An Interview with Lance Eaton

An educational consultant who has been working with schools coping with AI

This week’s post features an interview with Dr. Lance Eaton, writer, educator, educational developer, and consultant. Dr. Eaton has been thinking about AI in higher education almost from the moment ChatGPT was introduced in November 2022. Many know of him for his repository of syllabi policies for AI generative tools, which has now been viewed by tens of thousands. Dr. Eaton also writes a substack that I highly recommend, AI + Education = Simplified.

DB: How do you think your background in faculty development and instructional design prepared you for this new Gen AI moment?

Lance Eaton: My time with faculty over the last 15 years has been what allows me to be successful in working with them and make them feel valued, recognized, and willing to take risks in their teaching and learning because of the space that I set up. I want to model the type of relationship, the type of connections and meaning-making with faculty as I hope faculty will with students. There’s a lot of trust. I try to be honest and explain my concerns, but also recognize their agency. And so I feel like that’s something that I’m now doing in this other space around AI and education.

DB: What do you think your biggest challenge has been in talking to people and getting them to understand and accept this technology?

Lance Eaton: I think the biggest challenge is helping them to understand we have to live in this in-between, uncomfortable space where we are right to be critical and have our eyes on a lot of concerns about how this could go wrong. And also, we have to be careful to not allow that to paralyze us. It’s like, Alice in Wonderland, believing six impossible things before breakfast.

DB: That segues nicely to the next question. I know in other talks you’ve given, you’ve advocated for being simultaneously skeptical about AI, but also open to it, and that just seems like a tough balance. How do you keep that balance yourself?

Lance Eaton: I have degrees in History and American studies. Degrees where you are really scrutinizing things and thinking about impact and cause and effect. I can see a great deal of the problems that AI represents. I can see that it is a tool of hyperproductive capitalism. I can see the environmental pieces. I can see all of those things, and know they exist. And trying to be mindful of those is hard.

The optimism is grounded in my own experience. There’s a good amount of people I know who have been using these tools, and found them incredibly helpful. It helped them figure out things that they didn’t feel or weren’t equipped to figure out on their own. Helped them to create things that they have the vision for, and maybe need some additional support and guidance to execute. Or just very simple sense-making that changes the way you might approach things.

Every technology has these uncomfortable trade-offs. This is gonna get a little funky, but there’s comfort in the discomfort, because that means I am trying to hold things in tension. And holding things in tension for me means that I am… paying attention.

DB: Let’s get into your areas of expertise and talk about AI use in students, educators, and administrators. Let’s start with the students. I know that when I talk to students, their biggest concern is the job market. So, what kind of advice do you give to students when you’re talking to them about the future job market and how fast things are changing?

Lance Eaton: The piece of advice that gets bandied about that does feel almost manufactured: “You won’t be replaced by AI. You’ll be replaced by somebody that uses AI effectively.” I hear that, and that feels like such corporate speak. We’re gonna be living in a world that is way more dynamic. I really love that question of, not what do you want to do, but what are the problems you want to solve? Because that’s what every job is; it’s a certain kind of problem solving. And what is it that excites you? You want to think about this as different directions you can take. Both professionally, like, this job over that job, but even conceptually in the different types of work. You’re not just funneling down to one thing, but you’re building experiences and insights that allow you to move across domains. There’s a great book from a couple years ago: Range, by David Epstein, “Why Generalists Triumph in a Specialized World.” You’re gonna need that generalist disposition to figure out where you can fit in in specialized places.

DB: Let’s switch over to talking about teachers. I know you’ve talked to a lot of teachers about how to incorporate Gen AI into their instructional design. So what are some strategies to promote learning?

Lance Eaton: I think red teaming, or just this idea of using AI output as a form of exploration and critique is a really good first step. We are already living in a world of AI slop. Why not engage with that if we’re seeing it end up everywhere? We’re highly educated people who have been studying our fields for possibly decades. It can be very obvious to us. We have that expert knowledge. Why not use those things that are blatantly obvious to us with students to help spot that? This is critical analysis. This is deep reading. This is doing the things we’re want our students to do anyways. I see a lot of traction, a lot of inroads with that. And then go into your disciplines. Think about “how is this changing the field?” What new things do they need to teach around AI, or just the field in general? And what things do they need to let go of? You know, at one point we gave up things like the card catalog, right? How we do research is fundamentally different than how we did research 30 years ago. New skills are going to show up, old skills can be let go or modified.

DB: My colleagues biggest concern is cheating. It’s just a huge, rampant issue within universities. Do you have concrete strategies you’ve suggested?

Lance Eaton: One of the things I’ve been doing lately in a lot of my workshops is I’ll ask faculty “how many of you have had meaningful conversations about AI with your students?” Conversations that aren’t “Here’s what the rules are.” Conversations that are actually dialogic. To me, this is the opening gambit. Why are we not talking about this with students? In the absence of thoughtful, meaningful engagement with this tool over the last 3 years, which has been in front of them in lots of different forms what else are students left to do? Yes, they should be accountable, but people are gonna be people. And so I think that the biggest piece of advice is having honest, earnest conversations with students that isn’t just punitive, isn’t just hierarchical, but is really trying to surface an understanding about where they are, and what they are figuring out, and why, or why they wouldn’t use it in class, or what might be appropriate ways of using it in class. You gotta talk to the students. You gotta build that trust and that relationship to figure out what is and isn’t gonna work. That does reduce the willingness of students to use it inappropriately. You’re treating the student as an adult.

DB: What about if you’re talking to academic leadership. What sorts of things do you suggest when you’re talking to them?

Lance Eaton: I think any institution needs a policy. It’s not that the policy needs to be big or expansive. Just even covering the basics is really important. It gives faculty a stronger sense of where the institution stands. And that helps them think about how they will use it in their classroom. If you don’t have a policy, faculty members don’t know if the institution’s gonna go to bat for them on edge cases. And there’s gonna be a lot of edge cases with AI.

DB: What kind of advice would you have for institutions trying to craft an AI policy?

Lance Eaton: It’s not gonna be perfect. Structure it so that there’s intentionality. Come back to it year after year to make sure “does this still all work?” Ground it in the legal implications and understandings. What are the protections that are owed to students and faculty? Some things that are incredibly clear for the time being. I do not think faculty should be putting students’ work into AI. For whatever reason, whether it’s for grades, whether anything like that. I do not think we are in a place to do that. Hold off on that until there’s a more thoughtful, deliberative process developed by the faculty. I love to see in faculty governance a really thoughtful exploration of “Where does the tool fit?”

DB: I want to be mindful of your time. Is there anything I missed that you’d want me to share with readers?

Lance Eaton: The two things I keep going back to in all this is its absolutely valid to dislike all the problems in the world with AI. I think it’s incredibly important if you are in that space to actually use AI. Until it surprises you, or does something useful. I’m not saying you have to keep doing that, but get to a point where you have that aha moment. I think that’s really important for people to hold onto before they go into their classroom and present their view of it as this horrible thing.

That’s one piece, and then the other is this is collective work, and the more we can share and talk and learn from one another, the more we can elevate things that did work, things that didn’t work, things that we’re learning. Nobody is going to hold the solution to this individually. It is going to be held in collective trust, whether that’s within the discipline, whether that’s within your department, your division, your college, whatever associations you’re a part of, this is collective effort to figure out something that is complex and ubiquitous. A lot of us are suffering in our own silos, and if there’s ever been a time to break down silos, this is it.

DB: That’s a great closing quote. Love that!