Anand Rao
Stefan Bauschard
Lance Eaton, Senior Associate Director of AI in Teaching and Learning, Northeastern University [AI Faculty Cohorts Collection]
TL;DR
Higher education is asking 18-year-olds to spend up to $400,000 on a degree with no guaranteed outcome, no clarity about what’s actually being taught, and often a demand that they ignore the most consequential technology of their lifetime — all “for the love of the mind.”
Lance Eaton argues that AI didn’t break higher ed; it exposed what was already broken. The lecture-and-paper model was never grounded in learning science — it was grounded in convenience, designed by the “tech bros of the early 1900s” (Carnegie et al.) and clung to ever since.
As I (Stefan) point out: Faculty resistance to AI is increasingly an act of privilege — privilege of age, of tenure, of group consensus — paid for by students who will graduate into a workforce that already assumes fluency.
The path forward isn’t abstinence (which has never worked as policy) but radical engagement: start where students start, treat the syllabus as a living conversation, and accept that the assessment isn’t a hoop to jump through — it’s how we reach new ground. And in an era when AI can produce a passable essay in seconds, debate becomes one of the live demonstrations of human thinking — real-time, adversarial, accountable, impossible to outsource. It’s exactly the kind of “demonstrable learning” Eaton argues we need to get closer to.
Eaton will open this thread as the keynote speaker at the University of Mary Washington’s Reimagining the Liberal Arts in the Age of AI conference on July 22–23, where the question on the table is no longer whether the liberal arts must change, but what they must become. Three years in, we’ve already lost a graduating class to institutional paralysis. We can’t afford another.
Introduction and the Upcoming Keynote
Anand Rao: Welcome, everybody. We’re excited about our guest this week — Lance Eaton, Senior Associate Director of AI in Teaching and Learning at Northeastern University. He received his doctorate in higher education from UMass Boston and has been teaching in higher ed for 20 years. Stefan and I have both been fans of Lance’s work for a long time and are excited to have him join us. Thanks so much for joining us.
Lance Eaton: Super excited to be here. I’m similarly a fan of your work. Glad we’re getting to spend some time nerding out.
Anand Rao: One of the reasons I’m particularly excited is that you’ll be the opening keynote speaker at the conference my university is hosting — Reimagining the Liberal Arts in the Age of AI — on July 22–23. The title of your keynote is From Awakening to Building: Liberal Arts Next Moves for Teaching and Learning in the Age of AI. Do you think the liberal arts are ready for next moves? Are we ready to start to pivot?
Lance Eaton: No — but when are we ever ready for change? If we were ready for change, we’d have changed. I come from this as someone who teaches history, literature, and writing. It’s an area I care deeply about. I’m a true nerd — I love to read, I love to be deeply involved in the life of the mind.
We’re navigating a tension about how we’ve been doing things, and so much of what people consider “AI issues” are less about AI and more about what AI revealed was already there. It just magnifies it. Part of the conversation in July builds off two earlier talks I gave with Connecticut College and the AI and Liberal Arts Symposium last October — where I got to meet both of you in person.
For all the problems people have with AI, it gives us a moment to radically reflect about what we’re doing — what we should set aside and what we should hold on to. I don’t think we’re going to see another generational moment where we get to actually try to make this better. Prior to the pandemic, we were largely just going along. The pandemic uprooted everything. Then AI uprooted everything again. It would be incredibly valuable for us to think about how we get closer to the source of what demonstrable learning actually looks like.
Lance Eaton: This is why I’m a big fan of Stefan’s work — diving into debates and using debate as a live form of getting people to demonstrate their understanding. What I’m trying to do in this talk is build off the first talk, The Sleep of Liberal Arts Produces AI, and the February follow-up, The Awakening of Liberal Arts, to really think about: now what? What does this look like in action, what’s going to feel uncomfortable, and what can be exciting?
The Convenience Model of Education
Stefan Bauschard: A follow-up — in the abstract, you’re saying AI will expose long-standing tensions, making them visible. When are we going to move on? COVID happened, and people thought, “I have to modify things temporarily.” Then AI came along, and the reaction is to defend the status quo against AI. But the status quo is the lecture and the paper. Those were always problematic. Could you unpack that idea — that AI brought it to the forefront, and that the lecture isn’t some magical way to teach?
Lance Eaton: Two trains of thought, and I’ll try to connect them.
The first: we always seem to forget that the way we did things was decided largely on convenience. The way we structure higher ed and K–12 is a convenience model. Scientifically, there is nothing that says having people meet in 50-minute, 75-minute, or three-hour chunks intermittently throughout the week for 15 weeks is the proven way to do it. We have a model more or less given to us by Carnegie — who himself was the tech bro of his era. A lot of higher ed is structured around what the tech bros of the late 1800s and early 1900s decided education should look like, and we’re now clinging to that. Which is hella ironic. We don’t have education grounded in how people learn. We have education structured around what’s convenient and what we can get away with.
Part of my background is in open education and open access. We keep forgetting that the textbook was the mass-produced way to share knowledge. Does it still make sense to have a 50-page explanation of a process when a two-minute video does it so much better? Those things are coming to the forefront for students and faculty.
Second: the classroom and the lecture. I have concerns about lecture when it’s the only method. We need a diversity of approaches in our classrooms. Some sharing of knowledge and information is something we all crave — it depends on how it’s structured and who the messaging is for. We’re sitting here doing a podcast, which in some ways is a conversation among us, but it’s still essentially a lecture to listeners. Many of us love video essays. There’s still value there. What we’re negotiating is the value and meaning of the learning space.
“Militant Apathy” and the Post-Pandemic Student
Lance Eaton: I was at NERCOMP in March. Beth McMurtrie, who writes for the Chronicle, has done some really great work — an editorial piece on teaching and learning centers. One thing she highlighted that I’ve been chewing on ever since is that, as we came back from the pandemic, students returned with what she called “militant apathy.” Awesome term — let’s pause for that.
That made immediate sense to me. Students came back to a kind of false sense of reality. They went online, did all their learning, and then were told they had to come back in person. There was a sense of fraudulence: Why didn’t I get a discount? Why were you charging me the same? That creates real distrust about whether institutions practice what they preach.
Then AI shows up and disrupts that further. You’ve got to be prepared to write long reports. Why? There’s a machine that can do it. These elements feel fraudulent to students and to the world: Oh no, this is the real thing — but maybe not.
That gives us an opportunity to ask in our classrooms: Who am I doing this for? I have amazing colleagues who don’t lecture, but they’re still struggling: How do I approach learning if AI is ubiquitous? It’s not just the lecture part. It’s: what do I do when I have these tools that, in different ways, outpace me in content and output? What is my new emerging role and identity? That’s the question a lot of us are grappling with.
Why AI Is Different
Anand Rao: I love that phrase, militant apathy — and I see some of it in colleagues, too. There’s a lot to unpack. The curtain was pulled back during COVID in many areas of society: You said you couldn’t give us universal healthcare, but you did for a little while. You said you couldn’t give us universal basic income, but you did for a little while. Why can’t you continue that? That’s the context we’re all living in.
I want to ask about how this moment is different from past disruptions. We’ve all used the example of calculators disrupting math. The internet. The printing press. If the standards we accept were really just determined over the last 150 years with no real scientific basis — why didn’t we see those disruptions as opportunities to consider these problems? You’re optimistic about this as an opportunity. Why does AI present that opportunity in a way we haven’t seen before?
Lance Eaton: It’s the speed and the ubiquity. With the internet, it took 15–20 years before you could feel reasonably comfortable that everybody in the room could access it. It might mean spending extra hours at the campus lab. The system itself took that long to work out where students were learning these things and how they came prepared to engage with them.
AI hit 100 million users in less than two months. In under three years — now over three and a half — it became ubiquitous in all the major tools we use. It’s all across Google, all across Microsoft. For most students and faculty, it’s already here. With the internet, there were more opportunities to push it away, more opportunities for incremental adjustments.
I remember faculty in undergrad telling us online databases weren’t real. During my dissertation research, I found online journals, and people argued they weren’t real journals. Now nobody prints physical journals. There was gradual progression. AI compressed all of that into under two years. Even now, institutions are starting to give students access to models — and that’s still mostly happening at the most resourced institutions.
The internet probably should have required us to shift, but it didn’t enforce it. You could still use the same assessments. There was more plagiarism — I had a student from Harvard tell me that in high school they’d run papers through nine different translators and back into English to avoid getting caught. It was happening, but it didn’t problematize assessment the way AI does. AI forces the shift. Now it can write the paper, do the test. We’re going to have it in Meta glasses, in contact lenses. There’s no way around it.
Small Design Shifts That Increase Learning
Stefan Bauschard: As a follow-up — you promised “a small set of design shifts that reliably increase learning and value.” What are some of those shifts, and why do you think they’re reliable?
Lance Eaton: The one I’ve been leaning into and getting the most traction with is also the most uncomfortable: have students start research with AI.
It’s uncomfortable for faculty, especially those who haven’t used AI. The way I help them understand it: there was a time when doing research meant physically going to the library. Nobody goes to the library anymore. I did an entire dissertation, never set foot in the library, had thousands of sources. Why? Because there’s an efficiency, a clarity, compared to the card catalog.
We’re in that same relational space now between exploring databases and starting with AI. The three of us are fairly comfortable navigating databases. But if you’re early in your career or early as a student, starting with AI feels incredibly more efficient and sense-making — so long as you use it well and recognize it as a start, not the end.
This is the through-line that will be hardest for faculty to navigate. The reason this move matters: we have to start where students are in our teaching practice. I’m guilty of starting courses where I am and expecting students to get to me. Increasingly, we have to recognize where they’re going to start. They’re going to start with AI. We can either help them do that well — and lead them to the work we want them to do — or we can keep fighting them.
We can say “go to the databases.” They’ll still go to the chat. They’ll go to the thing with less friction, because databases are full of useless or confusing friction. When we talk about small design shifts, the mindset is: if I know there’s a task they’re likely to start with, how do I help them start there in the best way possible?
The reliability piece is contextually dependent and that’s what’s hardest. It will require more from us — more faculty effort to figure out what works. I’m equally curious when a colleague has done something with AI and it’s gone well or gone badly. I want the details — down to the prompts. There’s a lot under the surface we’re just starting to scratch.
Resistance, Privilege, and the Ethics of Avoidance
Anand Rao: I find that compelling, but colleagues push back on the work I’m doing at the center. They ask: Why do you claim this is inevitable? Maybe we have agency to stop it. Instead of meeting students where they are, maybe they’re not in the right spot and we need to reintroduce friction. What do you say to colleagues who argue we should resist?
Lance Eaton: It’s onions all the way down.
On a political level, yes — they can absolutely push back, and I’d encourage that. I agree with many of their concerns. I sometimes get frustrated when answering questions because I find myself saying things that are true but sound like I’m defending tech bros — which isn’t where I want to be. I have lots of concerns about what they’re doing.
The inevitability part: these tools may not, in the long run, be inevitable. But how long do we keep having that conversation at the cost of students? We’re in year four. Students are graduating this year having had four years at many institutions with no clear institutional guidance, no curriculum change, and a scattershot engagement with what AI means in their work. That feels negligent. Even if the argument is “you shouldn’t use it at all,” that grounds itself in critical thinking — and critical thinking requires actually engaging with these tools. You don’t talk about it like it’s Fight Club. That’s problematic, because students will encounter AI on their first job and be told to use it. They can absolutely resist if they’re uncomfortable. But have we prepared them to work in places where this is the norm? It’s great to have values. It’s also great to have dinner.
The bigger frustration: look at the proposition we’re putting in front of students. It is utterly insane.
We tell students to spend tens or hundreds of thousands of dollars on this thing called education.
We give no guarantee the outcome will result in a degree.
We give no guarantee it’ll result in a job — or a job that pays the bills.
We give them very limited agency over their course of education.
We make them take courses based on 300-word descriptions, even though a single composition class might be offered in 100 wildly different sections.
We expect them to do this semester after semester.
And we expect them not to use the technology that’s everywhere and that employers are increasingly demanding.
All for the love of the mind.
I deeply believe in the life of the mind. But asking that of an 18-year-old is insane. We need to be critically engaged. You don’t develop judgment and self-efficacy through abstinence. Abstinence generally doesn’t work as a policy — and I say this as someone who spent his 20s and 30s researching abstinence-only education and the problems it creates.
Stefan Bauschard: I almost think resistance is an extreme act of privilege. Privilege of age — people further in their careers can wait it out. Privilege of tenure. Privilege of group-think — if everyone around me agrees no kids will use AI, then they have no alternative.
And it’s at the expense of the younger generation. College tuition is roughly the price of a first home — $200,000 to $400,000 in most markets. Imagine selling someone a house and saying:
We won’t tell you the resale value.
We’ll let you know where it’s located later.
We won’t guarantee the roof doesn’t leak.
You’ll probably need to pay for additional improvements after — like AI classes you’ll take after you graduate from our $400,000 institution so you can actually get a job.
But there’s a chance it’ll work out.
That’s the deal. The more I think about cost — and I’m on my second kid going through high-tuition universities — what if I just bought them a house? Would they be better off? In many cases, AI will probably have more direct economic impact on their lives than what we’re teaching them.
This is an extreme position of privilege. You can’t go to a company and resist AI — you won’t have a job. You can’t go into the military and resist AI — they’ll kick you out. Resist AI — but recognize who pays the cost.
The Highest-Leverage Moves for Fall
Stefan Bauschard: For a faculty member listening — say a 4/4 load at a regional comprehensive, or 5/5 at a community college — what’s the single highest-leverage move they could make going into the fall?
Lance Eaton: I’ll give three. These are the ones I’ve been talking about almost since day one.
1. Play with it until you have an “aha” moment. If you have a negative disposition — which is valid — play with it until you get a moment of wow, that did something really cool. The expectation isn’t that you keep doing it. It’s to understand the way this tool can feel unlocking and powerful, just like so many other tools, and to hold that in your head as you think about what it means for your students.
2. Find a community of practice. Find people to play and share with. AI intersects so much with thinking that the more we see how others use it, the more we can refine our own practice and find ideas that work for our classes.
3. Make AI an active conversation throughout the course — not just a syllabus policy. As you approach every assignment, articulate the ways students can use AI. Give them prompts. Create space to ask: Did people use this? What worked? What did you like? The absence of that means people are taking guesses and taking semesters to figure out what’s happening, rather than refining assignment by assignment, activity by activity.
None of this will be perfect. It’ll continue to be messy. That’s the work — and it was always messy, even before AI.
Anand Rao: I love that answer. We have to have those conversations. Students are more likely to buy in, more likely to take it seriously, more likely to view you with credibility. You get a better sense of where they are. And it’s all changing so rapidly that even if we gave them a playbook, it would be outdated before the end of the semester. The hard part for many faculty is that it looks like you’re giving up authority or expertise — and that’s tough to admit.
Lance Eaton: That’s the damning part — because that’s performative expertise. The real expert knows what they know and knows what they don’t know. There’s a compound effect that meets the availability heuristic in our learning spaces: semester after semester, we refine things and still get students asking the same questions. We build perceptions that we’re improving but students aren’t — so the students must be the problem. We forget we’re no longer students. We don’t always have a beginner’s mind, or patience with beginners. I’ve taught this a thousand times — why isn’t it obvious to you? There are psychological things we’re all navigating in this space.
Faculty Cohorts and the AI Faculty Cohort Programs Collection
Anand Rao: You mentioned communities of practice. I want to use that as a segue to talk about your recent project, the AI Faculty Cohort Programs Collection. You have over 100 institutions contributing. Tell us about it — and any patterns that surprised you.
Lance Eaton: At Northeastern, on the Center for Advancing Teaching and Learning Through Research (CATLR) team, we’ve been running faculty fellow programs around AI for two years. They’ve been rich and interesting. We thought: What if we engaged other people running fellows programs to learn and exchange?
So we did what I’ve done before — created a Google Sheet, a Google Form, and asked the world: What are people doing? But beyond the resource itself — which is valuable on its own — people are hungry for the conversation. So we’re also doing a convening on May 27. If you submit your cohort program details, you’re invited to do a deep dive: what’s working, what isn’t, how can we help one another?
The bigger picture: we’re hoping to lead toward a two-day summit in early August where we invite not just facilitators but also the faculty inside these cohort programs. One of the richest things in communities of practice is getting to learn what others are doing — and seeing how others experience their cohort programs. They’ll all be different. There are 100 institutions in this collection and hundreds of others doing similar work. What can we gain by being in conversation? How can we move the needle on supporting faculty?
Liberal Arts, Transfer Skills, and Teaching for Adaptability
Stefan Bauschard: You write that the liberal arts remain indispensable — but only if we translate our values into learning designs that hold up under new conditions. How do you define the liberal arts, what’s especially important about that concept now, and how might you translate it into practice with an example?
Lance Eaton: When I think about liberal arts, I’ve always thought of it as the set of thinking tools that empower you to move through the world and be better at transference. That’s a personal definition, not a textbook one.
Here’s what I keep centering on: I genuinely believe students already have nearly all the skills we want them to have. The problem is transference.
Previous to Northeastern, I was at College Unbound, which focused on adult learners and used competency-based portfolios. We tried to emphasize — and I think this is true of younger traditional students too — that students are already practicing many of these things, just in domains the liberal arts don’t validate.
If you were a single parent during the pandemic, you were practicing the hell out of problem solving. You have a master’s degree in problem solving, because your kids survived. Many students have these skills. The question is: how do they further develop them and transfer them? That’s where the liberal arts comes in so powerfully — it gives us many domains to try out: this is what problem solving looks like here, this is what it looks like there. We’re trying to abstract it as a process. That’s incredibly valuable as we move into an AI era that demands flexibility and the ability to transition contexts — professional, technological, agentic.
In the classroom, one of my favorite activities — I’ve been doing it for eight or nine years — draws this out. I teach a lot of literature and history, where interpretation matters. I tell students: You all know how to do a deep read of a text. You know textual analysis — you don’t call it that, but you know it. I prove it with an exercise of two words.
I put on the screen: I just got this text from a friend. All it says is: “It’s fine.” Tell me — is it fine or not, and why?
I’ve had to cut off that conversation at 20 to 25 minutes. What are they doing? They’re doing textual analysis. I use that as the starting point — meeting students where they are, helping them see what’s already there, then bringing them toward the discipline.
The Agentic AI Gap
Anand Rao: The variety of institutions in the cohort collection is itself a great example of what learning environments can look like. I’d love to follow up after August. But I want to shift to AI agents.
There’s a gap between people resistant to AI and people accepting it. There’s another gap I’m worried about: between people using a chatbot and people using agentic AI. For most users, the standard chat experience hasn’t changed much — Claude 4.7 Opus doesn’t feel that different from 4.6 in regular use. But when you move to agentic work, there’s an explosion of opportunity. I’m running a workshop for local business leaders on agentic workflows. I’m worried we’ll get everyone to where AI was a year ago, and they’ll miss the rest. How do you approach that gap?
Lance Eaton: I don’t know — I might ask an agent to figure it out.
A comparison I made when paid versions first emerged: the free version was like being on the internet in the 2000s — but when I asked what you meant, you said “I’m on AOL.” That’s the difference between AI chatbots and agentic AI today.
I worry about a few things.
First: complexity. It’s another layer of language and configuration — workflows, security, permissions. And friction is genuinely concerning here — because the tendency, and I’ve been guilty of this, is to just hit “always allow.” When I’m using Claude Code or co-work, I catch myself granting free reign. That’s not a good idea. The tools make it easy because they often only give two choices — black or white. I think there could be much more gradation.
Second: I’m still trying to figure out the learning use cases. With generative AI, fairly early on — I started teaching a course on AI in education in January 2023 — I could find positive uses: simulations, debates, reflection, scaffolding. Things this could really help with in the learning space.
Agentic AI? I’m still grappling. Whether real or performative, the Einstein AI moment was when everyone went check your draws — sign up for $40 a month and a bot will do all your classes. So we know the concerning, problematic pieces. But what’s redeemable — beyond productivity? I’ve used agentic AI for some really cool nerdy productivity work. But what about the learning space? That’s what I’m still working out in conversation with people like you.
Stefan Bauschard: I tried to come up with educational examples. The clearest one: initial feedback on student work. As soon as a paper hits the submission system, the agent provides initial feedback — clearly labeled this is not from your professor. Students could revise before final submission.
I think of this because, working on our pluralism book, someone suggested: When you think a chapter is done, put it back in the AI and ask it for major references you’re missing. That worked great — three or four useful additions. That kind of thing could happen automatically as you work — the AI sitting in the background. It requires prompting, but that’s a workflow.
To me, the value isn’t giving faculty all the examples — it’s getting students to see how workflows function, so when they enter the workforce or become entrepreneurs, they have the intuition. This wasn’t working, so I tried it this way. That’s workflow thinking.
I have similar concerns to Anand. It’s hard to make the case for agentic AI to skeptical colleagues worried about loss of friction. The immediate case is: students will need this because the job market demands it. So one response is: teach it at the end. But that’s the problem from the ad — wait four years and we’ll tell you.
For me, two things stand out. First: manage the agents. I’m experimenting in a small-group communication course with the idea of small student groups working with agents as participants in the group. The friction is preserved — humans have to make decisions, guide the agents, give and receive feedback.
Second — and most important: we have to view these tools as malleable. It’s too easy to say the agent can do this work better than I can — so let it. But if we view it as a jetpack that lets me do better work because I decide what it does, that changes the equation. The only way to get there is to first understand how it works.
The hard part: this might undermine motivation. Why design a workflow if I can just automate it? How do we get to a mindset where the standard isn’t typical or past practice but something better?
Stefan Bauschard: Maybe the product isn’t necessarily the focus. You’re grading the workflow — with the understanding that, over time, a good workflow yields a good product. That works especially well in interpersonal or group settings.
It’s tricky, though. Students will see agents doing all the work and think: I heard companies are replacing people with AI — I see how now. It’s not one AI, it’s a thousand AIs. Dave Blondon said he finally got a thousand AIs to work together. I can’t out-compete a thousand AIs working together.
Going back to value: all of this has to become more practical and meaningful. That’s where co-ops, internships, and similar models lean in.
Lance Eaton: This is where open pedagogy lives — the output has a life beyond the grade. Service-learning projects. Digital projects. Things other students will use. That reframes the assessment: the assessment isn’t there to prove a hoop was jumped through. The assessment is there to reach new ground. That’s where I’m slightly excited. I still worry about agentic AI specifically, but with generative AI, I feel there’s good grounding right now.
Three to Five Years From Now
Anand Rao: If a liberal arts college made this transition well, what would be concretely different about the student experience three to five years from now?
Lance Eaton: One: they’d still be in the transition. Both because the tools are still changing and because this is inherently a refinement process. Whenever colleagues say if you had them do this, AI could still do it — yes. It’s AI all the way down. There has to be a cyclical process of revisiting what we’re doing.
Two: clearer distinctions between major coursework and breadth coursework. Practices that are deliberate matter more in the major than in a course outside of it. Helping students understand that distinction — and articulating it in how courses are designed — matters more.
Three — and some in the liberal arts will hate this: they’ll be more tightly attuned to industry and disciplines. AI forces us to ask: what do we still need to teach, and what do we need to let go of? Just like the calculator forced that. Just like the internet did. The problem is we won’t know the impact of what we drop or hold on to until people are in the field. That’s three to five years out. We’ll need to be in regular feedback loops with graduates — where they feel prepared, where they don’t — and actively recalibrating every two or three years, when historically calibration was much more deliberate over longer periods.
Stefan Bauschard: We’ve mostly talked about methods — teaching methods, assessment methods. We could do another podcast on content. Are we still teaching coding? Or does coding become the freshman composition class — everyone takes it, but few major in it?
The hard part: change is slow. Adding a course can take a year. Restructuring a department is much harder. We’ve seen departments dissolve through attrition at Syracuse and elsewhere. Industry is changing very quickly. Education in most places isn’t even trying to.
Lance Eaton: Yeah — and that’s part of why I framed it as successfully transitioned. The school that’s transitioned well has already figured out how to change faster than its peers. That’s also part of the answer about agentic AI: we have to introduce it.
I was doing a workshop a week and a half ago at North Shore Community College — where I first started teaching, where I first did instructional design, a place I love and graduated from. As an icebreaker, I put up a slide: In April 2010, I presented here on a technology that was ruining education. Faculty were severely worried. What was it?
Wikipedia.
That moment crystallized something for me: what I was doing then is the same as what I’m doing now. It’s not that I love these technologies — I find them interesting, but I don’t necessarily feel good about them on lots of levels. But they’re here. I don’t think we serve students by refusing to wrap our heads around them. Fifteen years later, Wikipedia is a different story. People say: go there to start your research. Let’s practice public writing. Let’s look at debate by going into the history of these posts. We can’t refuse to engage with the world our students live in. And that means grappling with things we may not like.
Closing
Anand Rao: Lance, this has been a great conversation. I can’t wait to welcome you to campus this summer. For those who haven’t followed your work, where can they find you?
Lance Eaton: The easiest way: search Lance Eaton and Substack. You’ll find mine — AI + Edu = Simplified. It really isn’t simplified — that’s me being a little ironic. From there, you can find my other hubs.
Anand Rao: Excellent. Thanks so much, Lance.
Lance Eaton: Thank you.