In the first part of this interview, we learned about how Christine has been exploring AI use herself along with figuring out its place within her classrooms. In this part, we dig into her experience in creating a community of practice to help other faculty learn together and share.
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 in higher education classrooms that you would like to share, consider being interviewed for this Substack.
Christine: I think this is a good moment to transition into communities of practice, because so much of the playfulness and curiosity now comes from my work in our center for teaching and learning at Fairfield, the Center for Academic Excellence.
My colleagues there think about AI every day, and we try to be playful and talk about what we’re doing. We can see how an idea from my biology class might influence a film studies class or a completely different field. That cross-pollination happens because we can quickly say, “That’s possible now. Look what’s possible.”
That sense of playfulness and curiosity, moving toward “How can I open things up?” rather than “How do I lock things down?”, is where I see the next phase of working with colleagues. It happens when we’re open enough to say, “I need to take a fresh look at what I’m doing.” We can wrestle with a moment in a course when we admit, “I’m not sure this unit is connecting with students in the way I hoped.” Then we can ask, “How might I engage them? What’s a new way to play? What could I never do until this moment?”
A bunch of us are starting to think about those playful ideas, and simply talking about them together gives us the confidence to try. You know your discipline better than I do, but I might offer another way to see a fun little thing to try and ask, “Could that work for your knowledge, your discipline, and what you want to achieve with your students?”
Lance: Yes! Seeing your session at NERCOMP 2026, it’s one of the (many) reasons I want to interview you. Can you tell me about the community of practice you started within nursing fairly early on? Where did it come from, and what happened?
Christine: When I found that my undergraduate nursing students were really resonating with this conversation tool, I went to my colleagues in the nursing program and said, “I think you all need to know about this.” I went to one faculty member I knew was deeply engaged in undergraduate nursing education and said, “I think you might find this powerful.” She instantly said, “We need to try it. Let me gather a couple of people.”
I started a small community of practice with our nursing faculty, which quickly expanded across the Egan School of Nursing and Health Sciences. After one meeting, it grew from about 6 to 12 to 25 faculty in the early part of the fall, and the community persisted through the whole semester.
Lance: It was a virus.
Christine: Yes, it was infectious.
Lance: So your work went “viral”?
Christine: That’s what you get with microbiologists! And colleagues in centers for teaching and learning at other schools would ask, “What were you doing to keep them there?” It wasn’t food or stipends. It was the shared feeling of, “I need to explore this. I want to know more. Thank you for creating a space where we can talk about it.”
That’s the most important part: a community of practice isn’t about everybody adopting the tool. It was about facilitating conversations: “Let’s just talk about this. I want you to see what I’m doing; now, what are you reacting to?”
Some faculty would say, “My goodness, I have to do this right away,” while others came with questions or hesitation, and that’s valuable. We need all those voices. People felt comfortable enough to say, “I don’t know; I’m reluctant to use AI.” I could honor that and say, “That’s okay. We’re here together because we all care about the same students.” That mattered.
What came out of it was a shared space, an organization on our learning management system, where everyone could click on our resources, explore, and use a fill-in-the-blank tool to prompt AI themselves. I gave them resources that made it a low-lift opportunity to try and build their own activity.
Every month when we met, everyone committed to trying something, such as clicking on someone else’s activity or developing one of their own. They didn’t have to deploy it in the classroom yet; it was about trying and becoming comfortable together. By the end of the semester, the conversation had moved toward which ways to use the tool, when to use it, and whether and how to grade it. The nuance of where became the conversation, instead of “I like AI / I don’t like AI / students cheat.” That became incredibly exciting.
By spring, we had a compendium of activities everyone had built because it had become a sharing space. “I see you doing something, and down the hall I teach those same students, so I can immediately transfer that into my course and modify it a little.”
Everyone felt like we were supporting and building together, rather than each person having to build the whole thing independently. People felt supported and part of something we were creating together.
Lance: There are a lot of parallels to how it happened in your classroom: that whole idea of “we’re just going to explore it,” and giving people the agency and choice of what to explore and what they’re comfortable with, with a space that makes room for that.
Christine: In our Center for Academic Excellence, we run workshops across disciplines every semester for the broader campus conversations about what everyone is doing with AI.
This community of practice was a side project, really exploring AI in a disciplinary context. Doing both in parallel, I can truly say our disciplinary approach ramped up so quickly that the group of faculty who started a year ago saying, “I don’t know how to use AI,” now includes people in our Egan School who have created research partnerships with our engineering school.
They’re looking at what it means to build LLMs for healthcare practice and to get students practicing simulated conversations in their simulation lab. They’re asking what it looks like to use AI in graduate programs, to understand how students experience writing a doctoral thesis in an online program, and how to scaffold that writing. It’s moved beyond one-off classroom activities. From the undergraduate to the graduate level, people across that healthcare space are working in a “we’re in this together” spirit around this population of students.
The disciplinary context gave everyone strength together, and suddenly people were taking the work to another level. When I closed the community of practice this spring, I did a survey of where people’s interests were moving next, and lowest on the list was doing more with AI Conversations because they already felt confident with them.
The next things they wanted were doing research, engaging in the scholarship of teaching and learning, and writing papers about what we’re doing. It was expanding rather than, “I think we’re done here.” It was, “What’s next?” There was such joy and enthusiasm in seeing what can happen when the right group of people want to learn from one another.
Lance: How does your experience with this work at Fairfield contrast with what you read in the Substacks, newsletters, and podcasts about the conversations happening elsewhere?
Christine: First, I’m a devoted reader of your Substack. Truly, because the first time I met you, you were bringing real nuance to the conversation and opening a space for everybody to share: “It’s okay where you are. This is something we all have to figure out together.”
I love that framework: we can all share something we’re learning and wrestle with things together. In some ways, this is a moment like the pandemic. Not everyone had taught online, and yet we needed to. Those of us who had taught online could say, “I feel comfortable; let me share what I know.” We had to level up together. After the pandemic, some people returned to in-person teaching, some continued teaching online because that was their program’s format, and some said, “I never want to teach online again.”
With AI, I think we all need to remain aware of how it continues to grow and impact teaching and learning, both through its opportunities and its challenges. I take seriously the questions about how this affects the way we assess students and what we’re actually assessing.
But from a center-for-teaching-and-learning mindset, it’s valuable to take a thoughtful look once in a while at what we do in the classroom and ask, “Are we achieving the things we intend to achieve?” Sometimes we have to humbly say, “I’m not sure,” and then we study it, try something new, or learn from one another. That’s where evidence-based practice comes from.
I also have the benefit of being connected to another community across many campuses. I’m part of the SCIENCE Collaborative. Fairfield is one of 14 institutions looking together at how to support student success in STEM courses. That gives me insight into what’s happening on other campuses in different contexts: minority-serving institutions, state institutions, R1s, and private institutions.
We’re building a working group right now around what AI is doing in the introductory STEM learning space. I’m excited to help facilitate that because this is a group that already thinks deeply about student success, alternative grading, and universal design, taking a student-forward approach to how we support learning and exploring how AI fits into that work. We’re starting that next phase this summer.
Lance: What’s next for you around figuring out AI?
Christine: As I said, I’m an experimenter by training. With AI especially, we’ll always be exploring a moving frontier. Just when we feel like we’ve wrapped our heads around it, the next version comes out and gives us something new to consider.
I see my role in our center as being there for the faculty community in a way that helps everyone come together and share what we’re learning. And I want to intentionally engage faculty who are thinking at the edges of what’s happening.
I mentioned I’m working with one of our core writing faculty on an intentional study. What happens when a biologist and an English professor research AI together? It’s exciting. It’s wonderful to think about what it means for a first-year student to take a writing class and then, the next year, take a biology class where they write with AI.
Students take many different courses across the core requirements because we’re building a bigger picture of what they should synthesize. But we don’t always know whether that synthesis is happening. We intentionally design these learning sequences, so studying and learning from them could translate across many contexts. What does it mean to write in your discipline with AI? We might help you figure out a way to assess that writing.
I see my work as bringing together people who are wrestling with all of this and being comfortable with not having it figured out yet. Experimenters don’t always have the answers. You have to be able to say, “That was an experiment. I’m not sure I learned what I expected, but I’m willing to look again, revise my hypothesis, and try. I’m willing to bring in experts from another discipline and look with a fresh perspective.” That’s the way I’m trying to approach it: bringing people together, staying open to what we learn, and knowing that if my north star is doing right by our students and supporting faculty as we figure this out together, we’re working in the right direction.
Lance: Any other insights you want to share?
Christine: I have two children who went through college during this early AI moment, and I love talking with them about where they find AI helpful. They’re at different universities than mine, so I get another window into the student experience. It’s reassuring to know that institutions everywhere are actively figuring this out.
I’m very grateful that at Fairfield we keep trying to meet the moment. Faculty are coming to this work from many different starting points. In a center for teaching and learning, I’m not in the business of telling people what they have to do. I’m here to support faculty as they navigate this moment and to meet them when they’re ready to engage. If you come to a workshop, I’m ready to explore with you. If you’re not there yet, that’s okay. We’ll be here when you are.
I think the next few years will bring many opportunities to support faculty who are thinking, “I haven’t tried this yet, and now there is so much to explore.” That’s okay. We can start where they are and work through it together. With agentic AI, I’m especially interested in how we continue to help students value and stay engaged in the learning process.
In my classroom, I can create the conditions for students to engage deeply, practice, and see the purpose of the learning. If I tell them, “This is my time with you, and I really want to use it intentionally,” I’m inviting them to bring that same intention. I believe most of them do. If we start there, seeing students as partners in an opportunity to learn together, it’s a much more hopeful and productive experience.
That’s the perspective I keep trying to hold: looking for the possibilities, staying in dialogue with students, and continuing to build learning experiences that invite them in.
Lance: I agree. That’s why I step into it, trying to find where the possibility is. The other thing I’ll see at the broader level is that the big trend I feel for this next year is curriculum change. So much of this has still been at the classroom level, but I’m starting to see intentional, planned efforts to update curriculum accordingly. The ones starting to figure it out are ahead and in higher ed, being a year ahead can mean enacting changes five years in.
Christine: I agree. And across majors, departments, and fields, I think accreditation and evolving professional standards will increasingly shape these conversations. As those standards evolve, people will have opportunities to wrestle with what it means to teach in the discipline now.
That’s part of why I love to nurture anyone willing to sit, wrestle, and explore in any discipline, because they’ll be a huge asset in shaping new learning outcomes. We’ve spent a lot of time here on healthcare, biology, and STEM, but I’m part of conversations across other disciplines, too. So much of this has to come from disciplinary experts who are willing to try: the people who understand the nuance of where learning and productive struggle happen in their fields better than I do. I especially love leaning into our newer faculty, who may be coming from graduate school having used AI and bringing that experience to their departments. Anytime you can bring someone into the conversation who says, “I’m learning how to do this,” it can have ripple effects across a department.
The Update Space
Upcoming Sightings & Shenanigans
CUMU Webinar: The AI Tightrope: Recognizing and Resisting the Roles of AI in Community Engagement. Wed, Sept 23, 1pm (ET).
EDUCAUSE Online Program: Teaching with AI. Virtual. Facilitating sessions: ongoing
Recent Recordings, Resources, & Writings:
Davis, L., & Eaton, L. (May 2026). Expanding OER with GenAI. EDUCAUSE Review.
AI x Higher Ed Podcast with Anand Rao & Stefan Bauschard. Episode: Universities Must Adapt to AI—Here’s How They’re Doing It (May, 2026)
Damm, C., & Eaton, L. (2026, March). From prompt to practice: A framework for transparent GenAI use in higher education. EDUCAUSE Review.
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
AI Syllabi Policy Repository: 200+ policies (always looking for more- submit your AI syllabus policy here)
Finally, if you are doing interesting things with AI in your higher ed classrooms, consider being interviewed for this Substack or even contributing. Complete this form, and I’ll get back to you soon!
We periodically host small-group workshops and leadership sessions for higher ed teams. You can learn more about our current offerings here.
AI+Edu=Simplified by Lance Eaton is licensed under Attribution-ShareAlike 4.0 International
The shift from “do I like AI?” to “where does it actually belong?” feels like the most important part of this.
What made the community work wasn’t consensus. It was creating enough psychological safety for people to experiment, hesitate, compare results, and revise together. That turns AI adoption from a tool rollout into a learning system for the faculty themselves.
I also like that the disciplinary context mattered. A Biology teacher, nursing faculty member, or writing professor is not just choosing whether to use AI—they’re deciding where productive struggle, judgment, and evidence of learning need to remain visible in their field.
That feels much closer to the real work than any universal “AI in education” playbook.
Thank you @Lance Eaton for building a community to care for this work.