--- title: "Future-Proofing the University: AI and the Future of Higher Education with Bryan Penprase" podcast: "Modem Futura" episode: "Episode 104" kind: transcript hosts: "Sean Leahy and Andrew Maynard" date: 2026-09-29 audio: https://cdn.simplecast.com/media/audio/transcoded/fca32cee-ef4c-4b50-82e5-4c768318a8c1/018f87c3-4d42-4bc0-9d66-53873b51ec68/episodes/audio/group/94bed528-fd17-4272-9528-67b912e13ddd/group-item/349605b6-f71d-4111-a4a3-340d3825535c/128_default_tc.mp3?aid=rss_feed&feed=3frRI0HN show_notes: https://text.futureofbeinghuman.com/modemfutura/104-future-proofing-the-university-ai-and-the-future-of-higher-e.html mirror: https://text.futureofbeinghuman.com/modemfutura/104-future-proofing-the-university-ai-and-the-future-of-higher-e-transcript.html --- # Future-Proofing the University: AI and the Future of Higher Education with Bryan Penprase *Full transcript* Modem Futura · Episode 104 · September 29, 2026 · 64 min · Hosts: Sean Leahy and Andrew Maynard · about 11,000 words Show notes: · Listen: [MP3 audio](https://cdn.simplecast.com/media/audio/transcoded/fca32cee-ef4c-4b50-82e5-4c768318a8c1/018f87c3-4d42-4bc0-9d66-53873b51ec68/episodes/audio/group/94bed528-fd17-4272-9528-67b912e13ddd/group-item/349605b6-f71d-4111-a4a3-340d3825535c/128_default_tc.mp3?aid=rss_feed&feed=3frRI0HN) · [Apple Podcasts](https://podcasts.apple.com/us/podcast/future-proofing-the-university-ai-and-the/id1771688480?i=1000792155068&uo=4) · [Spotify (show)](https://open.spotify.com/show/3eFl4hY4t1qTCWE2Bxotrg) · [YouTube (video)](https://www.youtube.com/@ModemFutura) --- **Sean**: All right. Countdown activated. Virtual studio engaged. I don't know. **Bryan**: Yeah. Ready for lunch. **Sean**: We're here. We're here. We're ready for lunch. We're getting in it. That's good. All right. So, Andrew, we're here. We've got a special guest. So I say we'll just jump. We'll forego everyone's favorite part, which is me blabbing on. **Andrew**: The shortest cold open ever. **Sean**: Yes. We're going to set a record for the shortest cold open ever. We'll just jump into it. **Announcer**: Initializing waveform. Modulating signals for transmission. Exploring the possible, probable, and preferable futures. Welcome to Modem Futura. **Sean**: So anyway, thank you for joining us. Welcome to Modem Futura, the show that explores the intersection of technology, society, and the possible, probable, and preferable futures, asking deep questions like what will it mean to be human in the future? I'm Sean Leahy, joined by my co-host extraordinaire, Andrew Maynard. And today we are joined by a special guest. Today, we have Dr. Bryan Penprase with us, who is the professor of physics and astronomy at Soka University of America and also serves as vice president for sponsored research and external academic relations. Bryan has a list of four books that he has authored and countless papers and all that kind of fun stuff. So we'll be sure to link, Bryan, your profile link in the show notes. But also, I want to make a special announcement for your most recent book that has just been released that is entitled Future-Proofing the University: Innovations in Learning, Technology, and Finances in the Age of AI. So, Bryan, welcome to Modem Futura. **Andrew**: And the obvious question is, are you going to tell us how we keep our jobs in an age of AI? **Sean**: I got to know, man. I got kids. I got kids to put through college. I got a roof I need to put on my house. How do we keep this thing steady? **Bryan**: Well, you know, the business model is broken, as they all say. Right. And yet the product is in great demand. So I think there'll be even more need for people who can think clearly, especially in the age that we're living in. So I think our jobs are secure because there's a definite need for higher education for universities. And in a way, they're not really stepping up, I think, as much as they should, because everyone's clamoring for someone to figure out how to save us from the robots destroying civilization. The universities played the key role in developing technologies and ideas that brought about AI. And they're also moving in lots of directions to help us thoughtfully use the AI. And so I think the universities have a big role to play in navigating us out of the mess that we're in right now. **Andrew**: Right. It actually sounds a little bit like the sort of AI companies sort of getting us into the mess and they now claim they're also going to get us out of it as well. Maybe the universities are all part of this. **Sean**: I mean, what better way than to sell a service, right, is to create the problem to which only you can solve. And also, Bryan, I think I heard in there, too, that I think raises are in order for all faculty, I think. **Bryan**: Yeah, I think so. I mean, the universal basic income will be coming, I guess, and faculty will get a piece of that. But, you know, being able to think, I think if the AI does its job, we'll need to double down and triple down on finding more creative ways to harness our human intelligence. And I think that's going to be the new niche for us human faculty types and us human academics is to fully occupy our role as humans and push that forward. I think in a way, the dawn of the robot age, if you will, is probably the dusk of the robot professor age. So it's calling us to a sort of higher standard of humanity in what we do, which I welcome. I'm excited about. **Andrew**: So just on that, and I'm fully in agreement with you, and it's amazing how many conversations I'm having at the moment around sort of what is special about what we do at universities and how we leverage that. But just that phrase, the dusk of the robot sort of electro whatever. Do you really feel that's the case? Because there seems to be so much pressure at the moment to use AI as a professor substitute, if you like, sort of saying all this learning, we don't need these old fogies in front of students. We just give them an AI and surely that will be better. **Bryan**: Yeah, well, if the robot can do your job for you, you're probably not doing your job that well. I think that's one really important thing to keep in mind. And it applies to a lot of professions, but all the more so for faculty, professors. If you aren't able to really be sort of the tip of the spear of human creativity and thinking, then you're not really fulfilling your role. And I think in a way, our process of scaling higher education has sort of outsourced the scaling task too much to humans. So if anything, a lot of the scalable parts of the enterprise will happily be taken over by AI and thereby free up the humans to do those human things. And a lot of people talk about that. But it's hard to do in some institutions because they've really bought into the kind of the mechanized kind of industrial revolution type paradigm of large batches of product, if you will, not being a student. **Andrew**: I have no idea where that happens. Certainly not at ASU. We're a really small place. **Bryan**: I'm not going to name any institutions in particular. **Sean**: Small school somewhere in the, you know, American Southwest. **Bryan**: Yeah. But it's just too tempting to fall into those patterns. And so I'm really actually quite excited about the challenge of just breaking free from that completely and reclaiming our human connection with students, our communities. **Andrew**: So it's interesting, both you and I are physicists, And of course, you've just come from a physics class. We were talking before and of course, sort of talking about sort of the physical demonstrations and experiments you do. That feels like such a human thing. And we didn't talk about this beforehand, but I'd love for you to talk a little bit more about that. How important you see that hands on stuff, both with the experiments and with the students actually taking them through it. **Bryan**: Yeah, it's amazing because if you think about what it takes to make your class AI proof, which is kind of the first reactions that most faculty have is AI is here. The students are all going to cheat on my assignments. Well, you're not designing your assignments right, first of all. So in the same way, like if the AI can do your assignment for you, you're not doing the right kinds of assignments. So in my case, I love the embodied experiential things, and I have the luxury of teaching at a little college where our maximum class size is 16. So it's actually much more feasible at my school than at a large, gigantic place like ASU. But if you do have the luxury of being in a small class, you know the students. You can take them outside. I was just with them last night at our observatory, and we were sitting around under the stars and looking at the moon and, you know, musing about the formation of the moon. And in class today, I had two students playing around with this bicycle wheel demonstrating precession of the Earth. And just that physicality, that embodied experience is, you know, eternal. It's irreplaceable. And it's also just good teaching. You know, you learn when you do things. And so shifting away from the kind of content delivery and lecture and having students return back content in the form of papers, and instead having actual projects that are tied into some personal experience, some personal only the student would know kind of locales and memories and connections to their past, their identity. These things strengthen learning, and they're naturally AI-proof as well. So making it personal, making it local, making it experiential, I'd say those are all fantastic, and they're good for the students, and they're good for the faculty, but they don't scale particularly well. That's the only problem. So that's the big challenge is how do you maintain the scale? You can certainly go with batches of 16 and do all these things, but how do you then create little squadrons of – **Andrew**: But here's an interesting question because, I mean, higher education, especially in the U.S., is obsessed with scaling. I mean, if you're teaching 16, why not 160? If 160, why not 1600 and so on and so forth? But it strikes me that we maybe are thinking about this in the wrong way. We're just thinking purely in terms of efficiency. But if you're talking in terms of formation, the way you scale is to have more people teaching small groups of 16 rather than one person in charge of sort of 1,600 or 16,000. **Bryan**: Yeah, absolutely. So you create kind of hierarchical trees of sorts between, you know, advanced students and sort of mid-level kind of personnel who may not have full PhD levels of training, but who can handle squadrons of TAs. And you could imagine like these sort of trees of collaborating, teaching fellows or something, forming that kind of personal connection led by a human who has all the advanced training. Or you can even sort of think differently about the whole profession. And that gets us right back to the beginning. What does it mean to be a faculty member? What does it mean to teach? What are the things that rely entirely on the level of training and experience, that sort of vast amount of training that your average PhD faculty has? And what things could be done by just sort of an advanced person kind of in the field or someone, an alumni who's out there? This could be a great opportunity to broaden the community by bringing in professionals and mid-level people from all walks of life and creating an extended campus through these new types of arrangements. **Andrew**: Yeah. **Bryan**: So if we're creative, we could find ways to make it more personal and then also use the AI for the things that do scale. So you could imagine like a number of tutorials and kind of basic kind of content receiving and testing type functions being done by the AI, but then really focus on having high quality experiences in there and really making space for that. Yeah. **Sean**: Yeah. Yeah. Well, Bryan, that's really interesting, too. And one thing that I found interesting as well is, you know, between the three of us here, we've all, you know, Bryan, with you and your book, Andrew, you've just come back from a trip to the UK where you gave a talk around challenging, you know, the university model in the age of AI. I just gave a keynote last week under a similar frame. And one of the things that was, I think, kind of interesting there, and, you know, Bryan, I think this kind of gets at a little bit what you were talking about is, you know, so much traditionally we look at how do we assess that learning has taken place? And that focus has typically often been on the output artifact, right? But yet we know that that's sort of like the iceberg model where that artifact, you know, has it ever been a great, you know, representation of true learning? You could argue that, but really that what we're often not seeing or not assessing is that friction, right? That friction of the learning process, the messiness that happens in there where you actually feel the growth and the expression and all those kinds of things come together. But that raises this big challenges, like these universities and just education more broadly than that has been developed basically looking at that artifact output as the means for the assessment. And so while we're also thinking about holding that in one hand, while also this idea that, you know, AI tools will 10x, you know, fill in the blank, the question then becomes like, what to your point, right? What do you 10x? Are you 10xing the assignment? Are you 10xing your ability to find different modalities? And then how on earth does the organization as a whole change in such a way where it provides the value there as well so that we can be encouraged to do that? Because one thing that I'm curious about as well is as our students continue to come to these universities, right? Like how has, you know, living in a world transformed by AI already changed their experience to which the university isn't even aware or isn't even prepared to handle how these new generations of students will be coming in armed with something we've talked about on our show in the past, this invisible upgrade of this, like what they can do with these tools that you can't see. That's not part of that, creating those like traditional essays and exams and things like that, which, you know, the AI tools themselves can do so well. But what we want to get at is the human part, the learning, the productive struggle, the friction, whatever you want to call it. And so I'm like, you know, so I want you to solve this problem for me, Bryan. Just give me the one. You've got it, right? You're sitting on it. **Bryan**: I know it. Well, you're getting at a good point here, though, which I think, again, is sort of the upside of the AI. If you see it as the job killer or whatever, or see it as the killer of thinking, you're only seeing the downsides. And what it is doing is it's making extinct a lot of patterns that were unnecessarily mechanistic just by virtue of our inability to scale things and our lack of creativity, really, in fully mobilizing the creativity and talents of faculty and students. If you think about a group of students in a room with a faculty member, this new technology is putting everyone in some ways on the same footing. So you immediately have broken down the hierarchy. You've immediately broken down the inevitability of the instruction where the poor faculty member is learning alongside of their students. And that can be either seen as magical and wonderful or terrifying, depending on the personality of the faculty member. So immediately, once you get rid of your illusion of omniscience, which many faculty members have cultivated over many decades, and really recognize that this is instead an opportunity to deeply collaborate with students in a way you may never have in the past, you've opened up an entirely transformative kind of learning, which actually can scale, because now you can empower students to take on tasks that would have been impossible for them to do a few years ago. So they become research associates alongside of you with the sort of turbocharged engine for zooming through information space in a way that was absolutely impossible. So I'm coming up with an image of a bunch of people on jet skis zooming around on the water instead of just swimming with some of the students barely keeping their heads above water. **Andrew**: But that feels like a very different model of AI use than I hear from a lot of places. A lot of places are trying to sort of formalize it, teaching students how to write the perfect prompt, for instance, which drives me around the bend. But now you're basically sort of saying sort of let the students sort of play and learn and develop their skills around these tools, and together see how you can sort of go faster and explore more. **Bryan**: Imagine that, a room where everyone is learning together and it actually goes places where you didn't even expect it could go. That is what you might call serendipity. That is what you might call spontaneity. Again, which depending on the personality of the instructor is either terrifying or magical. **Andrew**: Right. So then as you're going back to Sean's question, what do you do with the terrified instructor who is only thinking about how do I evaluate this? How do I give them a test that finds out what they've learned? What is the 90 degree shift in thinking there? **Bryan**: Yeah, it requires them to actually talk to the student. Well, you know, that's dead in the water, I'm afraid. Some have resorted to that barbaric task of reinstituting blue books. You have to do longhand. And that's triply cruel to the students because they never write by hand for anything ever. Whereas when we, you know, we're old enough to remember those dark days. But we were actually writing love letters by hand. **Andrew**: Can I just say, so this is a very US thing. I mean, coming from the UK, I've only been hearing about this blue book thing over the last year or so. **Sean**: And we probably should explain what the blue book is. **Andrew**: Please do, because I used to do exams right by hand, but we never used to call it blue book. It was just doing exams. **Sean**: Yeah. Yeah. Well, and I feel like I was probably the last, well, I don't know how accurate this is, but I feel like I was like the last generation in the US when I, because I remember I was there. So the start of my undergraduate career, it was blue books for the exams until about my junior year and then it switched. But yeah, but yeah, the blue books, you used to buy them at the bookstore and they were literally just these like, it was just paper with a blue, you know, like a folder, a blue folder on the outside that you could write down your, you know, what class it was for and you'd buy a stack of them. And they were, it was like the official allotted like notebook paper that you would write your exam questions on. So yeah, and then you'd sit there in these gigantic lecture halls. And you'd hear, it would be like, just like in the movies, if you imagine it, right? When they're like, all right, you may begin. And you hear everyone flip open the book at the same time, which there is something nostalgic. I miss that sound because to me, you know, cause that, that's how you, that's what was the lived experience. And so I'm like, I do miss that, that like, you know, a hundred people going with the book and opening it, you know? **Bryan**: And it is, you know, it is a valid way to test an individual's performance, but it's unfortunately a very isolating and very narrow channel of performance so the degree to which you can sit in your own in a giant room all by yourself scratching out facts and figures and or writing a brilliant essay in a blue book it's it's assessing a certain kind of skill but is that really the skill that's needed in the 21st century do you need people who can scratch out a full kind of encyclopedic account of Thucydides' treatment of the Peloponnesian War from memory? Or is it better to have people be able to work with others, with other human beings, be able to communicate clearly, either verbally or in video or in other media, and to be able to organize and invent things that are truly original? So I would say the latter. And I think that's where we need to go. A lot of our traditions have been sort of sanctified, mostly, again, by faculty persons, overinflated, nostalgic memories of the golden past. **Andrew**: I agree. So it's so interesting listening to this. So growing up in the UK, I graduated high school in 1984 and then had my three year degree and everything was done on the basis of end of year exams, written exams. And it suited me down to the ground. I loved it and I did OK. But at the same time in the UK, there was this massive debate about how this privileged a very small number of people who were good at it and disadvantaged everybody else. And so back in the 1980s, the UK was already moving towards alternative forms of assessment and continuous assessment and continuous learning. And I feel that was the 1980s. I feel as if we're going completely roundabout now and getting back to where we were all those years ago. **Sean**: Well, it's that full circle, right? They're flummoxed with like, we can't figure out, we have to go back where the AI can't go, which is like, you know, sit in a room. And then I will say this. This was always the nightmare of those blue book things is while you're sitting there working and you're stumped or you have to sit there and think, there's always that kid next to you who was flying. **Andrew**: And then you've got your name on the top of the page and they've got about 10 pages. **Sean**: You're like, oh, what was this? And then the worst is like half an hour in that one student gets up and hands theirs in with the full confidence and you just sink. And you're like, I am doomed. So, you know. But if I experience that, why should my students escape that same level of academic torture? **Bryan**: Yeah. Yeah. So we perpetuate trauma and sort of dress it in kind of a fake glory as if it were some kind of brilliant rite of passage. And it was really just our faculty that we took classes from who couldn't think of anything better because the guy before them did the same thing. I mean, there is, again, virtue to being excellent in that type of competition. But in a way, it's just one event. If it's the Olympics, you become then a really good pole vaulter. And we produce squadrons of pole vaulters with that kind of teaching. I think what's going to be exciting about this new era is people are going to really have to experiment. And you'll get such a great differentiation and almost like a Cambrian explosion of sorts of new types of assessments, new formats for class, new innovative, inventive ways of mobilizing the students. And I'm super excited about it. I think it's going to be amazing. **Andrew**: Can I just ask you about that, and specifically what you're doing? Because again, I very much agree with you. And yet I see in universities, even though they conceptually get this, they feel as if they've got to formalize this through policy. And my sense is you can't do this top down. You can't tell faculty exactly how they're going to be creative and how they're not going to be creative and what they're going to do and what they're not going to do. But I don't know whether I'm on an island of my own there. **Bryan**: I think you're right there, Andrew. And I think, you know, again, it requires people and administrators are also complicit in all of this to let go a little bit of their omniscience and their control. And, you know, if the university is a creative community, if it is a group of brilliant scholars all kind of co-located, now is the time to really tap into that. We need it more than ever. These scholars should not just be repositories of past knowledge, but they have to step up and help us get through this. I've been thinking about it. Everyone's looking around saying, oh, my gosh, what's happening? The robots are going to kill all of us. Who can help us? And the AI companies are saying, hey, please regulate us because we're about to kill everyone. **Andrew**: Stop us because we can't stop us. **Bryan**: And the government's saying, well, we can't possibly regulate you because it will destroy our competition with China. And everyone's saying, what do we do? What will we do? Well, guess what? There are all these really smart people at these places called universities who know stuff and who think deeply about the human condition and who can offer a lot in getting us out of this. And if nothing else, just providing prescriptions for how to use the AI because it's going to come no matter what. And then to think also about collaborating with the government and with the labs to actually shape it. So it can then be trained in new directions that are actually more useful. You know, if you just hand it off to the industrial leaders and the political leaders, well, you can see what's happening. **Andrew**: It's actually so it's a great case study and how not to do this. And especially, I'm so glad you said it, not me, the fact that we've got these universities with all these brilliant people in who have had years, decades of experience of thinking through these problems and thinking through how you navigate them. And yet how easy it is for us to be forgotten. So that sounds awfully self-serving. **Bryan**: That was actually part of the reason why I wrote this book, because I was looking to find answers. And it wasn't just about the AI, but the AI was a big part of it. There were so many threats to education, all kind of converging. And in the same way that people talk about a singularity, you know, as being a convergence of exponential technologies, in a way, the universities were approaching a singularity or a cliff of converging threats between the demographics, the flagging political support, and so forth. And we needed to find answers urgently. So I went around the world, you know, looking at different universities, trying to find answers. And there are a lot of answers just in the side of AI, of schools that are really stepping up and doing things. And, you know, ASU being one of them, because, Andrew, I went over there. **Andrew**: Yep, I remember our conversation. **Bryan**: Yep. Lots of great stuff happening there. And there's lots of things happening, you know, all these different centers that are around in different universities. Of course, the Stanford Human-Centered AI Institute, and Carnegie Mellon has a great center for ethical use of AI. There's all these different examples that are popping up, and new universities too that are built, you know, centered on AI to take advantage of it. So the book kind of goes into it. There's a big chapter of centers for AI use around the country that are doing interesting things, policies for AI that are emerging, and then there's another section on new universities that have been started that are kind of AI-centered or AI-forward. And so I'm also working with a group that's called the Future Universities Alliance. And so we're meeting on October 2nd. My co-author, Noah Pickus, has organized this out of Duke. And so we've got this group of about 80 brand-new universities that have all been competing to be part of this group, this cohort. And they're getting together with established universities to work out new policies and new ways of managing AI and doing other things. **Andrew**: Yeah, yeah. So I've got to get the book and read it even more now because this is exactly an area that I've been interested in. But I'm interested to know whether you deal with the scaling challenge because it strikes me, even from what we've just been saying, That if you take this assumption that we have to be sort of scaling 10 times, 20 times within universities, the challenge of AI just collapses into something which is actually tractable and exciting. **Bryan**: Yeah, so that's something I have a whole chapter in the book on massively scaled universities. And so I put ASU in that category, too. **Andrew**: Just before we hit 200,000 students. **Sean**: I was going to say some clarification to our global audience because ASU has ticked the threshold of 200,000 students. **Bryan**: Yeah. So you guys, and I have a big section on ASU, a big section on Western Governors University, a big section on Southern New Hampshire University. And I spent time visiting all those places during a sabbatical and met with their leaders and got a really great feel for how it works and how it's possible to scale. Because for me, remember, I come from a small liberal arts college. I'm teaching 16, not 200,000 students. So I first wandered into this meeting. It was called the ASU+GSV meeting, which I think you guys go to. Sean knows it well. And because I'm an astronomer, for me, it's kind of like passing into a different universe because the liberal arts college and the ASU+GSV meeting are about as different a group as you can imagine. But I love crossing into new cultures and learning about other things. So I wandered into the Western Governors University reception. And again, remember, I'm from a small liberal arts college. I said, hey, who are you guys? What is Western Governors University? And they looked right back at me and they said, what the heck is Soka University of America? And we talked to each other. And I said, wait, you have how many faculty and they're where? They're all over the country. And we have like, you know, tens of thousands of faculty and we have coaches and we have success coaches and we have and they go on to all the different pieces of it. And I was just fascinated by it because I think there's something that these two universes can learn from each other. And so that was part of the reason why I was trying to get this book together, because everyone's sort of in their own corners, like the massively scaled people. They never talk to people like me from a tiny college. But I was fascinated. And, you know, vice versa. There's so much that we in little places can learn from their focus on these success coaches, these coaching aspect of how you really stick with a student from the start, help them through all the different challenges, not just the coursework. And you have a centralized, streamlined, advanced technology supported system for managing those students. I mean, that's a fantastic thing. And a lot of big UC type campuses, large state universities have been doing this. But these large scale places have just really got a perfect kind of recipe for the managing the whole student, the customer service model, which Paul LeBlanc is very proud of, a culture of caring, that they're able to scale a culture of caring to 200,000 students. It's fantastic. And so there's a lot to learn from that. And so I think that's been a really surprising thing. And I think all the little corners of academia have a piece of the puzzle. So it's just they don't talk enough to each other. **Sean**: Yeah, I was going to jump in there because what's interesting, Bryan, and I would love to hear also just from your experience researching for the book your experience going around to different universities and having a glimpse at different models and different, different centers. You know, when we were talking just before the, before you went to the GSV, I similarly, I came from a liberal arts school as well at one point, and then was right into GSV after that. And it is, it's like glitz and glamour. You're like, you know, it's like country mouse finally made it to the city sort of a scenario. And you're like, my suit jacket's not expensive enough to be in here. I'm, I stick out like crazy. But so what I'm curious about as well is like one, just from the experience of doing that background research and talking to these different groups, I think you've already done a good job of articulating that there are these pockets in places that are doing really interesting things and are very innovative and are maybe even taking this challenge head on going, well, we can use this to, again, 10X what? We can 10x things of care and of intent. But there's this constant sort of, you know, friction that is at play there. While those groups are pushing and moving, broadly speaking, I think, you know, the larger universities, it's, you know, whatever metaphor you want to use, it's hard to turn a giant ship when, you know, that they're still rewarding the older traditional sort of mechanisms for promotion, for how they measure success, how we do those kind of things. And something I would be curious about is from your perspective of this, you know, in this unfolding in the age of AI, is this different than how this has been done in the past? For example, you know, rewind the clock 25, 30 years when it was moving forward, when everything was becoming, you know, internet web based or sort of moving in that sense. Because we've, you know, same kind of patterns of disruption and changing how we do things. And, you know, in the early 2000s, you know, online learning was the death of education and everything was going to be ruined. Right. So we've had those conversations before. And I'd be curious from your sense, is the institutional response, is that what are we feeling the like the outmoded response? Because they're still like not moving in a way that responds to the accelerant speed of this sort of like AI age. **Andrew**: They're still 2000s universities with a veneer of 2026. **Sean**: Exactly. Right. Or one unit that's 2026 and everyone else is like, yeah, but those are the weird guys out there. And we're just going to that'll pass. That'll pass. **Bryan**: Well, you know, there is a virtue and, you know, the universities will remind you this instantly in their ability to retain and be sort of curators of our culture. And that's a definite thing that you don't want to lose. But at the same time, lots of metaphors come to mind. The first one is a giant asteroid heading at Earth and kind of a mass extinction event of sorts where the climate is utterly different now. And it's not a matter of adaptation, I think, at the moment. I think it's a matter of massive, massive all-hands responses needed to respond to what amounts to a phase change in how information and how knowledge is transmitted and produced. And if there is not such a response, if it is just sort of rearranging the chairs on the top deck of our giant cruise ship, we may be more Titanic and less a fun, happy trip to the Bahamas, because the students aren't going to wait around for the university to change. **Andrew**: And of course, talking about massive meteors heading towards the Earth, you know what happened last time a really big one landed. **Bryan**: The large old animals died. Sure, and it was a great time for us mammals, wasn't it? The small liberal arts colleges are going to come out and evolve. Well, I don't want to push that analogy too far, but I do think that being small and being responsive is a virtue in this time. And if your institution can't be small, thinking as if you're small and agile or finding ways to mobilize in creative, you know, smaller units to have a differentiated response and distributed innovation, which a lot of schools are doing. And, you know, I talk about it in the book and I've seen it in different campuses. There's just some fantastic innovation happening, generally not from your very top universities either who are living in very great amounts of social prestige and wealth. So for them, you know, the climate hasn't really changed so much. But a lot of schools in the middle and on the bottom end of the prestige hierarchy are really paddling fast to try to respond. And a lot of it's actually quite creative and interesting to see. So it's an interesting time. That's what they always say: may you live in interesting times. A blessing and a curse, right? **Andrew**: But if I can just pick up on this idea of distributed innovation because that feels important. And it reminds me of what happened with IBM when they nearly went under all those years, decades ago. And the transformation there was going from a top-down, top-heavy organization to one which was far more distributed in terms of how decisions were made and how agility was baked in. And from what you're saying, it feels like we're at a similar sort of moment. So you can be big, but you've got to work out, as you say, how to think small within that bigness. **Bryan**: Yeah, it's really a matter of reinventing. It's both kind of focusing on a mission and being differentiated from others. So I spent a lot of time learning how to think like an MBA for this book. So I learned all the vocabulary: the disruptive innovation, yes, but also Porter's strategic forces, and I read about Drucker and all kinds of others about how to manage. I don't really know how to do these things because I'm an academic. But I know there are people out there who have spent their whole careers and gotten fantastically wealthy managing giant corporations. And they don't do it by offering the same product everyone else does, by not changing when the market is changing, and by not reconsidering their whole business model when everything is utterly shifted under their feet. And so that's really not something that companies do. And universities can learn a lot from them, But again, just like large schools and small schools, they tend not to talk to each other. So not to say that the corporate guys can just step in to a university and figure it out. Because I talk about in the book about how the inefficiencies of universities are actually not a bug but a feature. The fact that you have all these fantastically diverse and disconnected fields of people that just run into each other randomly and then make these improbable connections is part of the beauty. And the fact that you have students actually in place, interacting with each other and having midnight conversations that seem completely unrelated to anything toward their mechanical engineering degree, that's actually, again, that's where their lives change. That's what they remember. So, you know, having kind of the chance to learn from some of the ways that companies are able to focus on their, if not mission, their product. They produce a product. But universities, in a similar way, they produce a product, but they need to be identifiable and differentiated and not just replicating what everyone else is doing. And so they simultaneously are trying to work on what are irreconcilable and divergent missions that include vocational training, graduate education, social mobility, athletics, and high-level cutting-edge research. How do you do all that in the same firm, if you will? **Andrew**: Right, right, right. And presumably, how do you get the synergies between them as well? **Bryan**: How do you do that? I will say that in many ways, you just can't. And so in many ways, this model has to be broken down, disaggregated. You can create units, though, that do each of those things really well. And so part of the book also talks about models for new universities, which could be more focused on particular missions, maybe just the teaching mission. And here, I think what Sanjay Sarma is doing with the MIT New Educational Institution is super interesting, creating these very high-level MIT-like institutions, but focused squarely on the teaching mission with an interface, to use an MIT kind of term, between the teaching and the research by way of these practitioners who come in for various durations. And by way of sabbaticals, where the faculty at the teaching unit can go into the large mothership of MIT and get the full dose of all that brilliance, but then bring that back out into the classroom. And that's the kind of efficiency that scales, too, because you can create 5, 10, 20 of these NEIs for the cost of one MIT. And you're focused squarely on the students, and you're not trying to do everything at once. So I think the answer will come from this kind of unbundling and differentiation that will happen, I think. Again, the asteroid will make change much more possible. **Sean**: Yeah. Well, Bryan, it's interesting, too. So one of the things that, you know, as I think about how you're sort of describing that, I think something that sort of resonates that I find kind of exciting is, you know, again, sort of holding that idea of like scale by itself doesn't equate success in the model that we like going forward. Even if you can say it was successful, I think that that is like the flag, whatever the mark, you know, I've been using this term a lot and I really like it. So I want to wedge it in here. It's been marked for termination, right? Like it won't be successful potentially in the future, given this age of AI. But the part that sounds really exciting is finding a way, again, it's that question of like leveraging these technologies to scale what? Like how, which pieces are you, are you leveraging to create those new affordances from those technologies? And the part that gets me really excited about sort of that way of thinking is a new way of scaling that allows you to sort of collapse the distance between the learner and the faculty and what there's, again, that friction of going through the learning process. And, you know, because I feel like as maybe a byproduct of the traditional models of scaling is that you get further and further away from the student, right? They become less and less of an individual and they become more and more of a number because of the factors of scale. And so how then leveraging this age of AI and these affordances that these tools on both sides of the equation will bring is that how do we collapse that distance even at numbers scale of humans, but how do we collapse that distance into an experience that is exactly that? Because I think, you know, broad brush, as broad as you can get, our mission is to create critical thinkers that can go off into the world and do amazing things that haven't been done before. **Bryan**: I love that scaling argument. **Sean**: Yeah, and we get lost in the numbers, but it's like, how do we collapse that down to something? **Bryan**: Like we were geeking out about physics earlier, it's sort of like an inverse square law of sorts in terms of gravity, in terms of learning, and the farther away you are from each other, the weaker of learning is. **Sean**: And the more likely you are to be attracted to something with a greater force, right? So you can literally steal you out of orbit into something else. **Bryan**: Yeah. So it's a product of factors. It's a product of the learning, but also the coupling constant between you and the learner and bringing people close, building communities, building experiences. Those all increase that coupling. So you don't even need as much of a source, if you will. And as much as I hate the knowledge of it, the idea of a faculty member as a source of knowledge, but that's often used as a paradigm. And so in that way, if you look at the typical lecture hall, it definitely has an inverse square law because there's a faculty member in the middle and then the students close are learning stuff. And the ones in the 25th row are looking at their phones. **Andrew**: So I've got to see whether you remember this, because this is reminding me of how Richard Feynman used to teach. And you sort of have the stories where you had the sort of four or five students at the front of the class that he engaged with deeply. And as you went back through the class, no engagement whatsoever. **Bryan**: Yeah. Apparently his lecture series, which are beloved by physicists everywhere, physics faculty, came out of a course where the students didn't get it at all. It was a failure as far as the students are concerned, but the faculty were just thrilled by it. You know, this says so much about academia, patting ourselves on the back. **Andrew**: But can I just ask you on this? Because this is sort of resonating with me, this idea of connection and connection at scale. And I want to try this out and see whether you came across any institutions that are thinking in this way. So we are at ASU in supposedly a progressive university, but still when it comes to teaching, we're rather sort of conservative in that as senior faculty or as faculty, we have a three or four class load. Each one is three credits. So that's three contact hours a week. And you find faculty who are deep researchers, deep experts in their area. They want to go into class and they want to enthuse students. They want to spark that wonder in students. They don't necessarily want to spend hour upon hour upon hour of their precious time going through the basics. And yet with the model, we have to do that. We have to fulfill our large teaching sort of load by going through the minutiae. And it strikes me that the scaling model is dump all that sort of mechanistic learning onto something else or somebody else and allow the faculty to do what they're really good at rather than having to work out how on earth to fill a three credit hour course with stuff that feels like busy work, allow them to use that time to really connect with students and enthuse them. I mean, is that a model that anybody is exploring? **Bryan**: I mean, I totally agree that the deployment of the faculty is a disservice to the student and the faculty. You're a Nobel laureate kind of, you know, teaching how to draw vectors, or biostatistics 101. Although I had some good, you know, I went to a school that had a couple of Nobel Prize guys who liked to teach, which is great. And it was kind of exciting to see them. But at the same time, I think their time would have been better used to give a smaller but more intense dose. And this gets back to the inverse square law thing, where you have close relationships for a short while with small squadrons of students or small, very creative interventions with sort of delegates who can then run off, as you're saying. So they can be used as a designer of some kind, some exercise that would be brilliant, but then would be implemented by others. So you create these kind of chains. Now, obviously, faculty have been thinking about this for a long time. So they've invented graduate TAs and all this stuff. And that's, in a way, part of the idea. But those people tend to be contingent and tend to be inexperienced and tend to run out and go in different directions. So you don't develop institutional knowledge. So, in fact, like creating new roles for faculty, I think, are super important and sort of mid-level types of faculty. And that's, yeah, I've seen a lot of models like that that are taking root right now. One of the fun examples that we wrote about actually in an earlier book called The New Global Universities, that I wrote with Noah Pickus. It talks about ALX, which is a school in Africa. And they couldn't afford, there weren't enough PhDs. They had an ambition to train 3 million people across Africa, but there just weren't enough PhDs available. So they basically delegated people who are entrepreneurs to be faculty, and they created a curriculum, and they allowed these people to run clinics. And the clinics then were experiential and entrepreneurial and high engagement, intense communities of people, but they were led by and connected through a more elaborately developed curriculum. And in many ways, some of the other schools that I've written about, like Minerva University has a similar kind of curriculum designed by, you know, a centralized set of brilliant people. Western Governors University has differentiated roles for faculty. So there's whole teams of faculty that design a course instead of just each faculty kind of in their garage, their own version of, you know, if you were designing cars this way, we would still be before the Model T. We'd all have 1890s Mercedes. **Andrew**: But I've got to say, so I see some of these models, but I feel a lot of the models that I see sort of move away from the faculty. So yes, you've got the sort of faculty centric model, which doesn't work because everybody is sort of inventing their own thing. And the knee-jerk reaction is to get rid of the faculty and just go to a transmission model where you can plug anybody into that. And surely the sweet spot is you use those expensive faculty for what they're brilliant at and not waste their time for the things that they're not really adept at. **Bryan**: Well, so there, you know, the organizing metaphor has to be right. So if it is indeed the idea that you're creating a bunch of people who are going to deliver content, then you're automatically on the wrong track. Right. What you're instead doing is creating learning environments. So if you could then have your top level talent sort of connecting to the emerging, most urgent questions of their field and finding creative ways to find accessible pieces of that that you could bring into classrooms of undergraduates and then implement a set of projects that, you know, people of mid-level, graduate students, recent graduates, recent PhDs could then offer as sort of templates for students to begin to experiment and debate and create. And then those would then more be process-based than content-based. So it'd be more like you're writing a script of how, with like little branch points, like if student does this, then do that, if class is stuck here. So you have to create a more dynamic learning environment that is not static and that's not predictable. And I think throughout all of this that we're talking about, what's happened with AI, which is so exciting, and this revolution that's going to happen, which is again exciting, is liberating us from the mechanistic, predictable, deterministic A, B, C, the march of thousands along the inevitable path to the degree. Which is a ritualized thing that we've, you know, it's had a great run for thousands of years. **Andrew**: Now it's time for a bit of change, yeah. But actually, I'm loving, I don't know whether this is exactly what you're meaning, but I'm loving the idea of sort of taking my sort of my three credit hour course, sort of three contact hours a week, and basically sort of saying to the students, go off and do the sort of stuff that you need to do with AI. And when we come together, we're going to have fun. We're going to create a playground of a learning space, a learning environment that is going to spark those ideas in you. And then you can go off and sort of build on that with the AI. So it's a version of the flipped classroom. **Bryan**: Yeah. You're making the classroom more of a studio space. Not a place where you deliver knowledge. It's a place where people work together and create. And that's sort of how workplaces are now, too, by the way. So it's better training for the students to to be in that more spontaneous kind of rapidly changing and evolving kind of space. **Sean**: Yeah. No, I really like that because, again, it shifts the focus, right, of that time together is essentially it's community, right? It's community and with this important layer of, again, you know, I think part of the responsibility of the faculty, right, is to also demonstrate to students what does a profession of knowledge look like, you know, and moving away from, which I think everyone for the most part would, pretty much agree, you know, that the model of just information transmission of your time together is not well, not time used well, right? You know, could you imagine having Feynman as your instructor and you're just like, wait, what was that? And all you're doing is taking notes on what he's saying rather than engaging. **Bryan**: And I have stories about that. **Sean**: Oh, yeah, please. **Bryan**: I mean, the more general point is if you're teaching science, which is what I do, and you show up with this air of omniscience and we know everything, you're both misrepresenting the scientist and science. Because ultimately, a good scientist is humble, and science itself is confronting the unknown. So the sooner you can get students to a place where they recognize that's what science is. And so I always use this metaphor, which is maybe a little corny, but I think it's beautiful, which is the island, and this is coming from this fellow named Sockman, a 19th-century educator: the larger the island of knowledge, the greater the shoreline of wonder. That is, if you get students to that coastline where they actually see that science doesn't know, they're pushing off into the unknown. And that's what's exciting about it. So if you can somehow transport that into your class, you're doing a better service to the students by really recognizing how dynamic and how challenging and how humbling it is to be in science and in all fields of knowledge. So right away, by adopting a different stance as a faculty member, you can then break down some of the misconceptions about science and about knowledge. And students will realize, wow, they don't know everything. And I don't either. So I can go right alongside and contribute. So immediately you've solved a lot of the crushing kind of self-esteem problems that a lot of students have with these. But then the other part, I wanted to tell a story because I'm old enough to have actually met Richard Feynman. I was a student for a year at the University of Alaska Fairbanks, where I got tired of Stanford for a variety of reasons. And I decided to go as far away as possible. So I got on a bus and landed at Fairbanks, Alaska, where I was part of the proud group of the Society of Physics students. And we just on a whim invited Richard Feynman up to Alaska and he came. We had him for about two weeks and I met him at the airport. And this actually blew my mind, but not for the reason that I thought, because he got off the airplane and he just started peppering us with questions. He was asking us, well, why are the glaciers shaped like that? What is this area over here? And he was asking us all these things. And so that idea of question, question, question, it made a deep impression that even this guy who's like the smartest human to ever walk the earth possibly is asking questions all the time. And he's not high on himself either. He's just accessible and down to earth and sort of his mind is alive and open. And so I sort of see, okay, here's another principle where you have sort of in physics, you have the idea of emergence, where small little units have a certain pattern and it sort of self replicates in fractal like ways to larger structures. That's why solar systems and galaxies look alike. Well, in the same way, like a really brilliant scholar is open, is engaging, is always questioning and is reaching out to others without this sort of big reserve of I'm important and you aren't. And so in that way, they learn, everyone around them gets smarter, the world gets smarter. And the people around them sort of set a culture together, which can also similarly reach out. And a big part of what I've realized in doing this book is that our universities are too cloistered. So just like an individual who's kind of always having a big wall, like I know everything, I think the universities have similarly kind of created this impregnable kind of bastion of wisdom type reputation when they should be reaching out not just to deliver things to communities, but to bring the communities in. And so I've seen lots of schools talking about, you know, the idea of being a porous university. Yale wrote their new report on establishing trust. One of the key elements is opening the gates. And so they're finally getting the idea that they need to be responsive and connected to their communities. And I think, again, with this big asteroid coming at us, this is the greatest time where not only do we do this, we have to do this. We need the public support and the public needs us. **Sean**: Yeah, well, that's awesome, Bryan, because I feel like it's also this method of in this particular time, right, that not pretending that you know the answer and being open and saying we don't know the answer is not a demonstration of weakness, that that in itself is the demonstration of the strength, right? That you are openly saying, we don't have the answer. We should find it together and not this sort of false, like, well, if we don't say anything, we'll keep it private till we get our stuff in order. And then we'll tell the world that we've solved the problem. So we don't look foolish for not knowing, given that we should based on the history. **Andrew**: No, it doesn't work anymore. And just as we wrap up a beautiful anecdote that fits in with that, Bryan, last week, I was at the University of Edinburgh at the Edinburgh Futures Institute. And the institute is occupying a newly refurbished building. It was finished a year or so ago, but it used to be the Old Royal Infirmary in Edinburgh. So it had been there for 100 plus years. It was embedded in the community. People remembered it. People had had their kids in there that had their bones set in there. So when they came to refurbish it, they had this very, very clear sense that this has to be part of the community. And as a result, it's a beautiful building, but it is fully open to the public. Anybody can wander into that building, sit themselves down, sort of use the space, whether you're a business person, whether you're a mother with kids. And when I was there, you had mothers and kids in prams sort of walking through. And to me, it was the perfect example of being porous in a very, very practical sort of physical way. Just basically sort of saying our space is your space. Sort of come and join us and be part of the community. **Bryan**: That's so cool. And yet at our campuses, that's usually not something that is planned. There's no part of the campus that is dedicated to that. They certainly want money from the outside. And they provide the sports outside. But so one of the things like in the sort of the book, I come up with this sort of future university idea where you kind of create little units that are squarely focused on research, much like the Carnegie Research Institutes that are just research. But they're connected by way of interfaces to the teaching side. And these are little teaching units that are squarely focused on teaching. And they use the faculty in this way that we sort of discussed as coaches, as guides, as mentors, as guest lectures. And then they also provide the sort of connection to the state of the field. But then the other piece of the puzzle is something that I cooked up this term integrative institute, which is that every campus should have a common space like this where they regularly invite the public to contribute, to share their ideas, their questions, and not just kind of we're giving to you, but we are wanting you to come in. We can learn from you. We can grow with your partnerships. And, you know, again, campuses often have centers for community partnerships, but they're very focused on... yeah, formalized. To really sort of recognize that status of equality, which I think is another theme here of this conversation, breaking down these hierarchies, which AI is naturally going to do because we're all learning together, is just better for everyone. And I think long overdue. And I think when faculty begin to learn alongside of students more, when universities start to learn alongside their communities, I think everyone will be stronger for it. And I think it's exciting to see that happening. **Andrew**: Yeah. That sounds like a great place to wrap up. Wonderful. Bryan, you've done our job for us. You've landed the airplane, my friend. **Bryan**: Thank you. That's the hardest part of flying, too. **Sean**: Right, right, right. Oh, no, that's absolutely fantastic. So Dr. Bryan Penprase, thank you again. And for our listeners out there, again, don't forget to look up his new book, Future-Proofing the University: Innovations in Learning, Technology, and Finances in the Age of AI. I'll put a link to not only to Bryan's profile, but also a link to the book so you can check it out there as well. But no, absolutely fantastic. Thank you so much, Bryan, for joining us on Modem Futura. **Bryan**: Yeah, great. It's been a lot of fun. Thanks, Sean. Thanks, Andrew. Great talking. **Sean**: Fantastic. All right. So yeah, and again, for our listeners, we'll drop some information in the show notes, but also if you do us a favor, give us a like, rating, review, share with your friends, share with that person you know needs to hear the amazing sparkling conversation that you just listened to today. We would be much grateful. And Andrew, where else can people go if they want to stay connected? **Andrew**: Well, you can actually Google us or ask your AI about us and it will direct you to where you can find us. Or you can go to modemfutura.com/signup and it gives you the opportunity just to get a weekly email when a new episode is dropped. **Sean**: Yeah, and I'll throw in one other plug. Also, if you are enjoying playing with your favorite AI tool, throw it over to beinghuman.fyi and interrogate all you want to about Modem Futura, Andrew, me, what are we up to? And all kinds of the fun stuff. I think it's been a lot of fun. We've heard from some community members who have been doing that and it is quite fun to see what people are coming up with. So fantastic. Again, thanks everybody. Thanks Bryan. Thanks Andrew. And we'll see everybody on the next episode of Modem Futura. **Andrew**: We'll see you then. **Announcer**: Demodulating signal. Modem Futura is a production of the Future of Being Human Initiative at Arizona State University. Be sure to subscribe on Apple Podcasts, Spotify, or wherever you listen to your favorite shows. To learn more about the Future of Being Human Initiative and all of our other projects, please visit us on the web at futureofbeinghuman.asu.edu. End transmission. --- *This is the full transcript of an episode of Modem Futura (), a podcast hosted by Sean Leahy and Andrew Maynard and part of the ASU Future of Being Human initiative (). The episode’s show notes and listening links are at . Listen via the links above, or watch it on YouTube ().*