“...faculty are hungry to talk to each other…”
Part 1 of an interview with Autumm Caines, Tolu Noah, & Carla Vecchiola
About the Interview series
As part of my work in this space, I seek to highlight the folks I’ve been in conversation with or learning from over the last few years as we navigate teaching and learning in the age of AI.
If you have experiences around AI and education, particularly in the higher education space, that you would like to share, consider being interviewed for this Substack. Whether it is a voice of concern, curiosity, or confusion, we need you all to be part of the conversation!
Interview Introduction
The following is a conversation among Autumm Caines, Tolu Noah, Carla Vecchiola, and me (Check out their bios at the end). It stems from a series of No-Prep workshops that Carla and Autumm have been running at the University of Michigan around AI, and was inspired by Tolu Noah’s recent book, Designing and Facilitating Workshops with Intentionality: A Guide to Crafting Engaging Professional Learning Experiences in Higher Education.
Eaton: Autumm & Carla, you kindly invited me to the No-Prep AI Session at the University of Michigan-Dearborn in October. It was a great experience to be with you and your faculty. What were you hoping would happen, particularly around using the No-Prep format, and with this focus on AI?
Vecchiola: I first want to credit Tolu. I read about her No-Prep Book Club in the Chronicle of Higher Ed teaching newsletter, and I immediately knew it was a good fit for us at Dearborn. We have been running a cohort-based program on GenAI. It requires an application. It carries a stipend. We’ve had to turn faculty away from it.
And so we knew we wanted something for everybody else so that we could put something out for everybody to join. When I read about it, her title was perfect: the No-Prep Book Club. We love the structure: a quick welcome, faculty spend 20 minutes tinkering with GenAI based upon a book or a shared resource like some kind of prompt library, and then they come back for conversation, and the richness happens. That’s where the real learning for faculty happens.
We first did it on ChatGPT Assignments to Use in Your Classroom Today. After that, we wanted something that didn’t focus necessarily on student assignments, but that focused on prompts that faculty can use in their own teaching practice or not use if they don’t want to use GenAI. Autumm had been including your resource, Lance, in our cohort, the teaching prompts, and I knew I wanted to use it for this. Autumm mentioned she knew you and could invite you.
Eaton: How did they go? Why do you feel that this would be a good approach for working with faculty, particularly around GenAI, but just in general?
Caines: I think they’ve gone wonderfully, all of them. I haven’t attended all of them, but I think they’ve been really great. There’s a bit of a split between folks who can dedicate more time to it and want to dig in to do something deeper, and those who maybe are more attracted to a No-Prep session because you don’t have to prep. You can just show up. You can come as you are. That’s another way that we frame these sessions a lot of times is just “come as you are”, right?
With any kind of technology adoption, you’ve always got those who jump in immediately and are doing so much in there, and then you have others who are maybe more skeptical. I think that this session format can lend itself really nicely to either.
They come as they are, and then we talk a little bit about some things that they can do – in this case, either with GenAI or without using GenAI. Then, we just give them downtime to work on that stuff. We turn off our cameras (These have all been virtual for us). Then we come back together, and we talk about what happened during that work time.
I do a No-Prep session that’s actually based on just talking to your students about generative AI. Instead of having one resource that people go through, I give them five different things that they can choose from.
I let them know there’s probably similar research out there in their discipline, and they might use the time to go out and find and explore those things. I think it really does create a welcoming space just for everybody, no matter what they feel about GenAI.
Eaton: A quick side-question. You mentioned having a cohort program and the No-Prep program. Did you find anybody who was in the cohort program that was also showing up?
Vecchiola: I think we did have people who had been through the cohort and then dropped into a No-Prep. My suspicion is that faculty are hungry to talk to each other about GenAI. I think you could have gone through the whole seven-week program and still want more spaces for that kind of spontaneous conversation that’s going to come from everybody having that quiet work time and then coming to have an experience to talk about. We’re not just venting. We’re not just like offgassing our frustration or anger or whatever it might be, but we have a tool or technique that we’re working on in those 20 minutes. Those conversations are really rich, I enjoy them, and I would keep going to them even if they’re on the same resource.
Eaton: Tolu, having heard a little bit about what inspired the session, I’m curious how close this is to what you were originally hoping for, imagining where people would run with this idea (or others for that matter) when writing your book? How does it feel for your ideas to take on this form?
Noah: It’s honestly really awesome because when I initially did it, I wasn’t planning for it to be something that was so widespread. It was more like I did it, and it worked, so I decided to share it with others. I’m really excited to hear that the format resonated with you both, especially that No-Prep component.
We all have so much on our plates, and I wanted to be cognizant of that in the design of the series. I tried to design it to be No-Prep in the sense that instructors didn’t have to carve out time to do any reading or prep work beforehand. Instead, everything took place during our meetings. I also wanted it to be no pressure in the sense that they could come to as many or as few of the sessions as they wanted based on time, bandwidth, and other obligations.
I’m glad to hear that designing it in a way that honored people’s time and bandwidth resonated with you both. I love what you just said about how faculty are hungry to talk to each other. That was another one of my hopes for the No-Prep Book Club– to create space for open and honest dialogue about AI. When I first launched the series, it felt like we were being bombarded with all of these how-to workshops about different AI tools, but there really wasn’t space to just talk about what we were thinking, learning, trying, and grappling with. A big part of my hope for this No-Prep series was that it could be a space where those conversations could take place.
Along with that, I wanted this to be a supportive space because even as an educational developer, I was feeling a bit overwhelmed with how much information was coming at us and how quickly things were changing. I had a feeling I wasn’t alone. I wanted this to be a space where it was okay to come as you are with all of your questions, concerns, and things you’re working through, and know that you weren’t alone in trying to navigate everything.
Eaton: Can you share a little bit about when you first developed this model? What was the problem in professional learning you were trying to either interrupt, work around, or work through?
Noah: One area I was trying to interrupt is the over-reliance on the workshop model of professional learning. Don’t get me wrong, I love workshops, and I think they play a really valuable role in our professional learning programming. But I think it’s just as important to consider what other models of professional learning can help us to reach our goals.
I intentionally wanted this to be more of a learning and conversation-based approach instead of framing it as “I’m an expert who’s going to teach you how to do X, Y, and Z with AI.” More like I am a learner right alongside you who’s trying to work through this, and we’re all going to explore together to see what we can figure out along the way. I think that that framing really helped to create a space where we could draw on the collective wisdom and experience in the room.
Carla and Autumm talked about the focus on creating space for people of all experience levels to get together. That was also something that I was intentional about in the series. I told instructors that whether they felt super confident with AI or didn’t know much about it, they were welcome here. We were all going to be able to learn from each other.
Another area that I was trying to interrupt was application because sometimes in professional learning spaces, instructors come, listen to us talk, and then they leave. There’s not much, if any, time for direct practice and application. One of my goals was to provide a good chunk of time for instructors to explore and tinker during the actual sessions, instead of them having to find time to do that after the fact.
Eaton: I’m thinking about particularly the last 5 years and the Zoom-ification that has happened with much of our professional development. And as a result, many of them just become webinars essentially. They’re not necessarily interactive, and there is little room for socializing. Was that in the background? Do you recall any moments when you were thinking about that?
Noah: I feel like that was at the foreground, to be honest. It just felt like every single thing I was seeing was a workshop where the presenters planned to talk for 50 minutes about 25 different AI tools and how they could be used. And there’s space for just being exposed to different tools and approaches, but when everything is that, it just feels even more overwhelming.
I believe that professional learning should actually equip people to do something differently. If we want people to be able to actually apply these skills, we need to give them time to do that. If we want people to think critically about these topics, we need to build that into the design of the session.
Workshops about different tools and techniques have value, but it’s also important to incorporate social learning opportunities where instructors can learn from each other, and where they have time to tinker. That is something that faculty don’t often have: time to just explore. In fact, one of the pieces of feedback I got from an instructor was that they appreciated having some time to explore and then discuss their observations with others. We need more time for exploration and connection in our professional learning spaces.
Eaton: Through your journey of building different kinds of professional development, doing the book, and working with faculty, what are some new things you’ve learned?
Noah: One key thing I learned was the importance of balancing structure with flexibility. That was helpful both from a facilitation standpoint and for establishing a regular rhythm of what would take place from week to week. For the No-Prep book club, I wanted a structure that I could repeat across sessions, and that would provide space and time for instructors to engage in active learning and connect with each other. That’s what led to my Four T framework.
The four Ts stood for touch base, tinker time, talk, and transition. We first touched base as a group through a warm-up activity. Then, they had tinker time, which was about 20 minutes for them to independently read about selected activities from the book and then try them out in an AI tool of their choice. Then we had about 30 minutes to discuss our findings and remaining questions. The ending was transition time, where we engaged in reflection on the discussion. I also provided some reminders and a resource of the week.
One little thing I’ll add on to the resource piece was that I was really intentional about just providing one resource each week because I felt like there was a fire hose of information coming at us, and I didn’t want to overwhelm anyone. So each week, I offered one thing related to the topic of the chapters that instructors could use to go further with their learning. Instructors really appreciated having just one resource to go deeper with.
The Four T structure was really helpful for the gatherings, but it was also loose enough to allow instructors to do what worked best for them. They could choose which activities to explore and how many of them to try out. I had a Google Doc that they could use for note-taking and reflection, or they could capture their thoughts in another way. They could contribute to the discussion verbally or share their thoughts in the chat. There was a lot of flexibility within that structure, and instructors really appreciated the mix of time for independent exploration, time for group conversation, and time to reflect on what they were going to do with their learning moving forward.
Eaton: There are lots of different reservations and concerns about the role of AI in teaching and learning spaces. I would imagine across the four of us, we’d have distinct concerns, and also there’d be this good Venn diagram. But let’s start with what’s the big question or concern you’re grappling with when it comes to generative AI in higher education, specifically, or in the teaching and learning space?
Caines: I’m going to give a couple of different ones. I’ve been spending a lot of time thinking about our identity as educational developers but also as faculty. How we see ourselves as instructors and how this is may be challenging some of that identity piece, right?
There’s a great article in the Chronicle titled Stop Pretending You Know How to Teach AI that just really kind of stopped me in my tracks. Because it’s so new, we don’t really know. I do think it’s having a deep impact on our identity and how we see ourselves. I also think a lot about relationships between faculty and students. We’re hearing more and more about how AI is becoming people’s friends and lovers and all of these kinds of things.
I spend a lot of time in the relational learning space. I really believe the Barbara Harrell Carson quote, “Students learn what they care about, from people they care about and who, they know, care about them.” That’s just always foundational for me. So when we have something that isn’t human but makes you feel like it cares about you, and can also give you information, I worry about that relational piece of it.
Then I also think a lot about just the workload of it. There’s all this talk about how AI is supposed to be so great for efficiency, but I have a lot of faculty coming to me and saying this is causing more work. They need to go through and double-check every single citation now, because half of the citations are often just made up and fabricated. They didn’t use to have to do that before. I’ve never had students who were imaginative enough to do that on their own, but AI does that for them within 30 seconds. Those are maybe my big three.
Eaton: All right, we’ll come back if there’s any more that don’t come up in the next two.
Vecchiola: We all work in higher ed, and we’re seeing that GenAI is a major disruption to higher ed. It’s all we’re talking about, it’s all everybody is reading about in the Chronicle. It feels like we still aren’t paying enough attention to the big question of: Does this change what higher ed is? Does this change how people see higher ed and the value of an education? The thing that I’m most worried about is kind of the trust gap between faculty and students, as Autumm mentioned, and also between faculty and administrators.
The AAC&U national survey report showed faculty perceptions of student over-reliance on GenAI are incredibly negative. They’re worried about cheating. They’re worried about academic integrity. But when I was reading the Chronicle response on this report, they mentioned the same survey with administrators, and there’s a big gap between how positive administrators were about the impact of GenAI on higher ed versus this one, and faculty being so much more negative. I think that’s a challenge, that there are gaps between administrators and faculty, and the gap between faculty and students.
We explored this on our campus with a student panel, and we are ripe for having another one. This one was about a year and a half ago. What was really interesting was just hearing all the students. Some said they don’t use it if they have environmental concerns or if they were worried it would rob them of their own learning. But we also heard from students who said that they used it because their schooling system didn’t prepare them for feeling comfortable with academic language, and they were trying to create a more equitable situation for themselves. Students whose first language wasn’t English were trying to again create a more equitable situation for themselves. The students who used it had so many more reasons for using it than just “I’m cheating,” and faculty just assume students are using it to shortcut and to cheat. If we can have more conversations between those groups of people, we will do a better job of really deciding what this truly means for education because it’s big.
Eaton: That reminds me of the conversations Autumm and I were having in December of 2022 when we first stumbled upon a student that we thought might have used it. When we started to ask students about their usage, we got some of those same answers. Having heard your point, Carla, three years later, it’s like we’re still stuck in that loop, which is concerning.
Noah: One of the big concerns I have is offloading. How do we help students resist the temptation to offload too many tasks to these tools? I’m increasingly concerned about this with the rise of agentic AI, which could literally take the course for you. How do you help students see the value of doing the work themselves and engaging in that productive struggle rather than completely bypassing it?
I also want to plus one what Autumm said about the relationship component. I see this as a challenge in two areas. One is making AI the first point of contact (for example, “I had a question about this, so I asked AI…”). Please talk to other people, too. We don’t want AI to be the only place we’re going when we have questions.
Then, there’s also the issue of the way certain AI companies are marketing their tools as companions. The one that I’m most frustrated by right now is Friend. I live in Los Angeles, and there are billboards all over the place for this wearable AI device. They position it as someone who listens to you and supports you or something like that. And I’m just like no, no, no, no, no. This is not your friend. I’m concerned that it’s being marketed that way.
I also want to plus one what Carla said about how this is creating more work for faculty. They’ve had to do so much pivoting in such a short period of time. What does that do in terms of burnout? How can we make this feel more sustainable with all of these frequent changes and challenges that are coming up?
I think Carla mentioned something about how it’s taking up a lot of space in educational conversations. That’s another personal pet peeve. I wish we had these deep conversations about other things, like the science of learning and active learning, but AI has taken up all the space in the room. I would love to see more space for discussion about other aspects of teaching and learning that have always been important.
Eaton: Right! Not to have it suck up more air, but I want to follow up with this question. Do you find any distinctions of where you’re most grappling with AI’s impact and implications in your own work?
Vecchiola: The place where I notice it the most is in my teaching. I teach one course a semester, which I get to understand the faculty perspective. I teach online asynchronously. The way that I interact with my students is written text and I just saw so much Gen AI. Even in courses that had metacognition and reflection, it was clear that students were using Gen AI. They used it even to choose a song for themselves, which was an introductory question.
This is maybe getting into the next question, too, but I have to say that I was definitely losing my joy of teaching. I have rediscovered it this semester.
Noah: I think one of the tensions I’m personally experiencing in my role as an educational developer is how do I support instructors with GenAI while at the same time having my own personal reservations about it?
I think that’s another reason why I’ve leaned more towards discussion-based approaches as opposed to how-to workshops, because with workshops, it can kind of feel like I’m having to actively promote the use of particular tools, but with discussions, it’s more about creating space for dialogue and getting into the nuance of it all. That’s what I’m personally grappling with.
Join us for the second part of the conversation in the next post!
Autumm Caines is a liminal space. Part artist/poet, part technologist/administrator, and always aspiring educator, Autumm has worked in higher education for over fifteen years, bouncing between the teaching center, the information technology department, and adjunct teaching positions at a few different institutions. Currently, Autumm works full-time as the Lead Instructional Designer at the University of Michigan – Dearborn.
Tolulope (Tolu) Noah, Ed.D. is a lifelong educator with a deep passion for teaching, technology, and facilitation. She currently works as an educational developer at California State University, Long Beach, where she designs and facilitates professional learning programs for instructors about teaching and technology. Previously, Tolu was a senior professional learning specialist at Apple, an associate professor in the teacher education program at Azusa Pacific University, and a K-12 teacher. She is also the author of the book, Designing and Facilitating Workshops with Intentionality: A Guide to Crafting Engaging Professional Learning Experiences in Higher Education.
Carla Vecchiola is Director of the Hub for Teaching and Learning Resources and a History instructor at the University of Michigan–Dearborn. She works to cultivate a shared campus teaching culture grounded in student-centered practice. She brings a deep interest in the cultural politics of cities to both her teaching and her public work as co-curator of Exhibit 3000, the world’s first electronic dance music museum.
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Eaton, L., Nemeroff, A., & Sun, X. (2026). AI-assisted course design and development. In K. S. Ives, M. Cini, & R. Schroeder (Eds.), AI applications in online higher education administration: Strategies for maximizing returns and improving outcomes. Routledge.
Margin of Thought with Priten: Season 1, Episode 5: How Can We Center Pedagogy During the AI Tech Wave? (February 2026)
Online Learning in the Second Half with John Nash and Jason Johnston: EP 39 - The Higher Ed AI Solution: Good Pedagogy (January 2026)
The Peer Review Podcast with Sarah Bunin Benor and Mira Sucharov: Authentic Assessment: Co-Creating AI Policies with Students (December 2025)
David Bachman interviewed me on his Substack, Entropy Bonus (November 2025)
The AI Diatribe Podcast with Jason Low (November): Episode 17: Can Universities Keep Pace With AI?
The Opposite of Cheating Podcast with Dr. Tricia Bertram Gallant (October 2025): Season 2, Episode 31.
The Learning Stack Podcast with Thomas Thompson (August 2025). “(i)nnovations, AI, Pirates, and Access”.
Intentional Teaching Podcast with Derek Bruff (August 2025). Episode 73: Study Hall with Lance Eaton, Michelle D. Miller, and David Nelson.
Dissertation: Elbow Patches To Eye Patches: A Phenomenographic Study Of Scholarly Practices, Research Literature Access, And Academic Piracy
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