Week in Edtech 5/20/26: AI Backlash Grows, Anthropic & Gates Launch $200M Education Push, MasterClass, Chicago Booth & OpenAI Launch an AI-Native Business Program, and More! Feat. Angel Chung of The Wharton School & David Rogier of MasterClass

Join hosts Ben Kornell and Alex Sarlin as they explore the growing backlash against AI in education, the race to build AI-native learning systems, and the shifting future of edtech, workforce learning, and global education policy.

✨ Episode Highlights:
[00:02:18] Reflections and takeaways from this year’s ASU+GSV Summit
[00:05:16] Gen Z backlash against AI grows at college commencements
[00:08:06] China’s practical AI rollout contrasts with the U.S. race toward AGI
[00:15:09] Anthropic and Gates Foundation launch a $200M AI education partnership
[00:23:02] Debate over the future and business model of AI tutoring
[00:29:25] OpenAI expands its “Education for Countries” initiative
[00:37:28] New education tax credits could shift spending power to families
[00:42:15] Google, Meta, and Apple push AI glasses and XR learning forward
[00:48:40] AI simulations gain traction in workforce training
[00:51:06] Multiverse raises $70M for AI-driven workforce upskilling 

Plus, special guests:
[00:55:51] Angel Chung, PhD Candidate at The Wharton School, on proactive AI tutoring systems and new research showing measurable learning gains for students using adaptive AI guidance
[01:18:08] David Rogier, Founder and CEO of MasterClass, on AI-powered learning, the future of higher education, and MasterClass Executive — developed alongside OpenAI & Chicago Booth to explore the future of AI-native business education.

Learn more here: https://www.masterclass.com/booth-ai

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Transcript

[00:00:00] Alex Sarlin: Tuck Advisors was founded by entrepreneurs who built and sold their own companies. Frustrated by other M&A firms, they created the one they wished they could have hired but couldn't find. One who understands what matters to founders and whose North Star KPI is the percentage of deals closed. If you're thinking of selling your EdTech company or buying one, contact Tuck Advisors now.

[00:00:24] Ben Kornell: It's only a matter of time before AI automated weapons systems are making decisions about human life, period. 

[00:00:31] Alex Sarlin: And that is definitely one of the many scary things about AI that those forty-two percent of, of, of Gen Z is worried about. They're worried about their jobs, but they're also worried about- This is why 

[00:00:40] Ben Kornell: they're booing AI.

[00:00:41] Alex Sarlin: Yeah. I mean, the stories happening in Meta right now where they're basically trying to force thousands of their employees to have AI surveillance on their work so that it can basically train them to do a lot of the work like they do, and the people are rebelling against this. But I mean, that's exactly the kind of autocratic top-down decision that could be made inside a company or can be made inside a country that is extremely dehumanizing, to put it but lightly.

Welcome to EdTech Insiders, the top podcast covering the education technology industry. From funding rounds to impact to AI developments across early childhood, K-12, higher ed, and work, you'll find it all here at EdTech Insiders. 

[00:01:25] Ben Kornell: Remember to subscribe to the pod, check out our newsletter, and also our event calendar.

And to go deeper, check out EdTech Insiders Plus, where you can get premium content, access to our WhatsApp channel, early access to events, and back-channel insights from Alex and Ben. Hope you enjoy today's pod.

Hello, EdTech Insider listeners. We are back. It's old school today. Alex is back and in full effect, y'all. Welcome back, Alex. Week in EdTech has missed you, and we're so excited to talk about everything that we can fit into an hour. I mean, I don't think we're gonna be able to do it, but let's see what we can fit in.

Welcome back. And how was like coming off the high of ASU+GSV? It was just so huge that this year was like none I've ever seen before. 

[00:02:18] Alex Sarlin: Yes. It's so big. So many people there, so many sessions happening, so many events happening. Obviously, being within the GSV ecosystem makes it a different experience.

You're, you're sort of seeing behind the scenes and helping volunteers and doing some, some really important and sort of unseen activities. But it was incredible to see so many people from across the edtech and education and higher ed and workforce ecosystem all in the same place, all at the same time, lots of educators.

It is truly a high. I mean, you walk around, I think anybody who's there, you sort of walk around like aflutter the entire time, and then go back and, and look at all your-- the people you talk to and think about all the follow-ups, and it is-- it's quite an experience. So i-it's been amazing, and then it, it's already been a month, which is crazy, but now we're looking into the summer and thinking about what's next in this world, in this crazy edtech world.

[00:03:09] Ben Kornell: Yeah. It's been a really, really interesting time. I feel like we've gotten to the depths of the edtech winter, so while it's heating up outside, it is really getting cold in some parts of the edtech landscape. We also have really interesting articles coming out. This new series we're doing on efficacy research, I know you've been a champion of that for a long time, but w- actually unpacking what does it mean practically some of the Stanford scale insights, how does it apply to edtech and education and, and how we see all of this playing out.

We also have David Rogier from Masterclass on the pod, and then we've had a bunch of great events, including an incredible happy hour. It was so great to see everybody there. If you haven't checked it out, our newsletter kind of recap of ASU+GSV, it's kind of the seminal, like, exclamation point on that whole event.

So before we go into too much stuff, any other updates that you have, Alex, before we whip around the world of AI? 

[00:04:13] Alex Sarlin: Yeah. Well, just building on that, that research and efficacy piece, you know, one of the interviews in this episode, at the end of this episode, stay tuned for it, David Rogier is amazing at Masterclass, is doing really interesting work.

We also talked to Angel Chung, who's a PhD candidate at Wharton, who just put out some really interesting research using about, you know, seven hundred Taiwanese high school students and, and, and doing a comparison study about using AI or not, and they found some really interesting stuff out. I, I think we're maturing as a field, you know, as the edtech winter, as people are sort of getting a little nervous about screen time, as a number of states are starting to pass laws basically banning laptops in school except with exceptions.

There's some pretty interesting things happening out there, and I think there's a, a very vocal backlash against edtech, often because there's not enough efficacy research. There's some really interesting research coming out, so I think that the Stanford Scale Initiative is incredible. I l- love the series that we're doing with Stanford and Scale and, and the, uh, Overdeck Foundation.

It's incredibly important right now, and that newsletter was one of our most read newsletters of all time. It's just worth talking about 

[00:05:16] Ben Kornell: I mean, the backlash that we predicted in, in December, it really has come to full fruition. One of the stories from this week is really about the new college graduation ritual, which is booing AI.

We'll put the link here, but a number of commencement addresses, including Eric Schmidt, former Google CEO and former lead of Schmidt Futures, which unfortunately no longer funds education, got a rousing boo from the audience at University of Arizona's commencement. And overall, Gen Z is really raising their hand to say, "This isn't great."

42% of Gen Z says AI will harm job opportunities and wages for people like them, compared with 33% of millennials and 39% of Gen X, and 37% of baby boomers. I will say AI is still polling ahead of the Democratic Party, polling ahead of Trump. So, you know, it could be worse. But I do think that we've entered that phase where the technological change is upon us and some of the dislocation and disruption, we're starting to feel it and anxieties are riding high 

[00:06:29] Alex Sarlin: Yeah.

I think an important piece of that is that I think the perception for a while was that there was this big gap between the people who are extremely AI positive, many of whom were entrepreneurs or tech folks of various kinds, and educators. And there was this thinking that, okay, tech people want to lean in on AI, but educators and principals and administrators and professors are a little nervous.

And now there's actually a, a little bit of a change in that narrative because a lot of the people complaining and worried about the future of AI are students. You know, I think we saw this-- there's a-another terrific editorial that came out this week for-- in the, in The Times from a Stanford student talking about, you know, basically saying, "I'm the first AI generation.

I came in my freshman year, AI came into existence, and now we're graduating and we're using blue books now, and every single person I know has ChatGPT open every class, and the research is not yet showing that it's, it's positive, but we know that professors have all these different reactions. Students have all these different reactions."

Even the students, which I think we had this sort of hypothesis that the educators were gonna say no, but the students were gonna say yes. I think even the students are starting to say, "What does this actually mean, this crazy change?" And like the stat you just said, over forty percent, four out of ten Gen Z think that AI is gonna really affect their job opportunities and wages.

So no wonder when you have the likes of Eric Schmidt talking to a, a graduating class, they're not booing him. They're booing AI as a concept. And the reason we know that is they're booing almost any speaker who is talking about AI as a main part of their speech, and they're saying, "Hey, AI is coming. It's gonna be part of our life.

It's gonna be part of our world." And people are like, "I don't want it. I'm afraid of what it's gonna be." That is a huge change. 

[00:08:06] Ben Kornell: I mean, New York Times in their podcasts have also put out some interesting things about how China is rolling out AI versus how US is rolling out AI. And China's really rolling out AI in a practical way, where it's just integrated in things in a very small lowercase AI way, where it's just like, "Let's make this thing incrementally better," you know, like our matching for delivery drivers or our ability to improve, like, factory efficiency rather than this race to AGI.

And overall sentiment in China is much more positive about AI making life better as a result. Part of that is a perception. I think the other part is that we are in a trough of trust With tech companies. And I think we've believed the hype before that, oh, social media is gonna make the world better, or, you know, having infinite access to content and democratizing it through YouTube is gonna be great.

I think we're now grappling with some of the, like, social impacts of that, and this is where EdTech is kind of getting swept up into, for better or for worse. And there, there may be some ways in which we in EdTech haven't had a quality or a research or an efficacy bar that's sufficient, but the number of people that are equating, like, social media, a cellphone, screen time, a learning tool, and AI, that's one big bundle that it- they're just frustrated with, and the trust is very low.

And I think that that's a theme that's only gonna get worse when we get the Toy Story movie here at the end of the summer that's gonna be the toys versus the iPads- ... versus the screens There's a cultural zeitgeist here of, like, anti-tech that I think that wave is, it's not cresting. It continues to build.

[00:09:54] Alex Sarlin: I totally agree. And you, you start to see educators on LinkedIn literally talking about EdTech like, "Hey, no to EdTech. Hey, EdTech, you should know that," blah, blah. Like, literally talking to the EdTech community as if it was a monolith, as if it was big tech or big pharma or, you know, we, we've talked about it before, but, like, really angry a- and really, I think, lumping in some of that unfulfilled promises of the sort of Silicon Valley Messiah movement that we've seen over the last decade and a half.

J- or Daras, Jinar Daras wrote an amazing book about it, about, you know, this narrative of we're gonna save the world, and meanwhile, inequality keeps getting bigger, and some of these celebrity CEOs keep, you know, start buying islands and disappearing from the society. It's crazy. We also saw, uh, this week, I think it's really highly relevant, there's a interesting paper came out from the University of Colorado Boulder, and this is covered by our, our great friends at the EdTech Innovation Hub, I think becoming one of the most important hubs for news in EdTech, that basically says, "Hey, other technologies that have come into the classroom have sort of come in and usually been gatekept by the educators."

But AI is this arrival technology. We've all heard that before, and it's really completely changed the adaptation. It's completely changed how it works, and they basically posit a framework of all these different elements of how, how you can sort of put it together. But it may not be a total revelation, but it is, I think, really worth noting that AI, unlike almost any other tech in EdTech history, really came from all sides, right?

It came bottoms up through student usage. It came from the side through educational tools that started integrating it. It sort of just landed on the entire education ecosystem at the same time between, like, 2022 and 2023. And even now, three years later, I think there's almost no clarity about the proper policies or how the grading should work.

And you see places like Stanford, which are, you know, the, models of how to do this, going back to blue books and saying, "We throw up our hands." The, the, we-- It's, integrity is just out the window. If they say that, how would... And a community college or a state school, why would they do anything different? So it's just a, it's a really confusing time, and I think that the combination of the students and the professors, we always hear about students being worried about false positives for AI identification, right?

If they write something that's identified as AI, we've seen parents suing schools for that. This combination of things is so explosive right now, and I think the evidence is, is one of the only things we can do as an EdTech community. But another may be try to differentiate between EdTech and traditional, you know, big tech.

I wonder what that looks like. Uh, what do you think, Ben? 

[00:12:25] Ben Kornell: Well, it's harder because big tech is coming into EdTech. Okay. Like, there's a blending, a merging. And, you know, one of the sessions that I'm hoping to do at New York EdTech Week with Steve Shapiro is, like, this kind of merging of big tech and, and EdTech, and where's the room for the EdTech entrepreneurs to operate and be experts in our subject matter versus what's going to get consumed by the kind of AI Goliath.

And one of the challenges that we have right now is even if we make efficacy claims around AI, the models are changing so rapidly that it's hard to recreate those studies. And in fact, I think there was a recent one where they used LearnLM, which was the Google learning one, got great outcomes, then Gemini subsumed LearnLM, and the outcomes actually went backwards because the generalized AI wants to give you the answer, isn't really trained for pedagogical friction.

[00:13:25] Alex Sarlin: Yeah. You're studying something that's evolving, like, constantly. 

[00:13:28] Ben Kornell: I mean, I think the path for many entrepreneurs is likely to be digital-physical now So, you know, we've been through a digitization revolution where we took that textbook, maybe it was just like a doorstop holding the door open, and brought it online, and that allowed greater access.

It around-- allowed for greater data capture. It allowed it to be in different languages. It could allow for personalization at scale. There's all these potential benefits, but the distraction on the machine has also shown to be a real issue, especially for younger learners. So I think the people that I know who are thinking most innovatively about this are thinking about what's the digital physical combination.

Do I have printed materials that go with the digital materials? How do I connect the two? Is there a scanner or a digital camera? Or like, how do I have kids operate and learn in a physical environment? As you go up to higher ed, I think this idea of real concrete projects that have performance tasks involved that then can get digitally captured and assessed is kind of where everything's going, and the blue book is really the kind of sign that whatever you're assessing is probably not an authentic, rigorous, relevant assessment anymore, and you need to start rethinking the actual assessment itself.

So we're in that like transition period, but this is not a straight directional line to, "Oh, AI is transforming everything to be more personalized and more dynamic and competency-based." We're actually seeing retrenchment in several vectors. 

[00:15:09] Alex Sarlin: And one of the big arguments for the folks who are feeling very doubtful about the EdTech movement is they're saying, "Hey, look, textbooks were changed.

People, uh, y- laptops per every, you know-- There was the one Laptop per Child initiative many years ago, and now we're there. The people have Chromebooks, and yet we're not seeing results. We're not seeing the education system do better." That the promise was you bring technology and you personalize, you translate, you do all the things you just named, and it's gonna get better, and they're, they're not seeing it yet.

Uh, yeah, I wanna focus-- There's a really important headline this week that everybody in the EdTech field should be knowing about that I think is really relevant to your-- exactly your point about big tech and EdTech being really, uh, coming in the same path. I know we've-- Well, most people may have heard of it, but basically Anthropic has partnered with the Gates Foundation for a two hundred million dollar partnership.

It technically is health, workforce, and education, but the education piece is pretty important here. It contains K-12 tutoring, career advising, college advising, literacy, numeracy, curriculum designing in India and sub-Saharan Africa. It's gonna focus on foundational literacy and numeracy. That's a big focus for the Gates Foundation is, is global education.

In the US, a lot of it is K-12. But I think the thing that jumped out to me, and it's so relevant to the research piece that you just talked about, is this idea of this combination. It wants to fund the public benchmarks designed to test whether AI tools in these areas, K-12 tutoring, college advising Whether they work before they scale, quote, unquote.

Right? And the problem, as you mentioned, with having research that follows on that says, "Okay, well, let's look at this school used a AI for a year, and this class used it, and this class didn't, and let's see what happened." By the time you publish it, the models have changed. But if you're reversing it around and saying, "Let's have these benchmarks that the tools have to pass before they go into schools, and they test against different types of scenarios, they test against different types of, of, of learning needs," that I think is the dream that the philanthropic community is really thinking about here.

And combining the power of the finances behind Gates Foundation, $200 million in this case, and I'm sure there's more, uh, on the horizon here, we've seen other philanthropies in this kind of space, with the technical power of an Anthropic, it's gonna be really interesting. It also gives Anthropic sort of the catbird seat in helping define what these benchmarks look like.

I don't think that's meant there's anything sinister there with-- uh, the Anthropic education team is totally amazing, but it just means that of all the different big frontier players, they have taken this interesting position saying they wanna sort of be at the core of what these benchmarks look like and what defines AI quality.

What did you make of that announcement, Ben? I know you saw that the second it came out. 

[00:17:43] Ben Kornell: Yeah. And you know, our friend Drew Bent at Anthropic co-hosted our happy hour at ASU GSV, so just wanna state our priors, like we're really close with all of the teams at Google and OpenAI and Anthropic. One thing that's interesting about Anthropic is that they carved out a beneficial deployments team So their go-to-market from the jump was a B2B go-to-market path with the idea that we're gonna build tooling that makes companies better and more effective.

Whereas OpenAI with a consumer path with ChatGPT, and Google went with a integrate AI into all the Google services approach go-to-market. What they've all done though in education is slightly different. Anthropic carved out a beneficial deployment team with the idea that we're going to do some things that are for good, that have no commercial value to us directly whatsoever, but we do believe that our model will be the best model.

And by having it in all of these beneficial deployment categories, health and education and environment, not only will we do the most good, but our models will be a backbone for like social enterprise and so on. And the most cynical view might be this is the Google play in education, where you just get people using it from the earliest ages, they're gonna become natural users.

I, I think we're in a bigger race than that kind of framing. On OpenAI, education has been in their go-to-market team, and you know, Leah Belsky's there from-- You worked with her previously at Coursera. They've really been thinking about this as a business vertical. And with Google, they've been thinking about it much more around their integrated tools for schools and higher ed.

So it's actually not surprising that Anthropic's beneficial deployment team would be more nimble and less sensitive to like commercial outcomes and more like, "Okay, how could we deploy for the benefit of humanity?" So interesting that that structural piece would play out. I think the second thing to acknowledge is Mythos, which is the newest model from Anthropic.

There's been a delay releasing it to the public because of the actual damage that it would do on the internet. And we actually, in our last Week in EdTech, we talked about the number of patches on sites like Wikipedia or other publicly available data, it was like A few patches per month, and then boom, Mythos is released and like a 1000x number of patches that companies had to release.

Well, there is this sense of if we're going to be now in this age of releasing models that are this powerful, we need to be partnering with philanthropic organizations, almost like a B2B partnership like they would do in legal or another vertical, to make sure that this is constrained for good. And so I think that's another element of this partnership.

I will say anybody expecting a big payday from Gates Foundation through this partnership, my sense is that this money essentially funds capability building, but is probably not going to translate to, like, large active grants to organizations. So this fundamental, like, tooling layer, which we've talked about with Learning Commons, we've talked about with some of the folks at Gates, I think this is a supercharge for the tooling layer, but it's probably not going to lead to, you know, your local elementary school getting two million bucks to transform their school to AI like we saw in Gates when they did the small school movement.

Last but not least, uh, math education is a big focus of Gates, and so I expect the tip of the spear here will be in math. 

[00:21:26] Alex Sarlin: Yeah, and they, they meant-- they call out math specifically for math tutoring and together evidence-based math tutoring as a specific core of this. That was a great analysis, and I think you're really getting the ten-thousand-foot view about these three foundation models and how they're sort of thinking about the education space in very different ways.

I say this every time, but it's also worth noting that, that among all the foundation models, Claude has the most restrictions for under eighteens. It's not even designed to be used by teenagers or y-you know, school-aged kids, which means that that B2B play and that sort of much more structural infrastructural play, it's just a very different structure.

And I agree with you. I don't think this is-- this money is gonna be designed to go out to specific companies in as much as it's going to be used to sort of fund and create an entire evaluation layer for evalu-- they call it evaluation infrastructure, so that the entire education AI ecosystem is raised in quality and privacy and security.

So if you're an EdTech company right now and this news comes out, I think the thing to really follow is, okay, when are they gonna have these public benchmarks? How do we be on the right side of the public benchmarks? Because they might be an important thing to be able to say, "Our tools do really well on these benchmarks."

What aspects of the work that we do fuse with the, the focus areas and the sort of theses of what they're hoping to support. I don't think it's about how do I get the money funneled into my company, or how do I get a pilot with a school or a district through it. It-- There may be some of that, but it's much more about how do I position myself as a company to be on the right side, meaning the sort of the evidence-based side of this once the evidence is actually available in this way.

[00:23:02] Ben Kornell: I mean, Alex, a question for you. How do you think this kind of thing changes the race to build the AI tutor for the next generation? Do you think that we're gonna end up having what happened to LMSs, where there's just so much philanthropic capital that it distorts the market value and, you know, essentially it becomes commoditized?

And so Gates and Anthropic release their AI tutor, OpenAI releases their AI tutor, they're all relatively at token cost, and that's the way it's gonna go? Or do you think there's still meaningful businesses to build around an AI tutor? Or on the total other end, it could be every curriculum company has their own bespoke AI tutor trained on their own curricular data.

Like, I'm just trying to read the tea leaves 'cause it does feel like there's some sort of holy grail of the AI tutor that people are chasing, but it's not clear whether it's a winner-take-all or it's a fragmented system or a distort by philanthropy outcome. 

[00:24:04] Alex Sarlin: It's a huge and very important question. I can tell you how I think about it, but I don't have a crystal ball on it because it's such a complex space.

I think it's as close to a sure thing as possible in this, that the frontier models will continue to significantly increase their capabilities to act like different types of agents, act like different types of personalities, to constrain in different ways. We're already seeing really big leaps in, in their capabilities that started powerful, and they've been leaping very high.

So I think the idea that the, the frontier model tutors, like if you go to an OpenAI, an Anthropic, or a Google standard AI out of the box model and ask it for teaching support and tutoring support, it's gonna do an accurate job. Will it be an extremely good tutor, like a personalized assistant over time?

They've all sort of dipped their toe in that water, and I don't think any of them have seen like incredible returns on it, right? I mean, we've seen Google's learning mode, and I, I-- by returns, I don't necessarily mean financial returns. I just don't think they've seen enormous amounts of usage. I think what they've, they've done tutoring modes for their commercial products, but I think many of the people who would otherwise need tutoring aren't choosing the tutoring modes.

They're still choosing the standard Google Gemini, you know, conversation agent or the OpenAI ChatGPT. So like, I think the use case in which students self-select into a tutoring mode, it-- I don't think we've seen it yet. That said, the models will get very powerful. They'll get very, very smart about being able to support.

But I do think there will be some room for, as you say, two things for, for companies to be proprietary and get, you know, better than those models. One is proprietary data. That could be curricular data, it could be historic student behavior data, it could be because you work with an incredibly specific student population, like deaf students, where you know all of this additional things that about how to tutor or students with, you know, autism.

There is proprietary knowledge. I don't think that proprietary knowledge will be domain knowledge, right? I don't think it's that, you know, the person who has the most math papers is gonna make the best tutor, and I think we have seen some tutoring companies that are sort of focused on that. They're like, "We're gonna go deeper in our knowledge."

I don't think you can out outpace the frontier models on that. But I do think you can outpace it with the specifics of the delivery model, right? Uh, who are you do- tutoring? Is it within a specific curricular context? Is it within a specific career context, right? I mean, if you're doing a career guidance tool for students in Minnesota, there is a decent chance that you can gather more specific information about what careers look like in Minnesota, what job openings are there, what skills are needed than the OpenAI would have otherwise if you put all the pieces together.

So I do think there's a chance for sort of proprietary additions, but it's gonna be hard because I think these o- these frontier models are, are incredibly powerful. Th- the one thing I would say though, I mean, we've covered all the launches of ChatGPT's learning mode and, and Go- Google's Gemini mode. None of them have quite become, you know, household names.

I don't think that any of them, even though that use case of a tutor that actually acts like a teacher, that actually acts like a really good personal tutor is-- seems like a very obvious one. We've even seen Khan and Khanmigo say, "I am not sure," because I think a lot of people don't opt into working with a tutor.

So unless the context is there, unless there's a structure there, it doesn't make that much sense as a product if you're a frontier model to have the tutoring model and, and try to sell that or promote that separately than your core model, 'cause I think most people are choosing the core model. 

[00:27:29] Ben Kornell: Yeah, it's interesting.

I, I think if you're sitting at one of the big models, you're thinking about what's the infrastructure that I'm building and how far do I get to the last mile delivery? And basically, a lot of what you talked about, the unique contours of the use case and the population basically is applicable to everybody in every industry.

And I think the scary thing for the models is if their base model goes all the way to the last mile, they don't have any customers anymore. They only have consumers who only have a finite out-of-pocket amount. They also have liability because now they are delivering last mile, so the efficacy or impact of what they're doing, you know, they're responsible for.

And I've seen a little bit more of this in healthcare and legal. They are really thoughtful about, let's stop where our generalized model expertise ends, and let's find the right companies that partner with us to translate that to specific use cases. What's weird to me about education is some people think it's an industry, some people think it's just a, a do-good philanthropic thing for the world.

So the lines are not as clear. Like with legal, it's like, oh, you know, you need a, a law degree to make that distinction. But when it's a pedagogy question, oh, do you need a teaching degree or do you not? And, and so that line has been constantly blurred forward and backward, and I think this makes it a really tough buyer experience because if I go and buy something that is doing last mile, but then there's a new model release and it's essentially effectively free to me Versus paying for that last mile one, will I accept a B+ product that's free or relatively low cost versus a paid product?

This is our conundrum with our buyer landscape. 

[00:29:25] Alex Sarlin: It's a real one, and I think th-there's a great segue to the next headline, which is extremely relevant to exactly the question you just asked, which is, you know, OpenAI has been doing these... The OpenAI's Education for Countries initiative, where they go to countries at a time and develop these complex...

They're not complex, but they do these sort of countrywide education models where they give lots of free access, they do classes, they do professional development. The list of companies on this is a, is a wacky one. We know, we know about Estonia, Greece, Italy, Slovakia, Kazakhstan, the UAE, Jordan, Trinidad and Tobago, and then we just saw this week, OpenAI for Singapore.

Singapore is a particularly interesting one when it comes to education because as listeners to this will surely know, the Singapore education system has been very revolutionary and has actively, you know, adapted to be research-based and outpace many, many other countries. It's also a country with enormous AI usage and a lot of, you know, tech-forward thinking.

We also saw Malta working with Microsoft and AI to do a national AI education program that basically gives people access to tools and courses. I mean, it's a pretty strange list of countries, right? A, a lot of Mediterranean countries, a couple of island nations. Kazakhstan is one of-- is enormous country, but not very high population.

UAE is an incredibly rich country. Like, this is a weird group of countries, but it's also a group of countries that I think are willing to sort of be out front on things for a variety of reasons. Some because they feel like they're catching up, others because they feel like they wanna leap ahead, like, like Singapore and the UAE and, and maybe Jordan and Estonia.

But like, it's a weird group, but I think it's worth thinking about in terms of the exact question you just asked, because if OpenAI goes into a country and says, "This entire country is an OpenAI country, and every kind of system, education, health, any kind of job preparation, works through that kind of thinking," they are incentivized to go very deep in a lot of different use cases.

And the country is sort of basing its entire ecosystem on one particular frontier model's offerings. What do you make of the, the OpenAI for country? We've covered it over time here. It's incredibly interesting to me because it's just... It feels like this like almost like Cold War land grab. It's so wacky.

How do you think about this, and what do you make of, of it in terms of the questions you were just asking? 

[00:31:38] Ben Kornell: From an economic standpoint, there's a lot to be interested in there. But there's a diplomacy story here, too, which is that, you know, if US models become like anchor models in other countries Then that means Chinese models aren't becoming anchor models too.

And I will say, I think there's a new opportunity for edu-diplomacy in the world where if you combine an AI model with educational tools, you can help accelerate, you know, leapfrogging from Global South countries, and that could have really tremendous diplomatic potential. I'm thinking mainly of Venezuela, where they just kind of came online with the rest of the world.

So essentially, their education system is stuck in, like, the early '90s. And instead of having to rebuild a score or transform an existing system, they have an ability to just, from a nascent standpoint, just get started with an AI native system. You know, if the US could just increase the oil exports and tax it at, like, two percent, they could fund an entire education revolution in Venezuela.

So when you're looking at a company like OpenAI, so that's the diplomatic governmental side. Let's look at the business side. What is their valuation? I mean, their valuation's so insane that any kind of normal growth does not move the needle for them. They've got to get country-sized deals going. And, you know, there's an open map where there's a lot of, like, white space, and you're just trying to gobble up as much of the map as you can.

And unlike the US, where decision-making is so decentralized, education's one of those centralized plays where you do something with the president, the minister of defense, minister of education, minister of health, boom, you're in full stack with that whole country. And we saw this with Kira Learning with El Salvador.

There's also a playbook that EdTech companies can make globally that is essentially impossible to make in the US with our fragmented system. And I would also just remind everyone that while they're independent companies, any of the Chinese AI companies are essentially national state-run AI companies.

So one question that I've been thinking about is a little bit off topic here is the Industrial Revolution had like product market fit with democracy as a governmental system Does AI actually have product market fit with autocracy and centralized decision-making as a means? Because all of the power is in the aggregation of data, of surveillance, of technology.

And I worry that from an education system standpoint, the top-down systems are going to be far more effective at leveraging AI for student outcomes than decentralized systems like ours. 

[00:34:42] Alex Sarlin: I mean, I, I think there was a reason why there was this really pivotal inflection point moment, at least in the public narrative, when Anthropic pushed back against the Pentagon in the US about, you know, various kinds of...

Because it felt like that was taking a stand about exactly the type of question you're asking. 

[00:34:59] Ben Kornell: And meanwhile, OpenAI just signed the damn contract, and then Anthropic's already in most missile systems anyways. Like, as much as people applauded the moment, I think we could also realize it's only a matter of time before, like, AI automated weapon systems are making decisions about human life Period.

[00:35:17] Alex Sarlin: Yes. And that is definitely one of the many scary things about AI that those forty-two percent of, of, of Gen Z is worried about. They're worried about their jobs, but they're also worried about- This is 

[00:35:27] Ben Kornell: why they're booing AI. 

[00:35:29] Alex Sarlin: Yeah. I mean, the stories happening in Meta right now where, if I'm understanding correctly, they're basically trying to force thousands of their employees to have AI surveillance on their work so that it can basically train them to do a lot of the work like they do, and the people are rebelling against this.

But I mean, that's exactly the kind of autocratic top-down decision that could be made inside a company or it can be made inside a country that is extremely dehumanizing, to put it but lightly. I mean, it's a fantastic question. I'm gonna be, you know, try to be even-handed here and say, I wouldn't say that AI, it has product market fit with autocracy directly.

I would say that AI is incredibly-- it basically empowers anybody who's using it enormously. That can-- Y-Yes, if you're a centralized government that has data on everybody, that is a big deal. If you're, if you're a military system that has incredible- 

[00:36:16] Ben Kornell: There's a concentration advantage. 

[00:36:18] Alex Sarlin: But it's also a big deal for health companies.

It's also a big deal for big school districts that have millions and millions of data points. It's also a big deal for, I mean, for email marketers. Like, everybody gets a boost, and even individuals. 

[00:36:31] Ben Kornell: Well, and on the flip side, it does make it easier to build. Like anyone can build their own tool. So there is a democratizing element and potentially destabilizing of autocracies in that, like it's really hard to put that genie back in the bottle and everyone can build when they couldn't before.

[00:36:48] Alex Sarlin: I mean, people have done things like built tools that, that inject pixels into their art so that if an AI tries to train on it, it completely confuses and bewilders the AI. Like, there's a sort of counter-cultural, counter-revolutionary- 

[00:37:01] Ben Kornell: AI sabotage. 

[00:37:03] Alex Sarlin: Well, yeah. So- ... we're entering a very, very strange world, and I think everybody feels that.

It doesn't-- It's hard to know what it's gonna look like. But I think your point about the AI for countries, that American AI in, in a country like Singapore, which is much closer to China than it is to the US and has a lot of Chinese associations within Singapore, it's an interesting read on it. I-- It makes a lot of sense.

There's two more stories I wanna cover, but I don't wanna leave this topic behind if you wanna dig deeper 'cause it's super interesting. 

[00:37:28] Ben Kornell: One thing I do wanna say, Alex, you know, you're talking about what's the unit of change, essentially, and centralized departments of education can be the unit of change, school districts could be the unit of change, a school, a parent or family.

And in higher ed, you know, we're also seeing lots of different grain sizes of programs launching, including what we'll talk about with David from MasterClass. I think one thing that you and I haven't covered on Week in EdTech is also the unbundling of payments in the US. While these other countries are going to more centralized, we're seeing incredible rise in education savings accounts, and now I don't think EdTech realizes $1,700 per child tax credit coming out from the Trump administration as part of the OBBA, which basically would allow parents to find educational supplemental things to subsidize their kids' education.

So they could still be enrolled in the public school or a private school or a home school or whatever, but the 1,700 as a tax credit is available to them to purchase supplemental. And we're seeing that poll incredibly well. We're seeing a space where up to 10% of families are opting in with a growth rate that suggests this could get to, like, 20%.

So while we're thinking about like what does transformation look like at the nation-state level, I also think there's a pretty cool opportunity in the US at least to think about what does transformation look like where the family is the unit of change. And it's very rare that any industry gets an unbundling of payments like this, like we're having, where it's a government-funded but a B2C acquisition motion, and I think there's a lot of business opportunity there.

Back to your question about generalized AI versus last mile, I think there's a lot of opportunity for education folks to be that last mile because a student may not want that tutor, but the parent definitely wants that child to have that tutor. And there's a real interesting triangle there with student, parent, and learning product or program offering that's totally been unlocked in the last twelve to twenty-four months.

And now with this new seventeen hundred dollar tax credit, and I'm hearing that people are gonna come out with like HSA type cards, like a debit card. So it's not like I have to wait till I do my taxes and it gets low adoption. It could be something where I literally am spending the money in my account and, you know, it's called a Trump, you know, credit or something so that he gets the political win.

But I think there's real profound market opportunity on that motion. 

[00:40:10] Alex Sarlin: Yes, I agree, and I think it's a really interesting one. I mean, I have heard that the infrastructure for this is still being built, right? I mean, we have talked to Jamie Rosenberg, who is the founder of ClassWallet, which was early on this.

We've talked to a number of different people in this movement. 

[00:40:23] Ben Kornell: Joe from Odyssey, we've talked to. 

[00:40:25] Alex Sarlin: Joe from Odyssey, yeah, a long time ago. We've ta- we've talked to him a few times. This is a nascent world. It's a really exciting one for exactly all the reasons you say. It's also interesting in light of the increasing pushback from public education against tech and AI, right?

If a state like Tennessee passes a law that says you can't use technology in the classroom, you very well could see a lot of parents and families that are more tech-friendly saying, "Well, we're gonna spend our supplemental money on technology because the school is pulling back from it." Or vice versa.

You could, you could see that, that maybe the whole state says, "Okay. Well, that's the new normal. Technology didn't work for us. We take it out of our classroom, so we're gonna spend it on books, or we're gonna spend it on screen-free devices." So it's a very strange moment, and I think EdTech companies have a, have a real opportunity to sort of help shape that narrative.

Help explain their value, help explain why, as you say, the unit of change, if a parent or a family is a unit of change, if they have their own expendable income on educational supplementals, people can do the marketing and do the customer acquisition cost and market. You also have opportunities for people who were previously B2C to sort of move into that space and get government funding, right?

I mean, we just saw-- we haven't covered this, but Duolingo is shutting down Duolingo for schools, which is really interesting. With-- It's, it's a whole topic to talk about. But you can see potentially companies that have been successful in a B2C context then saying, "Okay, well, we already have the marketing, we already have the customer acquisition movement.

We know how to get in front of people. We know how to get people to sign up and stay with us," like a Prodigy. You know, maybe now we're, now we're gonna chase this, this audience. And I think-- I'm sure they're all thinking about it in, in, in some of those ways. It's really crazy. I've heard people talk on both sides of this, right?

I've ta- heard some entrepreneurs be incredibly excited about that sort of de- unbundling, and others say it should be exciting, but it's so bureaucratic. It's-- Those cards you mentioned are not fully out yet. It can go both ways. So I, I, I don't wanna put my chips on, on one side or the other too hard there, but I agree that that unbundling and that change in the unit of change, especially away from public schools and districts, is one of the most notable things happening in EdTech right now.

So this is a little off-topic here, but I, I, I think it's relevant. You know, we've talked about OpenAI, we've talked about, uh The Google I/O conference just happened, and a number of different things were announced, but a couple that stood out to me as potentially relevant, things that I think edtech companies should at least keep their eyes on because they could develop to be things that are revelations or product changes that are very relevant to the edtech world.

One is the Gemini Omni video model, which is considered a world model. It's basically a multimodal on both sides. You can change video to video, you can change speech to video, you can change any video to text. It's this incredibly-- That's why it's called Omni, and if you haven't seen the, the sort of demo video of what it's doing, it is pretty crazy, and I think it's something that builds on some of the things we've talked about on this podcast about a future in which, you know, you can literally sort of create or manipulate reality in some ways, or at least e-either in video format or in sort of live within an XR environment.

I think we're getting closer to that reality, and it just unveils some very sci-fi, speculative, very wacky and, and exciting potential for edtech. And then I think very relevant, they're also announced their eyewear. You know, we've talked, Google Glass is now, uh, uh, ancient tech history, but Google is giving its new AI glasses.

They're taking on Meta. They're competing with Meta, but they're also competing with Snap, who's doing that. Alibaba has been making their own AI glasses. Apple, the Apple device is unbelievable, but it's incredibly high priced. But they're-- Apple is launching AI glasses. But Google doing this and doing it on the Android system means it's another very, very big tech company sort of leaning into this XR glasses.

So if you put these two together, a video model that can transform materials into anything, that can make people appear, that can do, you know, just do unbelievable things, and then glasses, we're getting into some pretty weird stuff. I'm curious if you think either of those are actually relevant to edtech or they're just pure speculation.

[00:44:23] Ben Kornell: I definitely think they're relevant. You know, one of the first principles of Education Reimagined, which if you haven't read it, if our listeners haven't read it, it's like from ten years ago, twenty sixteen, and it basically said, "What does the movement from the industrial model to the learner-centered model need to look like?"

And it's learning anytime, anywhere is one of the big principles, and the idea that you could take your AI assistant with you, and not only can it hear your voice, but it can see your world. Imagine walking through, you know, the streets of London with a history lesson going on as you look from building to building or imagine it, you know, you're working through, you know, motorcycle repair, and it's walking through instructions and helping to teach you how to do it.

[00:45:08] Alex Sarlin: That exists now. We've interviewed founders who do that already, but keep going, please. 

[00:45:12] Ben Kornell: Yeah. Yeah. And, you know, none of what I just said is, like, totally on the revolutionary horizon. People are already doing this. It's just an enabling technology of hardware. I think the other is engagement. We talk about engagement is not outcomes, but-- and we've had Lawrence Holt on before.

We've talked about how when there is no engagement, it's very hard to get the outcomes. And I think that video as an engaging methodology of learning or, like, interactive with glasses has all of this potential. I think the question is just like, how do we move away from talking about the tech to actually talking about the use cases where this particular tech is advantageous to what one might normally do?

And that's kind of the evolution of all of these things is at first you're kind of like, "Oh, the tech's a cool thing," but, like, does... What can it really do? And this is where I think VR failed. VR closed you out from the real world and isolated you in the solo experience that was suboptimal But I think XR has a lot more potential in making the actual real world more engaging or unpackable from a learning standpoint.

What's your view? 

[00:46:27] Alex Sarlin: Yeah, no, I mean, I agree with a lot of that. I think, you know, we saw Meta sort of migrate from its Horizon Worlds. You remember a few years ago, all of Meta's big, you know, tech talks were about being in this virtual world where it's all these people without legs, and they're in-- sitting in meetings together, and they've migrated that strategy enormously.

They spent a lot of money, and they migrated that strategy to these Ray-Ban glasses, where it's XR, and it's much more integrated to the world. And I think that XR, you know, AR is definitely replacing VR. This might just be the sci-fi lover in me, like, I never wanna give up on this idea, even if it keeps being launched and people get so excited, and then it fails.

When I-- When I-- My first year in San Francisco, I saw, you know, I, I got to visit the Google campus and see all the people trying their Google Glass on before it was even out yet. And, and I remember w- people were so excited about it, and it was just yet another... I mean, you know, we've had we've been talking about- 

[00:47:19] Ben Kornell: Well, you and I previewed Omni too at the Google DeepMind meeting.

That was Omni there, and I remember your takeaway was, "Wow, a kid in their living room at 14 could create a feature-length motion picture." Like, this is coming. You could- And it's so true. 

[00:47:35] Alex Sarlin: You could edit video by hand. I mean, i-i- I recommend anybody who is just trying to figure out what we're even talking about here, Omni is a considered a world model.

Like, when you talk about walking through London and seeing the history, there was this classic EdTech thing called Londinium about that, where you'd see the Roman history of London. But the thing is, with AI You can walk around London, look at a building and say, "I wanna see what it might look like inside that building a hundred years ago."

And it will just make it in video. You can literally look in it and look at it like, okay, it's 1926, here's what's going on. You could talk to people inside it. I mean, it is so crazy what is possible with this that I just can't drop it, even though in the real world we just keep trying, trying and trying and it keeps not working.

I think there's gonna be a moment- 

[00:48:21] Ben Kornell: We're gonna have to get our hands on some of these classes 

[00:48:24] Alex Sarlin: Oh my God. I mean, there's gonna be a moment where this stuff just becomes how we live. I, I, you know, I think your, your, your training example is a really key one because we are seeing companies that are doing really interesting work with sort of o- hands-on VR and AR training, and I think that might be one way in.

[00:48:40] Ben Kornell: Simulations too are really taking off as a training modality, and I talked to somebody who-- they do certifications for veterinarian. Like, you don't want somebody training on your pet. Doing simulations was too expensive. Now it's essentially free to build a simulation to simulate, you know, a dog vet appointment or horse vet appointment.

And these are professions too that once you're certified, you make a really good living. 

[00:49:07] Alex Sarlin: Yeah. So there's a job training use case that I think is important, but I think the VR, AR, XR world and all the advocates for it still have never quite found the use case, with the potential exception of Pokémon Go , that just gets people truly on board, and they say-- A- and, and regular people, not just early adopters and sort of, you know, enthusiasts actually jump into it.

But I think it may be coming because I think AI just changes... You can create worlds basically with words. I mean, you could be looking at something, say, "I wish this was made of feathers," and it will look like it's made of feathers, and then you can walk through it and it's made of fe- I mean, it's like, it's things that you just like are truly out of Alice in Wonderland.

[00:49:47] Ben Kornell: Yeah. Some of the early use cases of AI analytics were actually in sports, and so I've seen some really interesting entrepreneurial stuff around AI glasses in sports. One is when you're watching a game, like the stats and interpreting what's going on, and immediately I transferred that to a classroom. If I were looking out at a classroom of learners, signaling and, and that kind of stuff, and data capture and so on.

I think the other thing that, you know, simulations in sports is all about visualizing the game-winning shot or whatever it is. And I think, you know, we covered somebody in the UK that was running SimU School, which was basically a new teacher program where they would simulate being a, a new classroom teacher.

I remember the dread I had as a new teacher walking in on my first day. If there's any way you can bring that down, I think there's real opportunities. But probably just given that we're in this anti-tech or tech backlash moment, it's so clear that just being really concrete with the use case is going to be the key if this kind of tech gets adopted in education especially.

Because if it's abstract, I think it becomes a nice-to-have and not the need-to-have. Are there any other headlines that we need to cover before we wrap up? 'Cause I feel like we could probably do a two-hour show this week. 

[00:51:06] Alex Sarlin: There's so much happening. I mean, there's one we should cover, which is the $70 million round from UK-based Multiverse.

Multiverse is a company that's been around a, a number of years, but they've been growing. They had their first profitable, uh, quarter recently, and they had just got a 70 million, which is not a small round, especially these days, uh, round with, with General Catalyst, Schroders Capital, Lightspeed Ventures.

Multiverse is really an AI and data platform. They've done apprenticeships in the past. I don't know if they consider themselves an apprenticeship platform anymore, as much as a sort of skill upskilling platform, but it's a workforce platform. It's, uh, it's run by Euan Blair, the son of former UK, uh, Prime Minister Tony Blair, which it gives it obviously a big celebrity halo in some quarters, but that's a big round.

What did you make of that? 

[00:51:55] Ben Kornell: I mean, reskilling, upskilling, we didn't talk too much about OpenAI today, but you know, they've launched their new foundation, and that foundation owns 20% of OpenAI. So there's going to be billions in philanthropy. We-- In our EdTech Insiders chat, we also shared a Substack about, like, what does this new generation of philanthropists look like?

You know, the railroad barons were one type. Will we see new universities and libraries, Altman U, Brockman College, you know, et cetera? Or are we gonna see new methods of philanthropy? But ultimately, a big concern of the AI money coming on philanthropically is around resiliency, AI resiliency, and, you know, people's ability to pivot in such a rapidly changing marketplace.

Will we be able to get people into jobs? And even if we have some sort of, like, universal basic income, people need productive work to be doing, and there will be new jobs created that it's not clear we'll have the matching ability. You know, European edtech still continues to go steadily strong. I will also say AI optimism there Versus pessimism there.

I think they've never gotten too high on it, and they've never gotten too low because, one, the regulatory environment is strong. Two, labor union element is strong. But there has just been consistent, meaningful growth around edtech there that, you know, I think US would probably envy now. And I, I feel like my read on India right now is it's going through a little bit of a retrenchment too, because there's just been a big falloff on user use and some tech backlash there too.

So we may have to be looking more and more to Europe. I just saw the CEO of Magma Math was at like, I think it was like Kensington Palace or something crazy like that for like a AI and education retreat. So clearly the governments there are taking AI readiness and AI tooling seriously. 

[00:54:03] Alex Sarlin: Yeah. I think they feel more responsible as governments for the future workforce than the US government tends to do.

I mean, we, we do a lot of bureaucratic programs, and there is, there is money, the bure-- the Department of Labor, but I just think the Europeans have thought about it as a sort of core function of the government to keep the workforce employed and, and skilled for a longer time. 

[00:54:23] Ben Kornell: For another session. Like, I'm hearing a lot of word on the street that Department of Ed is going to be part of Department of Labor in the next legislative cycle.

So I always thought that actually could be a good thing. There's something we can all agree on, which is that schools aren't really preparing kids for the jobs of the future today, and we could do so much more. But obviously there's a lot of politics, uh, you know, in DC and at the state level that you wonder how is all of this stuff going to play out anyways.

Well, Alex, any final words before we, we head out on our normal outro? 

[00:54:59] Alex Sarlin: I mean, it's just exciting to be thinking about these trends that feel global, they feel historic. Like, I feel like we've expanded our scope from education technology to, like, the future of the world in a lot of different ways, partially because of AI and partially because all these governments and giant tech companies have been really moving into the education space.

But boy, it's just fun to think about. I hope that others in edtech feel that even if it's a little bit of a winter, there's definitely some funding issues, there's definitely some backlash. You know, there's a lot of headwinds right now. It's still pretty amazing to feel like the things you're working on are, are related to literally the history of, of humanity, and I think we're, we're really deep in that right now.

That's the only thing I'd say before we go to our amazing guests. 

[00:55:40] Ben Kornell: Yeah. And as that evolves, if it happens in edtech, you'll hear about it here on Edtech Insiders. Great to have you back, Alex. Now we're gonna kick it over to our two interviews. Enjoy 

[00:55:51] Alex Sarlin: We have a really exciting guest today. We're speaking to Angel Chung.

She's a PhD candidate in operations, information, and decisions at the Wharton School, and a Penn AI fellow. And her research develops and deploys LLM systems, machine learning algorithms, and optimization methods for data-driven decision-making in healthcare, education, of course, and social good initiatives.

She works with practitioners and policymakers to translate research into real-world solutions, and her work came on our radar because she's been studying, among other things, the results of AI and LLM-based tutoring. Angel Chung, welcome to EdTech Insiders. 

[00:56:31] Angel Chung: Yeah. Hi, everyone. I'm Angel. Thanks for the kind introduction.

I'm happy to share more about work. 

[00:56:38] Alex Sarlin: Absolutely. So, you know, one of the things that's really interesting about your work is it focuses on LLM systems for real-world decision-making, and you're really interested in proactive behavior, which is where, you know, you, you go out of your way in your papers to talk about proactive behavior where instead of changing how the LLM reacts to questions, making the LLM a little more proactive in actually making the conversation happening, reaching out, guiding students for tutoring, more-- being more adaptive.

Uh, tell us about what proactive learning looks like and proactive LLM behavior looks like. 

[00:57:13] Angel Chung: Yeah. So I think with the LLM, people already think the LLM is kind of personalized in a reactive way, right? We all have, uh, LLM give us a twenty four seven AI tutors that can response almost every question you ask.

It's kind of personalized, pretty big step from before already. But the keys to things is like they only respond what student ask. But the problem in education usually is students often don't know what they don't know. So they cannot always diagnose thems- by themself about their own gaps or figure out what to focus on next.

So the proactive system will help us to personalize this learning by, like, guiding through this process. It's not just react to the question and waiting for the students to ask, because that's kind of limited. So making the LLM to be more proactive is a little... You can think of it like more like one-on-one human tutor.

So if you have a human one-on-one tutor that will-- like, the human tutor will kind of see how you are doing and give you the suitable question to practice and decide what you should practice next. So our platform is leveraging a lot of, uh, signals from students' interaction with our learning platform, and the LLM generates the some of, uh, real-time richer students interaction with the AI tutor to quantify those things to make the LLM be able to proactively guide the student learning.

Specifically in our study, it's more like selecting the suitable difficulty level of the practice question that students should practice next and to sustain students' engagement. So if you are a faster learner, this proactive learning will guide you faster, give you more difficult question quickly, so you don't feel bored.

But if you are a slower learner, you might need bor- more time to practice, so this proactive learning will diagnose that and give you, like, uh, from easy to difficult in a more slower way, so you don't give up easily 

[00:59:22] Alex Sarlin: Exactly. And, and you know, I mean, it's a really interesting combination of this adaptive type of learning where you're choosing the difficulty of the suitability and the difficulty of the questions combined with this really modern LLM, because the signals that the LLM is using to decide the complexity and difficulty of the question isn't just whether you got the question right or wrong, but it's actually the conversation itself.

It's that organic conversation back and forth helps-- all becomes data that helps the LLM decide how to choose a suitable question. Tell us more about how that works. 

[00:59:55] Angel Chung: Yeah. So I think actually to do this kind of a personalized learning sequence or practice question sequence, it, it's not new. It's been studied dec-decades, right?

But the difficulty or challenges of previous approaches, you have very limited signal, like you have to have some assessment question coming up the exam and do the grading. I'm sure all the educator knows how much work is that. And then usually you will just leverage a binary signal whether student do it correctly or wrong.

But the learning is way more beyond that. It's way more complicated. And with the LLM, the emergence of LLM really give us a chance to get more richer signal about that, especially when the students are struggling. A lot of them actually, especially in our context in Asia, they really don't want to ask the teachers directly in...

They don't want to show that they don't understand, so they are worried about their look dumb or whatever things in front of others. So with the LLM AI tutor, they can actually ask a lot of question that they actually have, but then they are worried to be judged by the teachers or TA or the human tutor.

But those question are actually really reflects how they are doing in their le-learning process. So leveraging those signal can Better capture how the students, their knowledge state in a more accurate way compared to the previous approach that you only leverage the binary signal. 

[01:01:22] Alex Sarlin: A hundred percent.

Yeah, capturing the knowledge state, I love that phrase. And you know, and you did this experiment in a really interesting context. You mentioned in the, the Asian context, you did it in Taiwan with, I think it's seven hundred and seventy high school students taking an online course about AI for Python learning.

So it's learning Python for AI, pretty complex subject. And you saw really meaningful effects, which is something, you know, we've been wrestling in the whole EdTech and AI space to find, you know, where i-- does AI really push the needle? Where does it actually create learning gains? And you saw some very significant learning gains.

Tell us about the results of this study and some of the context. 

[01:02:00] Angel Chung: Yeah. So it's very exciting to see that. Actually, the study is pretty simple in, uh, like the experimental design is very simple. We just separate the student into two kind of treatment and control group, which treatment will have the personalized learning things I just talked about.

Your practice question is, we have backend algorithm to estimate your knowledge state in the real time and dynamically adjust your practice question sequence. And the control group is just they follow the existing common practice approach, like just from easy to difficult. It's actually a pretty strong baseline as that sequence is developed by the instructor, like expert design the sequence.

So it's kind of amazing to see the treatment group does improve 0.15 standard deviation higher in the final exam score than the control group. The final exam, I have to say, we tried to make a, a pretty fair evaluation of the study, so the final evaluation is in person and there's no digital device allowed, there's a proctor, so we are making sure we are evaluating the actual learning there.

And this is a collaboration with the Taipei City government, so it's more like a policy things that give us students a chance to use AI for learning and get some certificate for their college application, so they do have some incentive to join the study. But at the same time, I have to say in this context, a lot of them are...

I would say I'm, I'm come from Taiwan, so I can say that during the high school is actually the most stressful time in your education journey to get into the college. So signing up this kind of outside certification program to learn a new thing and be able to let them stay on the platform to keep engaged, it's pretty difficult task, especially with this specific context, like, as they are very, very busy, stressful students, like, about their schoolwork already.

So pretty excited to see how this personalization can sustain their engagement and achieve the higher, like, score at the end 

[01:04:14] Alex Sarlin: 100%. And let, let's double-click on that sustained engagement. You know, as you looked at the treatment condition and the control condition and tried to figure out what was the underlying cause of some of this major, you know, your-- the, the increase in learning you said is worth-- is equivalent to about six to nine months of additional schooling based on the World Bank model.

That's a lot of additional schooling . So seeing this kind of result- Yeah ... you attribute to higher quality interactions with the chatbot and more time on task, more total attempts. So it's not about just doing more problems, but it's about spending more time and conversing with the chatbot in a higher quality way.

T- unpack that for us, and, and I'm sure you can explain it much better than I can. 

[01:04:55] Angel Chung: So yeah, first we find, uh, we are trying to see, okay, then what's going on, why they can really improve this much. So one thing, the chatbot quality. So for each question, students will ask the AI tutor about what they are struggling and what they want to solve.

But a lot of-- as you can see, as many previous study has shown that, like my advisor, they have a paper about like people might just use the AI chatbot to get the answer, right? So we kind of, for each of the conversation, we do ask, uh, LLM to judge whether student are actually using that to learn or they are just getting answer.

And then we do find it seems like the personalization, because you keep them in their zone of proximal development, which like, it's like the difficulty level is around there. It's not too challenging, but challenging enough, so they are very more engaged to really solve the question. So they will leverage the AI tutor to actually solve instead of like control group might just feel like, "Oh, I just need to complete this," and then just simply get an answer from AI.

And for the time, I have to say both the treatment and control group, they complete almost exactly same amount of number of question. Treatment group actually doesn't complete more. As I say, they are very stressful, busy high school students. But they do like, for each question, they are actually-- they do the practice.

They do spend a lot more effort 'cause they probably feel like that question difficulty more suitable for them and more challenged. Or enough cha- like, suitable challenge for them to do it. 

[01:06:29] Alex Sarlin: Yeah, and th-that's always been the dream of adaptive systems, is to be choosing questions that are pushing students right into their zone of proximal development, allowing them to actually engage, sort of get into a deeper flow state and feel like, "Oh, it's-- I'm not just doing a series of questions, I'm actually doing a question that's getting-- that's hitting me right at the right level, and I wanna, I want to dive in and learn and unpack it."

I mean, th-this is like a dream result. It's really exciting to see. And, you know, you interpret this, which I think is fair, as evidence that, you know, personalization and this type of adaptivity, this proactive adaptivity behavior in an LLM sustains higher engagement. It gets students to be spending more time on the questions, be asking better questions themselves, and then getting major learning gains.

So what do you wanna do with this result? It's a really exciting result, and one we're not seeing consistently yet. 

[01:07:20] Angel Chung: Yeah. So I think this is exciting results. We all-- we are also pretty happy to see that. And this is actually pretty much to test our original hypothesis that whether we can leverage the LLM, the signal, more richer signal, improve beyond pe- uh, a lot of literature.

Actually, ours is really based on those literature to do this kind of personalization. So adding this component for our results, we does sh-show that this hypothesis does work. So maybe we can go further into like, okay, now we can probably think about more different ways to capture all these more richer signal by leveraging this kind of advanced technology to better do this kind of proactive personalization.

And one thing I have to say, 'cause our study is focusing on student learning, but we don't have capacity, but I think in the future there's a huge potential by leveraging this to empower the teachers as well. Think about, like, if, uh, teachers can get the real time, like, um real-time signal or like something, some tools to help them to capture what each student's unique learning progress, unique learning trajectory, then the teacher can provide a better support.

And these things is like student can just use the platform, and this platform, our algorithm will just directly have those measure for the teacher, so teacher doesn't have to do too much. They can just, oh, log into the system to see which student might be far behind or some pe- people... Oh, maybe this student are doing so well, maybe they need a more challenging question, or people who actually need more support, right?

So I think that's the one thing. We do have a teacher dashboard when we deploy the study, but we didn't optimize that, which I think there's a huge potential on that front. And I have to say, this system also have automatically question, practice question generation pipeline that's kind of a agent system to generate a practice question.

So because in this kind of systems, think about if you wanna do personalization, you actually need a lot of a question bank for you to do this personalize. So a lot of people might think, "Oh, then that's so much work. You are adding teacher's workflow," but that's also taken care by the LLM as well. 

[01:09:42] Alex Sarlin: That's right.

And the added sustained engagement and the sort of higher quality conversations that the student is having as a result of getting questions tailored to their level is something that was also-- would also be very valuable to teachers because it allows the student thinking to be articulated in a much more thorough way, right?

If they're doing just easy to hard in a set sequence, and they just go at it, you know that they're not engaging as much from your study. That higher level conversation could also be a really meaningful input to teachers to further personalize or further remediate or, you know, do anything that's needed.

[01:10:16] Angel Chung: Right. And we believe that teacher definitely, when you have a human interaction with student, you might feel you can provide additional human signal combined with all this quantitative measure to do a better way than us do, right? So I think adding this teacher component would further strengthen this system.

[01:10:37] Alex Sarlin: It's a great point. So one more aspect of this study that jumped out, and I think is also, you know, y-you've mentioned so many sort of key issues in, in AI research right now, the idea, you know, of adaptivity, of proactive versus reactive, of academic integrity, and making sure it doesn't give answers, of engagement, of, you know, of, of conversation quality.

There's just so many different aspects of this. But one that people are quite worried about in the AI world is equality, is, you know, whether AI is going to exacerbate inequalities between students or shrink them. And one of the things you found in this study is that the people who were started further behind in Python, people who are beginners or were from schools that were less high status schools, actually had higher gains.

So it was starting- Yeah ... to, it was shrinking the gap. That's incredibly exciting. Tell us about it. 

[01:11:25] Angel Chung: So I think the key thing is, yeah, those concerns are very valid, and I think people really should be aware of that 'cause as many study in EdTech already show, it's double-edged sword, right? If you don't implement it well, it's really hard to say if you have unintentional harm or worsen the inequality.

But the thing is, is that I think in our study, we show that the More gain is coming from... It's not like a very, very rigorous causal story, but it's like a very descriptively pretty obviously shows that beginner gain m-more, and the high school who's like a lower admission score does also have a more gain.

This can kind of tell us if you do it in a more suitable way, implement it well, it's actually helping the people. I would not say it's like a... Compared to the more higher ranked school students or better students, it's not like we don't help them. It's like what we can help them is limited because there's a ceiling effect, right?

If they are already good, amount of the room they can improve is like limited. But for the people who actually fall behind or who need the, the more help, if you implement well, these things can actually improve the access to a more personalized one-on-one tutor, which was very expensive and not scalable before the LLM.

So if you do it well, it's actually another side of a story to improve the equity issue to bridge the gap. But if you don't tell the student to use it in a proper way, then it could have unintentional harm. So I think there's a very careful balance like an educator should be aware of to really implement this tool in a proper way, so it goes, like, the direction we are hoping for instead of unintentional way.

[01:13:19] Alex Sarlin: 100%. And just as a sort of final thought for our listeners, many things about this study jumped out, and I highly recommend people look it up. This study is, is incredibly, incredibly interesting. We will provide a link to it in the show notes, and I actually don't have the title right in front of me, but it's, it's all about...

Uh, what, what is the title of the paper? 

[01:13:38] Angel Chung: It's, uh, Effective Personalized AI Tutor via LLM-Guided Reinforcement Learning. 

[01:13:44] Alex Sarlin: Yes. 

[01:13:44] Angel Chung: Yes. 

[01:13:45] Alex Sarlin: Effective Personalized Tutoring via LLM-Guided Reinforcement Learning. Yes. That is fantastic. One of the things that really jumped out, just to circle back to the very beginning, is this concept, I think it-- as soon as you say it, it seems very obvious, but I think a lot of people have not really absorbed the idea that LLMs off the shelf, the sort of commercial LLMs, are designed to be answer machines.

They're designed to be reactive. You ask it absolutely anything, and it amazes you by having a great answer. That's sort of been the story of the last few years. But there's something- Yes ... right, there's something very, very different about an AI that is proactively choosing questions for you, putting you on a learning path, adjusting difficulty, creating practice questions on the fly, and actually offering them to you.

That's a very different model of AI learning. And I'd l- I'd love to hear you just talk to our audience about how they should think about the differences between proactive and reactive, and where this might lead us if we lean into a more proactive version of AI learning tools. 

[01:14:46] Angel Chung: I have to say that it's kind of probably a little bit unfair to say, like, a general LLM kind of it's not very helpful in their learning, because they are really just building for the general purpose.

But if you are actually leveraging to learn, then really need those careful design that you just mentioned. The proactive way to-- One thing we do is we hope to inspire a lot more people, especially educator or, like, more experts who have more pedagogical experience. Maybe people can think about more creative mechanism to do this proactive learning.

Um, our study, we do this kind of leverage this LLM signal to do a proactive personalization about the practice question sequence to sustain students' engagement, let them stay in the zone of proximal development things. But there are a lot of different ways you can do this to sustain or encourage the productive struggle, I would say.

Like, we have another study, actually, it's like, uh, using the adversarial task to, like-- Adversarial, it means that the question is generated i-i-intentionally to ... If you, as a student, I just copy and pass this question to the AI tutor, the AI tutor is highly likely to give me the wrong answer So that I will get misled.

So this kind of, uh, if the student are experienced this kind of adversarial tasks, uh, they will build awareness about, oh, AI could be wrong. If I really want to learn, I should be more careful, and I will kind of cultivate my AI literacy to how to actually solve a question. So our other experiment does shows that that's actually improve students' learning a lot more than the people who didn't have this learning, like adversarial tasks training experience.

So there are a lot of, I would say, just reactive to proactive, there are so many different diverse mechanism way, and I think the key point is just all the design should really ground into like the pedagogical experience or like educator, their knowledge for, for decades, and the evidence from the research to really figure out what is the angle that you can help and figure out where it helps, where it doesn't, and where it helps, like this angle we find it help, whether we can develop either further improve from this direction or kind of see if there are more creative mechanism to make the proactive learning better.

[01:17:18] Alex Sarlin: I love that. Yes. The-- I think there's lots of opportunity for both creativity and integration of learning science. As you're saying, you know, we know a lot about pedagogy and learning, and we know a lot about creative tutoring and creative ways to get students to think or to be surprised or to dig deeper, and we need to integrate those into our learning systems to really get the effects of tutoring a-and, and the effects of learning that we want with LLMs.

Uh, this is-- This paper was fascinating, and I really appreciate you being on here with us at EdTech Insiders to help explain it. Everybody here should look it up. Again, Angel Chung is a PhD candidate at the Wharton School in Operations, Information and Decisions, and thinking, among other things, about how educational LLMs can be optimized for learning outcomes.

Thank you so much for being here with us on EdTech Insiders. 

[01:18:06] Angel Chung: Thank you very much. 

[01:18:08] Ben Kornell: Hello, EdTech Insider listeners. We have an incredible guest. David is the founder and CEO of Masterclass, the streaming platform that you all know and love. You've probably gifted it to your mom. You probably thought, "Let's watch Hillary Clinton," or, "Let's learn basketball with Steph Curry."

But today, we have David really transforming what learning looks like for, you know, the entire learning cycle. So we're excited to dive in. Just a little bit of, of background about Masterclass. Over two hundred instructors, from Serena Williams and Gordon Ramsay to Martin Scorsese and Bob Iger, Masterclass has built an unmatched library of Emmy-nominated, Oscar-nominated, and James Beard Award-winning content.

I mean, who wouldn't want Gordon Ramsay's cooking class to be in the front of their mind at all times? Their-- The beef Wellington, mwah, wonderful. Welcome to EdTech Insiders, David. 

[01:19:04] David Rogier: Thank you, Ben. It is wonderful to be here. I'm very excited. 

[01:19:08] Ben Kornell: So Masterclass really broke through by making learning feel aspirational and making it feel not confined to a standard classroom, but really learning in the world, and this was long before generative AI.

How do you think AI is changing what learners expect and experience from education and what they should expect and experience over the next couple of years? 

[01:19:31] David Rogier: What they should expect, I don't know if it's gonna occur because I think people are slow to actually adopt this, but what they should expect is a level of personalization of learning that actually makes it engaging.

The AI allows you to teach the same things as you would learn in the classroom in a fraction of the time. So A, you should be able to learn things much faster than, uh, you would before. But two, in a way that you actually enjoy it more and apply it more because it's very much tied to exactly what you need to learn wh- and when, uh, you need to learn it.

So I think there's tons of fear in the education world of AI. I think that's thinking about it from a viewpoint of my job might be gone. If you flip it and say, "AI now enables me to do things I could never do before," I think AI will be one of the best things for education and for the educators that actually use and adapt to it.

I think it's gonna be wonderful for them, and they will get paid more money. 

[01:20:43] Ben Kornell: You've already been a pioneer on that front with engagement with aspirational talent. In the past, universities have been kind of a group that's ring-fenced talent and expertise and knowledge, and you broke the mold with bringing all the expert voices into one's living room, into my laptop, where I can go anywhere, anytime, and learn anything from the world's greatest.

But now AI can surface some of that information instantly. What do you think higher education institutions still uniquely offer in an AI world, and where's that intersection coming from Masterclass with institutions? 

[01:21:24] David Rogier: Yeah. I think for a long time, schools and higher education have really bundled three different things.

They've bundled one, the instruction Two, the social interpersonal bonds and relationships. And then three, the signal. I think AI allows you to unbundle those. So let me share why. We have known for a long, long time that the most effective way to learn is one-on-one instruction. So then the question has to be asked, why are we sitting in classrooms, if we are lucky, in elementary school of twenty-five people, and in colleges of hundreds of people?

There's only one reason. It's simply cost. 

[01:22:11] Ben Kornell: Mm-hmm. Efficiency of delivery, yeah. Mm-hmm. 

[01:22:13] David Rogier: Efficiency of delivery. It's too expensive to give everybody a one-on-one instructor. So who was it able to get that? Only people that were, that were rich. So what does AI allow you to do? AI allows you to create one-on-one instruction that's almost as good as a person, and it's $100.

So that means the education that elementary school's costing at 15,000 bucks a year per student, undergrad 80,000 for four years, you're now able to provide that instruction for about $100. And if schools aren't adapting how they teach and what their role is, they are gonna be dinosaurs. Now, what's the opportunity for them?

The opportunity for them is stuff that AI cannot do or is not good at. For example, what we know is that the bonds you make and what you learn from those bonds are very good for a host of reasons. Number one, AI isn't good at pushing back against somebody, and we know one effective way to learn is to have to discuss it and argue it and be pushed on what you believe and think.

A person is much better at that. Two, we know one of the best indicators of, of my attendance in school, and thus how well I'm gonna do in school, is the bond that a school teacher has not only with the kid, but also with the parent That I think we should start thinking of the teachers we hire, that they have to have that as a skill.

I'll be honest with you, I had a lot of instructors and teachers in K-12 that that was not a skill of theirs. They were actually quite mean, and in fact, I didn't wanna go to school because of them. And then three, you learn a lot from just interacting with people on making friends, on losing friends, on all those things.

But schools aren't designed for that type of learning education. If I was gonna try to optimize for the social aspect of my education, I would organize intense things that I do with peers. That means trips, that means projects I have to build, things I have to make. And so I would push schools and higher ed to redesign the on-campus experience to be very much of I interact with both my peers and with instructors, and the instructors have to be trained as to coach more than to, you know, yell or scream.

The third thing I would do is I think one of the things that schools have an advantage on, or this is like the third bucketed tier, is on the signal They have spent hundreds of years building their brands. Those are important things, and I think a lot of them have been cheapening it by selling things that aren't worthy of their brands.

I would stop doing that. So those are the things I would do if I was them. 

[01:25:06] Ben Kornell: What you're talking about is actually a trend we've been following over the last couple of years, which is really the great unbundling of education. And the history of technology is actually filled with bundle, unbundle, bundle, unbundle, and we often in tech talk about what's the new stack.

And right now everyone's talking about what the new AI stack is and where does the LLM fit, where does the harness, what's the agentic level? And if we apply that same framing or logic to education, some of the indicators outside of what you're talking about are unbundled payments. So we have education savings accounts that are unbundling the payment for K-12.

We have a lot more federal dollars in tuition support for career pathways, apprenticeships, micro-credentialing, et cetera, which to me seems like not only a political reality, but a response to the fact that learning's going to have to be an iterative, ongoing thing for all adults. So against that unbundling and rebundling backdrop, I'd love to talk with you a little bit about your MasterClass, OpenAI, and University of Chicago Booth School of Business partnership.

Tell us a little bit about it, and tell us what that new stack looks like in this formulation. 

[01:26:22] David Rogier: It was an attempt to unbundle and then, like, rebundle it all together, right? So first part was the instruction. So-- Oh, sorry, I should start even higher than that. What we saw, and this is why I think it's a once in a lifetime chance for any edtech entrepreneur or anybody working in edtech, the need for adults to reskill is the highest it's been in a very long time.

Even more than it was, I, I would argue, than with the PC because the rate of change and the adoption of AI is so fast, and schools are slow at that adoption. So this is a chance to unbundle. So what we found was that employers, and we went, we w- we went and talked to a bunch of them, everybody from a Bain Consulting to a DICK'S Sporting Goods, saying, "The skills people are learning in school are not the skills I need."

And we did more research, and we polled these folks, and they said, "Hey, if I was gonna hire somebody, how I'd value a twelve-week AI intensive course, I'd value it the same or more than an MBA." Now, I have an MBA from Stanford. I had a great experience from it. That was two hundred thousand bucks at least. A 12-week AI intensive course, you should be able to do at a fraction of that cost and a, and a fraction of that time.

And when we talk to individuals, consumers are like, "I can't spend that much money, but also I can't take two years out of the workforce. I'm gonna be left behind." So we said, "What can we do to create a 12-week?" We think it's basically the first AI native business school experience. We did it in collab with OpenAI and the University of Chicago Booth, and we did a few things that I think rocked this world and also caused people to be mad at us.

Number one, instruction. We actually ran tests with our advisors at Harvard and Penn. We had a group of students watch one of our classes. We had a group of students learn via AI. We then had a third group of students use what we-- like our learning science, which is combining AI and videos and engagement.

That third group learned just as much as the first two groups, but in over 60% less time, and they enjoyed it more. So that means in a 12-week course is essentially almost a 24-week course or, you know, something like that, right? So I can compact how much I, I'd learn to be much less, and the engagement is higher.

So that's number one. Number two, we said, "The stuff you're gonna learn is stuff you actually need to know So we are teaching things by a combination of operators, everybody from Ray Dalio to the CEO of Eleven Labs to n- folks that have won a Nobel Prize. And we said, though, this is gonna be applied to things that's actually gonna help you on your job outcomes.

And then we said, but we know the social part is really important, so we're gonna host on-campus experience for everybody at the, at the University of Chicago Booth, and then we're also gonna do things like live Zooms and AI labs. At the end of it, you're gonna walk away with a certificate from Masterclass and Booth and an option for one also from OpenAI.

We were told by lots of people this was never going to work. We opened it up a few weeks ago. We have five hundred slots in the first group. We've had over twenty thousand applications, Ben. I don't know what people are waiting for. The signal to the market is very clear. And so when schools are rejecting this and say, "Hey, I wanna be cautious with AI," I mean, fine, but we are gonna leave you in the dust.

[01:30:06] Ben Kornell: Yeah. I mean, I know one applicant, Alex Sarlin, my co-host on the podcast. Amazing. We're, we're all fingers crossed waiting for is it gonna be a fat envelope or is it gonna be a thin envelope? 

[01:30:17] David Rogier: I mean, th- this was insane. Our acceptance rate is gonna be harder to get into than HBS. 

[01:30:24] Ben Kornell: Well, let's just break down the elements that you laid out at the beginning from the backwards to the forwards.

That tells me signal-wise, there's real value, and I, I wanna also just be really upfront with OpenAI has signal value, Booth has signal value, and Masterclass has signal value. So combined, these partnerships, I think, open new opportunities for u- universities to combine their brands with other leading brands that are front edge to really signal- 

[01:30:52] David Rogier: That was very intentional, right?

[01:30:55] Ben Kornell: That's super smart. I think on the social, what we find is people are connecting in lots of different ways, and the massive infrastructure of the country club of the modern university is not necessary or essential. In fact, we've had Ben Nelson from Minerva on, and ten years ago, they were doing global campuses where kids would meet up in different countries around the world and do place-based learning, and it was transformational.

And so I think there's a natural evolution here around dosage that is really compelling and interesting. And then, you know, on the academic model, I see a lot of parallels with what Alpha School is trying to do. This idea of there's this, you know, essentially learning roadmap And if we can ramp engagement and be efficient with the content delivery, students can get through more faster with higher outcomes.

And I understand the controversy for-- The nice thing about this is it doesn't have to work for all learners, but we know that there's a significant number of learners and professionals for whom the existing, you know, two-year, four-year, and the dosage of in-person classing a-and all that stuff isn't working.

So really interesting to see those things come together. Can we just talk about what's one thing that people would be surprised that actually stays the same in this model? What's one element that is actually consistent with past learning models? 

[01:32:20] David Rogier: One thing that is-- stays consistent is that to really learn takes effort.

I think the dream of everyone is, can I make education something that is as easy to do as watching my favorite junk t- show on Netflix You're able to make everything as easy from the clicking to the watching to the product experience available on all screens. What you can't do though is to learn at the rate that you wanna learn at, you have to be engaged.

So it takes effort. And we try to make it as enjoyable and as useful and as practical, and I'm getting as much out of it. But I think one thing that stays the same, it's not like I can just like chill. Like you're gonna have to be engaged in it, and the more engaged in it, the faster you're gonna learn and the more you're gonna learn and the more you're gonna get out of it.

But it's not something that's just like, you know, everybody I think wants, including me, what, you know, out of The Matrix when like I wanna learn to fly a chopper and it's just like, you know, beamed into my head and now I know how to fly it. Like this requires work. 

[01:33:26] Ben Kornell: Yeah. I mean, cognitive friction is something that AI struggles with, AI native only.

And what we're finding is that when you're testing discrete knowledge, it's really hard to create that cognitive friction because the tools are universally surrounding you to just get the answer. And this is why I like the business school format for learning as maybe a future model, not just for business school, but for other, you know, case study method projects and, and so on.

It implies that kind of cognitive friction. And if you've got to work on it individually, of course you've got challenges, but then if you're working on it collaboratively, that even more ups the ante- A hundred 

[01:34:08] David Rogier: percent ... 

[01:34:08] Ben Kornell: around learning performance and so on. 

[01:34:11] David Rogier: And there's also stuff like you're able to do.

So like what we found in our research was like the number one thing you wanna avoid is somebody getting bored. That's actually the worst thing. If they get bored, they're never coming back. It's better for them to be frustrated and annoyed than to be bored. So like we work very hard to make sure that you're never bored.

We also try not to make you frustrated or annoyed either, right? But there's all different things that you're able to do to make it as engaging as possible. To your point on the business school model, what also works is to be-- is that there's a clear prize in that like, "Hey, if I learn this, this is gonna help me get a promotion or a raise."

And so that also pushes you 

[01:34:49] Ben Kornell: So where does it MasterClass go from here? Is this a one-off partnership? Is this the core strategy? Are you going with a winner-take-all kind of we're in it with Booth and OpenAI, or do you imagine future partners in the world? And today, your primary audience is adult learners, you know, eighteen plus.

Do you imagine eventually expanding that vertically into high school or middle school? Like, just help us look into the crystal ball of the MasterClass future. 

[01:35:17] David Rogier: I'll be honest, I didn't think we were gonna make things like this if you asked me ten years ago, even five years ago, maybe even three years ago.

But we are being pulled there by our students, and the impact we can have i-is like, that's why I started this, right? Like, to build something that people can't take away from you, right? To help people learn. So I think based on the impact we're having and demand we're seeing, we are gonna massively expand in this, in this type of education.

I don't think it's just business school 'cause I think this is a way to learn. But the reason, like, my purpose behind this isn't for us to, like, win. Like, I want, like, the world to win, and so we're very open to sharing it and talking and, you know, to other schools and other groups that are, that are trying to apply this, and we have been to help just share what we've learned, 'cause there's no way we're, we are gonna do everything for everyone.

And so I think there's room in the market for a few people. On the K to 12 side, we have no plans to go in anywhere around that because that, that's just, like, not our expertise. Like, it's not. I also think, for example, like, one of the most important things in K-12 education, especially maybe to, you know, age six or whatever, is actual play.

Like, play is a very effective way to learn. That's not our expertise. 

[01:36:40] Ben Kornell: Can I put in a plug for dual enrollment for high school? Because I do feel like there's juniors and seniors out there that are massively bored. They're also looking at the cost of college, and they're thinking, "I'm starting my junior year.

I've got two years of that. I've got four years of college, massive debt after that, and I could start a business now. I could do real-world relevant meaning that's-- learning that's going to actually translate to the things I'm passionate about. Maybe I'm gonna be a veterinarian. Maybe I'm gonna be an astronaut," like, and everything in between.

I think we've, like, undercut the imaginations of- Sure. Sure ... fifteen and sixteen-year-olds- That's fair ... with, like, a low-ceiling expectation of what high school exit is, which, you know, in California all you gotta do is pass an eighth grade math test and you can graduate high school. So I'd encourage you to lean in there.

You know, Sal Khan was on our podcast about a year ago talking about exactly that, that there's opportunities for kids to get early advanced credit, which not only creates cost savings, but allows them to do the job exploration. So I'm gonna put my thumb on the while I have you, put my thumb on the scale and advocate for that.

[01:37:51] David Rogier: I will look into it. I'll look into it. 

[01:37:53] Ben Kornell: In terms of how people can find out more about what MasterClass is doing with these programs, what's the best way for them to, you know, learn more and explore with you? 

[01:38:03] David Rogier: Go to our website, masterclass.com. You're able to look at the executive program I just mentioned.

We also have been doing some really interesting stuff on the certification side. It's with Masterclass and, you know, Masterclass is always from the best in the world, but that meant for a long time the best instructors in the world. We decided to expand in the last year to also the best firms in the world.

So like if you're learning how to lead, it's with Masterclass and the Navy SEALs, and you actually get a cert from both of us. On AI, it's with Masterclass and NVIDIA. And so those programs are doing exceptionally well. It's more of like kind of a crash course in it, so if you can't commit as much as 12 weeks, this is the way to do it.

We also host tons of live stuff, so join any of those for more. And if you ever have any questions, I'm at david@masterclass.com. 

[01:38:51] Ben Kornell: Wonderful. Well, David, CEO of Masterclass, what an incredible journey you've already had with Masterclass, and now the next decade is really looking transformational. Can't wait to hear all about it here on EdTech Insiders.

Thanks so much for joining us. 

[01:39:06] David Rogier: Thanks, man. 

[01:39:07] Alex Sarlin: Thanks for listening to this episode of EdTech Insiders. If you like the podcast, remember to rate it and share it with others in the EdTech community. For those who want even more EdTech Insider, subscribe to the free EdTech Insiders newsletter on Substack.

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