, but that’s still not as big as I’m able to get by having everybody in a class post their infographics so that you can all see huge variety of work in the span of scrolling through normal social media. And of course, I’m asking to do a little bit more, but in some ways, it’s almost that they don’t even notice that part of it because they’re just scrolling through and saying: “oh…” Comments that I get most on the first assignment have to do with “Oh, I’m so glad you chose to do that country, I’ve always wanted to go there. Have you ever been? …because it looks so interesting.” So it doesn’t start out as this deep political discussion. But because of those comments in the first week, I’m convinced of the engagement that starts to come from it. One part that I didn’t mention is I also put in the directions, that if they want to add something, like a fun fact, about the country on any of these infographics, they should, and I’m surprised at how many students then choose to do that on every single one of the infographics at the bottom. There’s a fun fact. And I’ve been amazed by what they’ve come up with. There are things that I don’t know about these countries. I thought, “this is fun.” So I do think that students take to it, they want to show other students their country, they want to say, “Hey, I learned this thing. Isn’t this cool?” And then I get to learn things I didn’t know.

Rebecca: Sounds like a great way to build community and to have a first interaction with a discipline, which sometimes can be off putting. And this sounds like a really positive way to get people interested in a discipline as well.

Michelle: Yeah, I think so, and I hope so. I hope that that does make students more likely to maybe take an upper level, look at us as a minor or major. And I will say, talking so positively about this assignment, because it’s worked so well. But I can also talk to you in a podcast about all the assignments that haven’t worked well, that I don’t get this excited about, that I can’t say were this level of success. And so when we were talking about coming on the podcast, I wanted to highlight this one, because of course, it’s the success. I wanted to show what has actually worked. But, of course, there’s a lot of ones that haven’t. So I do feel that I’ve hit on something here that can be very useful, that I’ve been really satisfied with, and my students seem to be really satisfied with.

John: We always end with a question. What’s next?

Michelle: So that is a great question. I’ve been thinking about that for a while. How do I take this to the next level? So I think one thing that’s next is sharing this out more. We’ve talked a few times about looking at other faculty and seeing if they would adopt something similar to this. So I think that’s one aspect. I think another aspect is to just continue to refine the assignment in a way that gets the most student engagement. I’d also like to see these assignments, if we can have them maybe printed for my asynchronous online classes. This particular class I teach mainly online now, I rarely teach it in the classroom another colleague has those sections. And so, right now, it’s simply archived either as the students own individual file, or in D2 L. And so I’d like to think about ways in which maybe we can archive them, maybe print out them in poster form and have them in the department so that people can maybe again, engage, get interested, a student walking by who’s not in political science who’s hanging out in the hallway waiting for their next class to begin, and starts reading the stuff on the walls and start saying, “Oh, that’s kind of interesting,” and maybe starts to get engaged. So looking at it outside of the particular classroom, I think would be a really exciting way to go with it.

John: Well, thank you. I’ve really enjoyed hearing more about these assignments. And I think it’s something that could work in many disciplines.

Michelle: Yeah, I hope so. And exciting for me would be now to sit back and kind of watch what other people do with it. And oh, okay. I hadn’t thought of that. That’s really cool. That’s always the most exciting part for me from the CETL angle is really just seeing how people take off with something in a way that I wouldn’t expect. And it’s been a pleasure talking with both of you.

Rebecca: I always love a good conversation about interesting assignments. So thanks for chatting with us and sharing in depth how the assignments work over the course of the semester.

Michelle: You’re welcome. My pleasure.

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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.

Ganesh: Editing assistance by Ganesh.

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