“What does it mean to catch students learning with AI?”
Part 1 of an interview with Christine Rodriguez
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 R. Rodriguez, Ph.D. is Managing Director of the Center for Academic Excellence and Asst Professor of the Practice in Biology at Fairfield University, where she leads faculty development initiatives in teaching, learning, and AI integration. Her work focuses on designing authentic, discipline-specific uses of AI to support student learning, including clinical communication simulations in undergraduate nursing education. She is a leader in the SCIENCE Collaborative, advancing cross-institutional communities of practice to improve STEM student success and belonging, and she facilitates a discipline-specific faculty community of practice in AI conversations at Fairfield University. Her scholarship and faculty development work focus on bridging scientific knowledge with human-centered communication and real-world application.
Lance Eaton: Tell us a little bit about yourself and how AI came into your teaching?
Christine Rodriguez: My background is in molecular biology. That’s where I began in research, so I think of myself as trained to be a tinkerer and an experimenter in biology. As AI filtered into my teaching of a microbiology course for clinical nursing students, I instantly became curious: What are my students using it for? What could I try? My experimental training carried over into the scholarship of teaching and learning.
My questions were: What is this thing? What’s prompt engineering? What does it look like to do this with students? And I kept leaning into whether it is meaningful for an activity in my class? Is it meaningful for how I’m assessing learning?
Basically, I put that experimental mindset to work figuring out where I could find meaning in AI for my own teaching. Once I did that, I could begin to see how I might help other people explore it, too. So I approached it with a lot of curiosity: trying things, changing them, and learning alongside my students and colleagues.
As I showed up to conferences, I’d think, “I’m no AI expert,” and everyone at the conferences kept saying the same thing: “I’m no AI expert.” I thought, we’re in this together. We’re all here to experiment, learn from one another incrementally, and figure this out together.
Lance: What kinds of tools, or what are the things you’re finding yourself using it for?
Christine: I started to realize that the more intentionally I worked with AI, the more it became consistent with my voice, my priorities, and my teaching. As that context carried across my projects, I started to see the benefits of everything I was putting into the process.
So I was doing deep work with ChatGPT for myself and with my students. At the same time, my colleagues in our center for teaching and learning were all trying different things, so we were learning from one another.
On day one in my clinical microbiology class, students come in, and I love to have them dive off the deep end with me. I tell them I don’t expect them to know any microbiology yet; it’s the first day. But let’s agree we’re all here because we want to be in a clinical role one day, working with patients. So I take them on a little historical journey through the bubonic plague.
I take one-third of the room and say, “Imagine you’re a nurse or caretaker for a patient with the bubonic plague in the year 1400.” The middle of the room: “You’re a nurse around 1880.” And this group over here: “You have a patient with the bubonic plague right now.” I take them through a series of questions and give them a series of resources: the Centers for Disease Control, the World Health Organization, the many resources we use all semester. But I also give them AI as an option, with a modeled prompt. I give them about five minutes to explore their era, and then we report back.
The great thing is that they quickly see, “Whoa, I learned what it would be like to be a caregiver in that time.” They can see that across eras, science changes. When we know more, we can make different decisions and do better. What we now know to do with the plague is very different.
They learn that in about a 15-minute period on the first day of class. That’s very different from me lecturing about the history of science. It engages them, and it shows them I’m here to play and explore with them while modeling this AI work. So that’s a good little peek into a 15-minute moment in class that shifts how I use my time toward really engaging students.
Lance: That earlier point is important and an ongoing challenge: the more you dig into a single tool, the more beholden you are to it. We see this on social media platforms, and with generative AI it becomes doubly so, because, as you said, it starts to extends your thinking. It supports your thinking enough that you get richer results.
It’s an interesting consideration especially as institutions start to acquire tools: are students and faculty going to switch? If your institution adopts AI tool X and they’ve been using AI tool Y for three and a half years, the switchover cost is real.
Christine: In the first semester or two, I always started with the question: Do all my students have access to this tool? So I leaned into the free version of ChatGPT because I wanted whatever I did to be available to everyone.
We’re now moving into a phase at Fairfield University where we’ll have access to a new suite of AI tools, so there will be even more opportunity for intentional engagement, with all students having access to the same tools. That changes the dynamic of what I might ask students to engage with because I know they have access.
That said, in the past semester or two, I’ve moved to saying “Here’s something I’ve put into ChatGPT. You can click that link and try it, or use your AI of choice and copy and paste this prompt.”
I really like to model what a good prompt looks like, because it’s not simply saying, “Good luck, try AI.” New learning outcomes in my course can involve what effective prompting looks like: what it looks like, from within this discipline, when a prompt is too general versus more specific. That’s me supporting their learning in biology through prompting, which is something that didn’t exist in my learning outcomes three years ago.
Lance: What I love about what you’re doing is that you’re not saying the equivalent of “just go do research on microbiology.” You’re saying, “Here’s a method of doing it well, or better.”
Christine: I let my students know that when they leave my course, I don’t assume that 20 years from now a nurse will go back to a microbiology textbook when a new pandemic happens.
I tell them, “I want you to be prepared for COVID-39. I want you to be prepared to look up what’s happening on the CDC, the World Health Organization, the American Society for Microbiology.”
I want them to be savvy because this is showing up in their workplaces. I want them to know how to look across these different sources and synthesize what they find, because those are the skills I’m preparing them for rather than simply asking, “Did I remember that flashcard from 20 years ago?” I’d like them to become an authority with that material because they’ve developed skills with disciplinary content, real public health information, and AI.
Lance: Building off that approach, you’ve already highlighted some great ones, like the bubonic plague exploration and giving them modeled prompts; are there other activities or other ways AI has shown up in your classroom?
Christine: One of the activities I’m most excited about is something I’ve been doing since spring 2025. In our learning management system, Blackboard, a new tool appeared called AI Conversations. In short, it’s a role-play, simulated AI chat feature similar to what people had been exploring in ChatGPT, but built into the LMS.
That addresses a question many faculty had: “I’d love for my students to have simulated conversations, but they’re using some random AI. How do I know what’s really happening in that chat?” I can build a straightforward role-play scenario and provide the context for what I want the learning experience to look like.
I can define a couple of basic parameters. For example, do I want the student to be the one asking questions, with the AI providing content and responding? Or, do I want the AI to be curious and the student to clarify, provide context, and explain?
What would it look like if my students had to explain something to the AI? It reminded me of years of teaching microbiology, when I’d say, “I want you to think about this because one day you’ll need to explain it to a patient.” Say they’re treating a patient for a viral infection and the patient has questions: Is there a vaccine for this? What’s the medication? What’s the test? What does this really mean? I wanted students to have the knowledge and confidence to answer those questions.
With the AI Conversations tool, I could make that an exercise in practicing the information. I have fairly large lecture classes (two sections of 48 students). I could design one experience, and AI lets each student have a personalized version of that experience.
I set it up so the AI acts as a patient asking questions, and the nurse has to clarify: What’s the infection? What are the testing methods? What are the side effects? How do I keep myself and my family safe at home? I give a short fill-in-the-blank example prompt, something like, “Hi, I’m your nurse today. I see that your patient history is such-and-such.” The student-as-nurse can define who their patient is: maybe a labor-and-delivery patient, maybe a surgical patient, and the infection they want to practice. The AI responds in that context, and the student gets to explain and practice.
This gives them formative practice for these conversations. When they’re later assessed through case studies on the same topics, they’ve already had repeated opportunities to practice while they’re in their second year and moving into clinical health assessment.
Lance: One phrase you said I’m going to be chewing on for a while is “an exercise in practicing information.” That applied process, actually trying it out, getting comfortable with the vocabulary, is so important. I’m preaching to the choir, but I’ll reiterate it.
And I’m really curious: I love the idea of AI Conversations in Blackboard, and I’ll be even more curious when they allow multi-person chat. Because then you get into social space and learning. One of our concerns is that so much of AI continues to individualize and disconnect.
With two or more students in conversation with a chatbot, where they have to coordinate, it’s almost like a group text: what does it mean to be in coordination with other humans and an AI? That’s also some of the work they’ll be doing in nursing.
Christine: I agree. You’re bringing up so many great things that my brain is dancing around the possibilities. The reason I embraced this tool so quickly was that it gave me a way to see students practice.
And because the AI conversation was a written conversation rather than a spoken one, I could look at every prompt the student wrote. As educators, we know that when someone can explain how something works, they’re demonstrating a deeper level of synthesis.
One of the great parts is the reflection component at the end, where I ask: What did you learn from this conversation? What did you find challenging? What would you improve next time? I have students do this in each infectious-disease section of the course, so they practice with different types of infections and reflect: “Wow, I knew a lot more details than the patient did. I had to explain it, and I had to think about how to explain it at their level.” You can see them learning what it means to educate a patient.
Because this is inherently a writing exercise, and my course already carries a writing-in-the-discipline designation, I’m now working with English department faculty in our core writing program at Fairfield to study it. We are exploring our students’ AI conversations from this past semester through ideas of genre and disciplinary transfer, thinking about the conversation with a patient as a genre and the conversation with AI as a genre. I’m very excited about that.
Lance: That is lovely. I’m curious, as we step into this moment and figure out new ways of engaging students around AI, where is your attention being drawn most? What are the most intriguing considerations for you when it comes to AI in teaching and learning?
Christine: From the teaching-and-learning side, I’m inherently curious about what students think of this. One of the biggest opportunities in this conversation is to ask students more directly how they’re experiencing it.
For the first couple of years, faculty have been wrestling with what AI means for assessment, wondering if students are submitting something that isn’t authentically their voice. We need to ask students: How are you experiencing this? Do you find it helpful? Is it challenging when people around you are using it and maybe you’re not? What is it like when every class has a different set of expectations?
We really need to loop students into this process. So when a student comes to visit me in office hours, I often ask about AI because I want to hear their one-on-one perspective. And because I’ve been modeling intentional use in my classroom, they feel comfortable mentioning it to me. They know I’m here to explore it alongside them.
At Fairfield, we’re a Jesuit institution, and part of our Ignatian pedagogical paradigm is the idea that we walk alongside our students and guide them. The more we show that we’re trying and exploring this with our students, the more it invites them into our thinking.
Every time I open that up, it immediately changes the dynamic. A student will say, “Let me show you how I study with AI, how I’m using this tool to make flashcards.” I’ll say, “That’s great. Have you tried this other tool? I heard about it from another student.” They’ll lean in: “Wow, I haven’t seen that one,” and show me what they’re doing. I’ll show them what I’m doing and say, “I can give the class the study guides in a format that works better for that tool, if you’d like.” Instantly, we’re partners in the learning process.
Instead of centering the conversation on catching students cheating with AI, I keep trying to hold the mindset of: What does it mean to catch students learning with AI? Where are the moments when we can see learning happening and create more of those opportunities?
It might mean rethinking certain tried-and-true things we’ve always done. But every time I see a moment where we can capture how students are learning or engaging, I want to create more of those opportunities. That’s my goal right now.
Lance: I love the flipping of that lens. I love the relational orientation, the accompanying them, the trust-building of stepping in and saying, “Here are some things to try.” It’s hard to build as a practice, but incredibly rewarding once you get there.
Christine: I’ll add one more thing about partnering with students. What I keep thinking about is what it looks like for a student to graduate right now, in any major, without having been guided by faculty in their discipline around the ethical use of AI, its practical use, and its limitations.
I want to be clear: it’s not that everyone has to use AI with their students. It’s that we have to acknowledge that AI is impacting every student. When they graduate, their next professional role may well ask, “So what do you know about using AI in this field?” I want our students to experience opportunities to explore that question thoughtfully within their disciplines.
So whenever we can intentionally say, “Here’s a challenge,” and also, “Here’s an opportunity,” that’s valuable. We can talk about both within our disciplines, sit with the complexity, and learn together.
AI is showing up in healthcare now as doctors and nurses capture the patient interview. In some facilities, AI is in the room capturing the visit, which can allow the healthcare professional to have a more authentic, personal conversation because they’re not as focused on transcribing. It also means that professional needs the critical skill to review the transcript and ask, “Did AI catch everything? Is there anything I, the human in the loop, noticed that it missed?”
That’s a skill we could be practicing together right now. We can lean into it and consider how our learning outcomes evolve: “What does it look like for our students to discern what’s really showing up in the language? Can I vouch for the fact that this is what I saw? Did the AI miss that the patient’s body felt different?” The AI may not notice that, but the nurse would. Capturing what the AI can’t is a skill worth developing.
Lance: What you’re saying reminds me of research I was doing in the mid-2000s. The parallel I keep going back to is the difference between abstinence-only and comprehensive sex education. At the end of the day, you want to equip people with the fullest understanding of the benefits, the drawbacks, and the agency to choose versus a complete dismissal that leaves them no way to navigate other than “no.” That feels concerning, because the environments they’re stepping into may have them, on day one, being asked, “Okay, how are you going to orchestrate these agents?”
Christine: Yes. And that’s one of the best uses of these AI conversations: giving students opportunities to wrestle with the more complicated things we’re all navigating, including what it means to have nuanced conversations with people from different points of view.
In some classes, faculty set up two different chatbots with two different points of view because, right now, our LMS supports a one-to-one conversation. The student talks to one chatbot, then goes and talks to another. I think it won’t be long before we have multi-party situations with multiple students and multiple AIs. But for now, we can set up scenarios for students to practice each of those conversations.
I started doing this about a year ago, after my nursing students had learned about vaccines and immunology. I tell them that vaccine conversations are complex. There’s the science, there’s social media, and there’s a whole lot of information out there.
My job in biology is to make sure you leave this class equipped with the science. But I’d also like you to see how that science fits into the bigger picture of supporting not only your patient’s health but community health.” So I set up the AI to be the parent of a two-year-old who says, “I’m hearing so many conflicting ideas, and I don’t understand. Should I get the MMR vaccine for my child? Could you help me sort through the information?”
I tell my students I’ve set the parent up to be vulnerable and seeking a thoughtful conversation asking the nurse, “Please help me clarify.” My students practice explaining the science and community health, understanding what vaccines do, and responding with empathy to the questions the patient has.
That’s a real conversation they may have, and I want them to practice it in a supportive, low-stakes setting. It’s there for them to practice and engage as part of class participation. I’m not taking two points off for using this word versus that word. It’s formative practice. That’s how I think of these AI conversations: formative practice, focused on, “Did you authentically exercise your voice and practice this information?” rather than the minutiae.
Lance: Thinking about how we’re increasingly moving to voice models, there’s the writing piece and the voice piece, especially for nursing students. There’s a lot of richness in developing the affective piece, because, to your point, it’s about how the information is experienced. That’s a layer we’ll always need to be practicing in our professional spaces, but particularly for work like this.
Christine: I do not have the bandwidth to have that practice conversation individually with 96 students every semester, or to give each of them the agency to choose their patient group and the particular infection they’d explore three times during the semester.
Now each of them can have a customized experience, practicing those conversations and getting authentic role-play practice, while I am still there ready to support them. I read through every one of those conversations. I look to see whether they’re having thoughtful, practical, authentic conversations, and I celebrate when they’re doing something interesting or let them know, “I’d love to see you explore this part a bit more next time.”
Lance: I want to start moving into the community-of-practice work you’ve been doing. But before I do, is there anything else you want to highlight about the classroom activities?
Christine: What I’d love to finish with on AI Conversations is to make sure people don’t think it’s only for clinical nursing. Another course I teach is BioInspiration, for non-science majors. It’s the practice of looking at biological features of an organism the way engineers do and saying, “We can design something based on what nature has already figured out.” I partner with an education organization called BioBuilder, in the Boston area, run by a colleague of mine from graduate school. BioBuilder does great work getting high school students excited about biology and engineering.
Here’s how much I’ve stress-tested this tool: in the first week of that class, I invite my non-science-major students to get curious and look at the biology around them. I show them a buffet of real examples: “Look at that great beak on that bird. Engineers model a train off of it to reduce the noise going through a tunnel as a result of what a kingfisher does when it dives into the water without making a splash.”
Once they pick their example, they have an AI Conversation with that organism, where the AI acts as the organism. I’m telling you, I’ve had students talk to a sunflower, a plant, a bird. The AI Conversations tool has responded to whatever organism the student chooses. In the background, I set it up so that once the student defines the organism, the AI explores the features: What happened through natural selection? Why is that trait advantageous? Why could it be a model for an engineering design project? So I provide the context and themes.
Lance: I want to take that class.
Christine: It is a delight to read these conversations, and again, this is the first week of class. “Okay, I’m talking to a sunflower now.” We’re opening up what could happen in a class when we start to think creatively about what’s possible with these role-play conversations.
Lance: Gosh, I love that so much. There’s so much creativity in it. Personally, as someone who’s been in these conversations for three and a half years, there’s a challenge to stay excited. Not because I don’t think there are interesting things, but because there’s a steady stream of rich critique.
We want that critique, but it often comes as “this is bad, and there’s no alternative.” So when I hear an experience like yours, I love it as a “Where do we get to do things we couldn’t do before, and play with ideas?” That play is so essential to learning. Particularly in the STEM disciplines, which aren’t always presented as fun, exploratory spaces. Thank you.
Join us for part 2 in the next post!
The Update Space
Upcoming Sightings & Shenanigans
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!
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