"...starting with the “why” rather than the “what” and “how” shifts the conversation."

An interview with Hannah Jardine

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!

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Introduction

Dr. Hannah Jardine is an educational developer and K-12 teacher educator; she empowers current and future educators to enact learner-centered, equitable, inquiry-based learning experiences for students across ages and disciplines. Currently, she is the Director of Faculty Development at The George Washington University in Washington, DC, leading a team of educational developers within the Center for Teaching Excellence. Through various initiatives at GW, she supports educators in modifying their courses, assignments, and teaching approaches in response to AI. She is deeply committed to “Students as Partners,” or students and faculty/academic staff collaborating to improve teaching and learning, and believes co-creating with students is key to designing for learning in the age of AI.

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Lance Eaton: Recentl,y you shared some great insights on a listserv we’re both on and I knew I wanted to talk more with you about it. What led you to step into thinking about and more publicly speaking about AI?

Hannah Jardine: I came to recognize that we needed to acknowledge and address the impact AI is having on teaching and learning, or else the narrative would become someone else’s narrative.

It’s recognizing that, as educational developers, we’re trained to pay attention to what faculty need and want. I started in this role last July, and when I came in there was a lot of skepticism about AI and its impact in the classroom. Many faculty were looking for support on how to teach at a time when AI exists, but not necessarily how to integrate AI.

The reason we moved into this idea of AI specialists—the “AI person” on campus—is that when AI first showed up, it felt foreign and new. It made sense to lean on the people who understood how it worked and what it was capable of. But we’re at a point now where it’s such a part of our broader reality that, if we leave it only to the specialists to focus on AI, we miss the bigger picture of how it’s impacting education.

A lot of the conversation around AI is about more than just a tool. It’s more than a technology. It’s being integrated into all aspects of life and education. At this point, we have to recognize it as a shared pedagogical responsibility, or we risk focusing too much on tips and tricks instead of the core of learning.

Typically, specialists are focused on integration. But it felt like there was a need for educational developers—and teacher educators more broadly—to ask what it looks like to resist AI, but to resist it in an informed and intentional way that still keeps student learning at the center. When is AI something to push back on, question, or critique?

A focus on AI in education doesn’t necessarily mean embracing and integrating it without questioning it.

Eaton: What you’re saying reminds me of how we don’t really need specialists anymore to teach us how to use the internet. It’s just the air we breathe. There’s been an increasing level of general knowledge about the basics, which then allows us to think more deeply about how we use it pedagogically.

You don’t have to join the Borg to talk about the Borg—to use a bad Star Trek reference.

You mentioned something interesting: you came onto the team and, even though you’re not an AI person, you became the AI person in that group. Have you seen that happening elsewhere? Have you seen others have that experience?

Jardine: I can’t speak to specifics, but reflecting on my experience, our team leader recognized that we needed to establish ourselves as leading efforts around AI for several reasons. One, our faculty need help. Two, if we leave this to someone else, it might go in a direction we don’t agree with pedagogically, and the focus could move away from pedagogy. And three, understanding, acknowledging, and responding to AI doesn’t have to mean you’re excited about it or even particularly interested in using it. It may just mean using it enough to understand how it works.

Eaton: That leads me to wonder, what would it have looked like if these AI conversations had started in centers for teaching and learning from the beginning, rather than in IT or other spaces on campus?

Can we play that out? What might have happened if that’s where the conversation started three years ago when AI first emerged? What differences do you see—good, bad, or in between?

Jardine: Where centers for teaching and learning and educational developers are trained to lean is toward student learning as the goal. In contrast, other spaces might focus first on understanding the functionality of the technology: how it works, issues of privacy, and what it can do. Those are all important. But that framing can lead to adoption and integration without necessarily thinking about the larger purpose of education and why we’re here.

Centers for teaching and learning can and should lead with questions about discipline and pedagogy. What’s challenging about AI is that it relates to disciplines in very different ways. It’s not just a tool students and faculty are using; increasingly, it’s also the topic or the content itself. In environmental science, for example, are we studying AI’s environmental impact? In anthropology, are we examining how humans and AI systems interact in society? It’s not just about students using AI on assignments unrelated to AI as a subject.

Going back to centers for teaching and learning, we can guide those broader conversations: What does this mean for your discipline? What does that mean for teaching and learning? Then we can bring in how it works. But starting with the “why” rather than the “what” and “how” shifts the conversation.

Eaton: That leads to another question. If AI is framed primarily as a technological problem rather than a social technology—as some have called it—what role has that played in who’s perceived as credible or authoritative in this space?

Jardine: Educational development—and education more broadly, including K–12—is a majority-women field. But when we think about technology, science, math, and STEM, there’s still a perception that those are male-dominated spaces, even if that’s shifting.

Within educational development, I’ve noticed, for example, that many of the more prominent Substacks about AI and education are written by men.

When AI is framed primarily as a technology issue, we risk losing focus on it as a humanities issue, a social justice issue, or a set of moral and ethical questions. Not to say that men don’t address those questions, but many of the women I work with—who are deeply focused on equitable and inclusive teaching—are quick to say they don’t like what AI is doing and prefer to ignore it.

What concerns me is that if the people grounded in those equity-oriented frameworks disengage, we won’t get a complete picture of how AI is shaping teaching and learning, or society more broadly.

Eaton: I appreciate you drawing out that tension and disconnect, because we’ve seen that play out in other ways in higher ed and across disciplines, especially in STEM.

Let’s consider what’s happening in classrooms and how institutions are navigating that. In that context, what do you see at your institution or in conversations with others regarding the tension between embracing AI and maintaining a pedagogy-first approach to decision-making?

Jardine: It reminds me of March 2020. I know COVID came at us much faster than AI did, but there’s a similar tension. I vividly remember that moment. I was a postdoc in a teaching center, so I didn’t have much authority, but we had to ask: What role are we going to play in supporting teaching and learning right now?

There was a huge focus on the technology. We have to teach people how to use Zoom. We have to teach them how to use the LockDown Browser. We have to translate everything online immediately and keep things moving. The institution I was at had this phrase, “Keep teaching,” and everything was framed around that.

But something didn’t sit right with me. I asked if I could put together something around “Keep caring” instead of just “Keep teaching.” If we’re going to keep teaching at this moment, we also have to demonstrate care. I started developing workshops on building community online and being compassionate and flexible. I remember a lot of educational developers began moving in that direction, and I appreciated how much we inspired and supported each other at that time.

I see something similar now. The immediate reaction is: figure out the tech. Later, we’ll figure out how to care, how to be in relationship, how to demonstrate the human aspects. But why not start there?

A lot of what you’ve written about competing values resonates with me. Speed versus care, that was the pandemic tension, and it’s here again. The logistical versus the emotional. Innovation versus resistance. Is resistance just assumed to mean staying in the status quo? Or can resistance be innovative?

I’ve been thinking about intentional, informed resistance to AI. Not ignoring it, not pretending it doesn’t exist, not just saying “don’t use it or else.” But resisting in a thoughtful way. That kind of resistance can actually strengthen student–teacher relationships, deepen student connections, and increase engagement with experiential learning.

Some of the pedagogies we’ve been advocating for years are now easier to argue for because AI complicates more traditional assignments, like the standard five-to-ten-page thesis-driven essay. In some ways, AI is pushing us to reexamine and potentially strengthen our pedagogical commitments.

Eaton: That resonates with a lot of what I see. I keep going back to Autumn Caines’ blog post in summer 2023, Looking for ChatGPT Teaching Advice? Good Pedagogy is Nothing New.

You mentioned resistance as innovative, and there’s a compelling throughline there. Resistance doesn’t have to mean staying in the status quo. Historically, resistance has often been creative and generative.

I’m curious: are there examples you’ve seen of resistance functioning as innovation in this AI context? Not as proof—just examples you’ve come across.

Jardine: Nothing specific is coming to mind immediately, but if I were consulting with a faculty member asking how to redesign a course in ways that make students less inclined to rely on AI in unhelpful ways, I’d return to some of the broader principles we’ve been offering.

That might mean focusing on process rather than product, making learning more local, context-specific, and personalized, and leaning into experiences and interactions. Then we can still ask: where might AI appropriately play a role within that?

If we start with the question, “What skills and knowledge do we want students to gain?” and design meaningful learning experiences around that, AI might help us identify community partners or generate questions to bring into those partnerships. That’s very different from using AI to write papers that students used to write independently.

There’s space for AI even among those who are resisting. Often, what we’re resisting isn’t AI itself, but the replacement of teaching and learning with chatbots doing the work for us instead of students engaging with each other and the material.

Eaton: What’s one faculty instinct about AI that’s worth preserving, even if it looks like resistance on the surface?

Jardine: One instinct worth preserving is the concern that this new technology might interfere with our ability to think for ourselves.

I talk a lot about how AI in education has to be considered within the context of discipline and field, and that looks very different across disciplines. But regardless of the field, our students are novices. They’re entering into a discipline where they don’t yet have the foundation. When faculty worry that students don’t know enough to use AI responsibly or don’t yet have the expertise to evaluate whether what AI produces aligns with disciplinary standards. That concern is valid.

There’s also the idea of productive struggle. We know struggle is central to learning. If we bypass that, do students ever develop expertise? Or can expertise develop alongside AI?

It raises questions about apprenticeship. Traditionally, there’s a novice-to-expert relationship between teacher and student. Now, there’s this other entity in the mix. What does that mean? How can the teacher guide when something else is mediating that process? And of course, the concern about critical thinking comes up. Will students still be able to think independently?

Eaton: Let’s move into the flip side of that. Where are you seeing faculty most successfully integrate AI? Given those concerns about critical thinking and apprenticeship, what might strengthen learning relationships if pedagogy leads?

Jardine: One approach is exploring AI together with students. Recognizing that this is new and evolving, and modeling the learning process. Faculty may be experts in their disciplines, but most aren’t experts in AI. There’s an opportunity to say: we’re going to figure this out together.

As disciplinary experts, faculty can guide students in examining how AI affects content knowledge and skill development. Exploring where AI goes wrong can be especially powerful. I’ve seen successful integration through shared critique—sometimes even humor. For example, asking AI to generate a meme about your field, then analyzing it with students and pointing out what it misunderstands.

I’ve also seen integration tied to students’ passions. Some students care deeply about AI’s environmental impact. Others are interested in representation and equity. I worked with a faculty member who had students examine how AI systems are trained and how knowledge is “fed” into them—looking at the importance of including underrepresented and minoritized scholars. Students even rewrote Wikipedia pages to diversify representation, recognizing that those sources help shape AI systems.

That approach helps students see themselves as contributors rather than passive recipients. It opens space for critique, activism, and agency; that we’re not just letting tech companies shape how AI functions in education, but participating in shaping it ourselves.

Eaton: It reminds me of citizen science and finding ways for even those who are resistant to see how their voices matter in shaping the conversation. That’s really compelling.

Let me ask something a bit more complicated. We’re now three and a half years into these tools being widely available. From the beginning, there were loud voices saying this was important. With the pandemic, once people realized the seriousness, institutions moved quickly. With AI, there’s been important work—faculty doing meaningful things, institutions experimenting—but there are also places just starting the conversation.

How do we understand these last few years in higher ed? Where does responsibility sit? Especially in educational development, where there’s often a rightful critique of technology, but sometimes also a kind of badge of being anti-technology. To what degree are we in this position because there wasn’t stronger engagement from the beginning?

Jardine: Your question makes me think about where the pressure to integrate AI is coming from. Often, it’s coming from students. And that connects to a longer-standing challenge, even pre-pandemic and pre-AI: What is the purpose of higher education? Is it learning for its own sake, or career preparation?

There’s increasing pressure to prepare students for careers that will involve AI. We can’t pretend higher education is just a privileged space for intellectual growth detached from economic realities. Students are paying a lot of money, and they want education that leads to employment. Many jobs are now signaling that AI skills matter, even if employers themselves aren’t always clear about what that means.

That ambiguity makes it harder for us to respond. We’re being asked to prepare students for something that’s often still a set of buzzwords. But students are asking—sometimes demanding—an education that feels current and relevant. They want practice with these tools. They want to be able to speak about them in interviews. I’ve had students say they didn’t get an interview because they couldn’t demonstrate AI-related skills, and they’re asking where they’re supposed to learn that.

Eaton: What I hear in that is that this tension isn’t new. It’s part of a longer institutional struggle: how to be deliberate and not shift entirely toward market demands, while also recognizing that career preparation is a primary reason many students attend college. We keep struggling to translate that balance into action in ways that serve students graduating today.

Jardine: As an educational developer, there are layers of complexity. Our ultimate goal is student learning, but we don’t work directly with students. We work with faculty, who then work with students. We’re helping faculty understand how the technology works, while also grounding that in how people learn and how to teach effectively. And now how this technology intersects with that.

We’re also navigating emotions: fears, frustrations, excitement. Faculty feel them. Students feel them. Administrators feel them. And we’re trying to help faculty translate all of that into classroom practice.

When you asked about a small shift that could create long-term cultural change, I keep coming back to pedagogy first: framing conversations around learning rather than around AI. In our programming, we focus on creating impactful learning experiences in the age of AI. AI is the context, not the centerpiece.

That shift has been meaningful. Some faculty are seeing AI as prompting better practice and getting to know students more deeply, designing more meaningful assessments, creating assignments where they can recognize students’ voices and thinking.

But there’s tension. Declining enrollments and budget cuts often mean larger class sizes. A truly pedagogy-first response to AI might require smaller classes and more relational teaching, which is not the direction many institutions are moving. We’re asking faculty to do more with less.

Eaton: Much of this is about care and thoughtfulness, and that’s hard when we’re also navigating artificial structures: semesters, credit hours, fixed meeting times—systems built for efficiency, not necessarily for learning.

Let’s make it concrete. A faculty member comes to you and says, “I want to be pedagogy-first with AI, but my course is 20 students larger than last semester.” What’s your first go-to recommendation?

Jardine: I would probably start with connection and visibility of thinking. In a larger class, it’s harder to know students well enough to recognize their learning processes. I might suggest building in low-stakes, process-oriented checkpoints such as short reflections, drafts, in-class discussions, or brief metacognitive prompts. Activities where students explain their thinking.

That can serve multiple purposes. It reinforces learning through reflection. It helps the instructor see how students are developing ideas. And it makes AI use more transparent, because students are articulating their reasoning rather than just submitting a polished product.

It doesn’t necessarily require a complete course overhaul. It’s a shift toward valuing process and dialogue, even in small ways. In a large class, that might look like structured peer review, small discussion groups, or brief written check-ins that focus on how students approached a task rather than just what they produced.

My first move wouldn’t be a policy change about AI. It would be asking: Where can we make student thinking more visible, and how can that strengthen relationships and learning—even within the constraints we’re facing?

I don’t think there’s a quick tip answer here. It’s not about one small adjustment. It starts on the first day of class with transparency and space for open, honest conversations about learning. Beginning with students’ passions and goals: Why are you here? What do you want to get out of this? What do you value? From there, we can build guidelines and structures together that work within the constraints we’re facing.

If a faculty member has too many students to meet with everyone individually, how can they strengthen student-to-student connections? How can they build other forms of interaction and relationships in the classroom? It’s about seeing the classroom as a created community, where everyone shares responsibility for the group’s success. That will look different in every context, but the orientation matters.

Eaton: Why does that feel especially important in the AI space, or even just in teaching and learning more broadly?

Jardine: A lot of conversations around AI and really around teaching in general are grounded in motivation theory. What motivates people to learn? Values play a huge role. Autonomy plays a huge role. When students have some ownership and see alignment with their own goals, they’re more likely to engage deeply and lean into challenges.

If we design courses with those principles in mind—values, autonomy, purpose—AI becomes part of the context rather than the central problem.

Eaton: If we’re successful in shifting the AI conversation to be more pedagogically centered, more equity-centered, more collaborative, what would be fundamentally different about higher education? What would you see day to day?

Jardine: In a recent AI community of practice conversation, something that kept coming up was the idea that we can’t really learn in the age of AI unless we rethink grading.

There’s a sense that students are misusing AI or relying on it in ways that short-circuit learning, largely because of fear around grades. The pressure to earn high marks can push students toward shortcuts. I wonder whether we’ll see a stronger move toward alternative grading approaches. We’re already seeing some of that. Decentering grades and finding better ways to evaluate learning and give meaningful feedback could fundamentally change higher education.

Eaton: Even before AI, there were institutions that didn’t use traditional grades, and that creates a very different learning environment. Goodhart’s Law tells us “when a measure becomes a target, it ceases to be a good measure.” A lot of this is about accounting. Unfortunately, we’ve seen that dynamic across many systems.

Jardine: It shifts from “I’m here to learn and grow” to “I’m here to get the degree.” The grade becomes the point, rather than the learning itself.

It would be more grounded in motivation, trust, and community. So much of the early AI conversation and still much of it now has focused on restriction, policing, detection, catching students cheating. It’s framed with suspicion and control. A pedagogy- and equity-centered approach, grounded first in learning and responsiveness to AI, would shift that tone.

It would move us toward a more trusting, collaborative higher education: one that’s co-created with students rather than imposed on them. Whether that can fully happen within our broader social and economic structures is another question. But centering learning, trust, and shared responsibility would fundamentally change how we experience higher education day to day.

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The Update Space

Upcoming Sightings & Shenanigans

• EDUCAUSE Online Program: Teaching with AI. Virtual. Facilitating sessions: ongoing

Recent Recordings, Resources, & Writings:

• 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 the teaching and learning space, particularly for higher education, 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.

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AI+Edu=Simplified by Lance Eaton is licensed under Attribution-ShareAlike 4.0 International