Preparing Students for 2076: Ben Kornell on AI, Assessment, and the Future of Learning

In this special crossover episode, Edtech Insiders host Ben Kornell joins Allison Salisbury, author of The Humanist, for a wide-ranging conversation about AI, education, workforce development, and the future of learning. Together, they explore how AI could reshape assessment, personalized learning, school design, and the skills students will need to thrive in a rapidly changing world.

💡 5 Things You'll Learn in This Episode

1. Why AI should be used to redesign education and not just make existing systems more efficient.
2. How assessment could shift from annual testing to continuous, actionable feedback.
3. The durable skills students need to succeed in a world of constant technological change.
4. What the personalized learning movement got right and wrong.
5. How AI, school choice, and new funding models could reshape the future of K-12 education.

✨ Episode Highlights
[00:02:31] Ben shares why he's changed his view on AI-native companies and the future of edtech entrepreneurship.
[00:07:15] Why optimizing today's education system may be aiming too low
[00:09:00] The case for preparing students for 2076 instead of 1926.
[00:11:10] The enduring importance of critical thinking, collaboration, and learning how to learn.
[00:15:27] How AI could transform assessment and provide real-time insight into student learning.
[00:18:09] The biggest barriers to competency-based education at scale.
[00:22:43] Lessons from the first wave of personalized learning and what AI changes now.
[00:27:43] What an AI-native high school could look like.
[00:30:19] The opportunities and risks of Education Savings Accounts (ESAs).
[00:39:42] Ben's $200 million vision for rebuilding educational assessment
[00:41:48] Why teenagers may be the most overlooked innovators of the AI era.

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Transcript

[00:00:00] Alex Sarlin: This season of EdTech Insiders is brought to you by Cooley LLP. Cooley is the go-to law firm for education and edtech innovators, offering industry-informed counsel across the pre-K to gray spectrum. With a multidisciplinary approach and a powerful edtech ecosystem, Cooley helps shape the future of education.

This season of EdTech Insiders is brought to you by Starbridge. Every year, K-12 districts and higher ed institutions spend over half a trillion dollars, but most sales teams miss the signals. Starbridge tracks early signs like board minutes, budget drafts, and strategic plans, and then helps you turn them into personalized outreach fast.

Win the deal before it hits the RFP stage. That's how top edtech teams stay ahead. 

[00:00:53] Ben Kornell: When you're thinking about how would one really redesign it, and my guess is that K-12 will be a lagging sector because the childcare value proposition is so high that people are willing to slog through quite a bit. But as you're looking at job reskilling and up-training, the velocity of change in our adult space is so fast that education is going to have to meet that moment one way or the other.

But my hope is that we can get more K-12 systems to think about that and think about their kindergartners being the workforce of 2076 rather than 1926.

[00:01:34] Alex Sarlin: 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. 

[00:01:50] 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.

[00:02:13] Allison Salisbury: All right, Ben, thank you so much for joining us on the Humanist. Really looking forward to a future-oriented conversation around the future of AI learning and work. I wanna start with what is my favorite first question, which is what is something you've changed your mind about in the last year or so? 

[00:02:31] Ben Kornell: Well, first off, it's so great to be here, and it's fun to have the tables turned where I'm actually the one getting interviewed.

So, uh, a little bit of, uh, like crazy context. I also have a fighter jet literally flying over my head right now. I'm in San Diego. In terms of something that I've changed my mind on, it's really about how AI native companies will grow. And originally, I thought, oh my gosh, this is going to be spurring the next unicorn set of companies across all industries, and including education.

And that was in a period where we saw new AI native learning platforms emerging, things like Magic School or Brisk or School AI. And they accumulated a lot of capital, and they were growing really fast. And now that we're in a later inning of AI, I'm just seeing how much can be built by micro teams. And I'm starting to realize that the new ecosystem is probably going to look much more like a barbell future, where there's just a ton of very, very small companies with like ten million ARR and ten people, and a few large companies.

And those large companies, their primary advantage will not be being at the forefront of innovation, it will actually be distribution. 

[00:03:59] Alex Sarlin: Mm. 

[00:03:59] Ben Kornell: And they can basically fast follow anything that they see in the micro company space and add it on to their distribution channel. So I think that has massive implications for like what does capital look like and to what this company for like-- And it's probably the most exciting time to build ever, but it also has made me think really differently about what it means to be a new company today and what the race should actually be.

[00:04:27] Allison Salisbury: So if you're a founder today wanting to build something, sort of the intersection, especially of K-12, AI, learning, and you accept this thesis, what are your choices? What are the choices we are thinking about in terms of your capital stack, your ambition, your go-to market? What are the different categories that you believe will start to emerge that founders will need to fit themselves into?

[00:04:50] Ben Kornell: Yeah. So on the founder side, I think there's no company that's pre-product anymore. You literally should have a product before you have a company because it's so easy to get a V1 up and running. I think the second thing is for any entrepreneur thinking about where their defensibility is and where their core value add is fundamentally changes.

One, you can go after total addressable market sizes that are much smaller and still have a really nice profitable company. So actually, this opens up a lot in education, I think. But two, the moment that you're really successful, just remember, everyone will copy you now, and the barrier to copy you is not very high.

So you either are gonna try to outrun them with a technical lead, which I think is really a function of cash and not very defensible, or in your user experience and the expertise you have in the actual last mile delivery of what you're doing. And so that's where I think for K-12 and for entrepreneurs who really are thinking about the intersection of impact and AI, like really, really knowing your customer segment, understanding that use case, and putting that use case or set of use cases at the forefront, that's really where your differentiation and your impact will come from.

The last thing, Allison, is like AI is a horizontal technology, so it really does well across a huge variety of things. So the other part of building a company is like, who do I hire, and what's the infrastructure I need to build? What does my customer service look like? And I just think that's where there's so much opportunity to learn from other founders and build your team as lean as you can with AI doing so many of those jobs.

Because then let's say you have a ten or twenty million dollar company. Is your path gonna be to grow that to a hundred million, or is it actually to start two or three other ten to twenty million dollar companies? I think it's the latter. So anyways, I'm thinking a lot about that. I'm happy to be proven wrong, but today's marketplace is basically a bulging middle, and there aren't a lot of scaled micro companies, and then there are some of these dinosaur behemoths, but I think it'll look very different in five to 10 years.

[00:07:10] Allison Salisbury: Ben, what do people systemically misunderstand about the moment K-12 is 

[00:07:15] Ben Kornell: in? I think that the biggest misunderstanding right now is that this is an efficient challenge. And- Hmm ... I think what we have a lot of entrepreneurial energy around and investor energy is how do we take the existing framework of today's system and make it work better for teachers, make it work better for students?

I think that's a really low aspiration. Hmm. The reason why our test scores are, I mean, our high stakes tests are what our high stakes tests are is 'cause multiple choice was the only way to instrument it. But now we have all of these constraints that are now open. You can have somebody submit a video or a portfolio of their work or do a oral defense of their learning.

There's so many other modes of assessment. This is just one. Optimize the old system and just make the factory more efficient. I think we're leaving so much on the table, and that's, I think, the biggest misconception is that we have to play by the old rules. 

[00:08:14] Allison Salisbury: One story I think about all the time is the first ever TV show was two guys behind a table talking into microphones, so functionally a, a radio show that they then broadcasted visually on TV 'cause they just took the old form factor, and then they transplanted it into the new form factor.

And then it took many, many years to start to take advantages of the affordances of the visual medium of television that then led to all the multimedia that we-- visual media that we know today, and it feels like we're at the very beginning of that. Like, we're basically just saying, "How do we do the things we've already done incrementally, better, faster, cheaper with AI without actually taking a step back and thinking about the affordances of this new technology?"

[00:09:00] Ben Kornell: I totally agree, and I think there's actually an even greater urgency in education because we're not preparing kids to go to work today. We're preparing kids- Mm ... to go to work fifty years from now. Mm. What we are effectively doing in today's industrial model schools is preparing them to work fifty years ago right now, and that's because our-- what we're delivering is not a 2076 education.

And I think there's some questions like what is education for? Like, who gets educated and why? What do parents and families value? What do students value? You almost have to go back to those first initial questions when you're thinking about how would one really redesign it. And my guess is that K-12 will be a lagging sector because the childcare value proposition is so high that people are willing to slog through quite a bit.

But as you're looking at- Yeah ... job reskilling and up-training, the velocity of change in our adult space is so fast that education- Mm ... is going to have to meet that moment one way or the other. But my hope is that we can get more K-12 systems to think about that and think about their kindergartners being the workforce of 2076 rather than 1926.

[00:10:18] Allison Salisbury: Hmm. I'm curious what your advice is to people leading schools and educational institutions at all levels around aligning with this future, given the future is being-- it feels like the future of work, and for that matter, the future of what it will look like to participate in greater civil society is being reauthored every quarter as new models get released.

We are surprised by the exponential curve. Our brains actually are quite bad at understanding exponential change. We overestimate impact in the short run and underestimate what's possible in the long run 'cause we're just bad at understanding exponentials. And so in a world that's changing as quickly as it's changing today, how do we backwards map ten, fifteen, twenty years to instrument those things into schools, especially for young people?

What's your advice for just like the mindset or the mental model for how to bridge those two worlds? 

[00:11:10] Ben Kornell: Yeah. I would say there's some really good news here. One piece of good news is that is actually going back to basics around what are the key skills we're trying to build. Critical thinking, collaboration, all the competency-based metrics that we've talked about for twenty years are actually super critical because the odds of us crystal balling this thing and knowing precisely what the jobs are of the future and building our sorting hat to better align kids to those jobs, that's just an impossible task.

But if you're actually thinking about what's the core tooling that a child needs to thrive in a dynamic world where not only their job will change eight or nine times, but maybe their whole industry will change that many times. And so it comes down to things like learning how to learn and being able to articulate an argument, being metacognitive, collaborating.

These are things educators actually know quite a bit about. Unfortunately, our system has been built to-- and this is why I think there's a big difference between learning and schooling. Schooling is participating in the system and checking the boxes that are required to proceed to the next level, whereas learning happens everywhere all the time, especially with precocious young kids.

So I think there's an opportunity to really, with rigor, do competency-based instruction that's experiential, where your data capture ultimately flows through to child progression. Now, what's my practical advice? I think the challenge, there's a thinker named John Kotter who talks about dual operating.

You're operating in the old world, and you're operating in the new world at the same time. And often what that does is it makes it really hard to flip over to the new world. Most educators I know today are talking about coherence- And their systems often feel incoherent. And so I think you've got to create innovation zones within your school.

That could be a grade level team, or it could be one school of your portfolio of fifteen schools, or it could be like houses where a third of your students are in an innovative portfolio of classes. But you need to carve out some space to actually do the work to transition to a modern education stack and experience stack that helps kids thrive in a new model.

And then you're gonna figure out, okay, here's what works locally, here's what doesn't. And my time in healthcare, I learned that all healthcare is local. Like, no one cares what's going on at the Mayo Clinic if the clinic down the street sucks. And ultimately, we as a system need to provide schools with all the tooling that they need to be successful, and we provide eighty percent of the high-quality curricular resources, eighty percent of the tools.

But that last twenty percent, really it's up to the schools to figure out what fits their school and their student environment. And I, I'm very optimistic on that too because now with AI tools, there's a lot you can build yourself as a school or as a teacher. I taught sixth and eighth grade for five years, and I would have been doing a lot of mad scientist things right now with my classroom if I were back in it.

[00:14:24] Allison Salisbury: I want to stay on this idea of the skills and competencies that are gonna most matter in the future are these skills that we've long had many, many different names describe them, the power skills, the durable skills, the human skills, but they're problem-solving, working through ambiguity, persuasion, creativity, et cetera, et cetera.

And what I think is so notable at this moment is I'm with you. I think AI makes these-- They've always been important. AI makes them existential. But AI also gives us the modalities to assess them for the first time at scale. We've always had the modalities to assess these skills sub-scale. We've never had the modalities to assess them at scale, especially we've never had a way of doing it in a way that's reliable or valid.

It could be ability to do comparisons across cohorts and individual students. And so I think it's a really exciting time in assessment, and I know you agree. And so if you were to take a step back and really think about if we came back in five years and had meaningfully improved the assessment ecosystem, what would be different?

What's the big, bold vision here? 

[00:15:27] Ben Kornell: Well, let's just go through the day of a life of a student. Today, just last week, my two boys, one is 10 and one is 14, they both finished up two weeks of state testing, and these are grueling half days of testing where the other part of the day you don't get any assignment because you're so tired from doing the testing.

And we're gonna get these results in October when they're already in a different class and all of the state tests are totally irrelevant. And with my boys, I'm gonna also get a report that they're 99th percentile and that there's really nothing to work on, so it's totally unactionable. This is our testing regime that we built because of how hard it was to instrument testing at scale.

Now, with AI and all the things you just mentioned What it will look like in five years is that every Friday I do a check-in with my students that is eight questions long or fewer, and that data is aggregated, and over the weekend, I get a report, and it says, "Here's the skills that you need to reteach.

Here's the zone of proximal development next steps for these students." And I'll get some recommendations about curricular or teacher moves that I could be making in the next week. And all of that data will aggregate up to data dashboards at the administrator level and even at the state level, where I understand where are my learning gaps by competency and by standard.

Now, the world where this is nice looks like that, where the world where this is transformational is if we actually radically realign what those standards are- Hmm ... and move them away from knowledge-based standards to competency-based standards. And again, like I'm not making this stuff up. Like about ten years ago, in twenty sixteen, there was a group of a hundred fifty people that wrote an article called Education Reimagined: A Vision for the Future of Learner-Centered Education.

And in it they describe the from to, from this system to the new system, where they outline all of those strategies. This is all pre-AI. And many of us tried to implement those systems, but we burned out our teachers, we overwhelmed our students, we didn't have the data to make thoughtful insight connections.

The AI we were using was advertising AI, which was forty percent accurate. Today, we now have the tooling to meet those moments. So, I mean, it's time to dust off the shelves. There's some stuff from the nineteen seventies that describes what this move from ultimately, in educator speak, it's summative testing, moving it to ongoing diagnostic testing, assessment as learning, assessment for learning, not just assessment of learning.

[00:18:06] Allison Salisbury: What's the biggest bottleneck in achieving this vision? 

[00:18:09] Ben Kornell: Well, like everything in education, there's like a chomping motion here. There's a bottoms up and a top-down. On the top-down, our policymakers, our states, our districts need to actually set new bar, a new standard, a new accountability systems. On the bottoms up side, we need teacher practices to shift and move to data-informed pedagogic decision-making.

And- I think often wearing my educator hat, like I remember every year in Alam Rock School District, this year is love and logic. This year is RTI. This year is we basically roll out new waves of teacher professional development every year and throw away the last year. It's going to take real work to build the capacity of our teacher systems to shift from directive teaching to actually adaptive engaging teaching.

And I will say also just to caveat, like having worked in personalized learning, many people think personalized learning is individualized learning. It's actually, it often looks like small groups, whole class, and individual work all combined. And so I think from the top down, you've got to set, here's the goals, here's the rules Here's how we can help people get set up for success in the 22nd century.

And then from the bottoms-up standpoint, okay, here's the teacher moves that you could make whole group, small group, and individual to help learners reach their potential. The last thing I'll say is, like, our system today is so oriented around remediation. Hmm. Everything-- I was on the school board in San Carlos, and our school district, I got elected, people were happy with the school district.

We had, like, 96% proficiency across the board in reading and, like, 89 or 90% proficiency in math. And yet our entire reading and math program was all built around intervention and raising the floor to meet the minimum bar of the state, which is by the time you're a high schooler and you graduate, you can do eighth grade math.

And every parent I know, whether they're from high income or low income, regardless of political persuasion or ethnic background, everyone wants their child to reach their full potential. And so when we create these new systems at the policy level, we need to think about them around raising the ceiling and not just raising the floor.

And so there's this group called Mastery Transcript Consortium. They worked out, like, what would it look like to have a continuum where a third grader, let's say they mastered all the third grade standards, they move on to fourth grade standards, then fifth grade, and there's really no ceiling for them.

These are-- Like, the thought work has already been done. Now we can instrument it. And this is another place where AI comes in. Everyone says like, "Oh, Common Core failed. How would you get everyone to align on common set of standards?" That's another beautiful thing about AI is it's so good at translating one paradigm to another paradigm.

You could imagine every state has its own state standards. They could come up with their own in their own words. But with AI, you could translate it across state to state and translate your curriculum to meet those standards across state to state. And even the most innovative schools, they'll be like, "Okay, here's your California state standard report card, but press this button here and it will create a Montessorian scorecard or an International Baccalaureate transcript."

There's so much translation that AI can do for us that we actually don't all need to get in a room and agree. All we need to do is have a starting point framework and get going. And so that'd be-- I'm sorry to get off-- I'll get my off my soap-soapbox here. I think the only way this happens in five years, though, is if we start tomorrow just doing- Hmm

our own thing to move towards that direction. And I think the biggest thing I'm seeing right now in schools today in K-12 is paralysis. Yeah. We don't know what to do. We don't know if we're gonna get in trouble for it. We don't know what's good, what's bad, what's not. And I can tell you with certainty, today's system as it is, is not working for the vast majority of kids.

So to me, the like, the bar is so low that we should at least start trying. But this is where I think the best advice I can give is create a community, like a subset, a safe space where you can innovate and let it be opt-in for families. And those families that aren't being served by today's system are gonna be so excited to have an opt-in ability, and you're gonna learn so much from this zone of innovation that eventually you create the demand for the rest of your system to change and the tools and strategies from top down and bottoms up to move it forward.

[00:22:43] Allison Salisbury: So let's talk a little bit more about personalized learning. The field, I think, is about to relitigate personalized learning under the banner of AI. You obviously lived through the last wave in a pretty intensive hands-on way. What did the last get wrong, and what do you believe is genuinely different this time around?

[00:23:02] Ben Kornell: So personalized learning in the last wave was really a surface layer adjustment that the premise was around engagement and the premise was around speed. So first, you had different types of models, but one very common model is you're on this learning train, and you can go faster or slower based on your ability.

And that used assessment to understand where you are and what's next, but in a very linear way. And then the second was, okay, we're going to adapt your content to make it more interesting or engaging for you. Some kids love fractions, learning from pizzas, others like it with baseball. And ultimately, those systems never fundamentally got to the competency reimagining level.

They were really about how to move faster or make the existing system more engaging. And the other thing I would say, it was-- there was an overfocus on individual learning rather than group and collective learning that also missed the point of actually how kids learn, which is vastly more social than people would want to give it credit.

So here we are today, and I think Alpha School is like a really great model of what a modern inheritor of that old world personalized learning looks like. You do the two hours of learning that's AI-enabled. You go to the learning gym, you do a bunch of practice, your test scores go up. Great for you While I think they're showing test score growth, and they probably have some student selection also going there, it's really optimizing for the old testing system.

And ultimately, the most positive view I have of Alpha School is that they're basically saying, "These metrics are a total waste of time. Let's just jam it into this two-hour window, and then the rest of the day, the kids can actually learn meaningful things, developing real competencies, doing big projects, and all of that stuff."

So I'm far more interested in what's happening the rest of the day than what's happening in that two hours of learning. But if we were to imagine what actual personalized learning would look like, it looks more like project-based learning, where AI is providing scaffolding or not based on an individual team of student or whole class set of meaningful benchmark projects.

And in that sense, it looks-- you know, when I think about my business school experience, it was one of the best pedagogical experiences I ever had. We did simulations, we did case studies, we did projects, almost always in teams of two to six people. I think that that's probably what K-twelve education, the majority of it should look like.

And then the second element that I think is getting a lot of attention in this AI moment is the tutoring paradigm, and can we get to two-sigma impact with tutoring? And I think that what people don't understand is tutoring is not a search bar where it's like, "I wanna learn about something." That's not how tutoring works.

Tutoring works where an adult who sees your test scores and sees your competencies and says, "Hey, I wanna work with you. I see these are areas you need to grow. Let me guide you through a set of exercises and practice to help you achieve mastery of that subject." So it's much more of a guided, driven by the instructor with engagement from the student because of the relational paradigm.

So there's the personalization of the full classroom, and then there's the personalization of AI tutoring. And I think the best models that I've seen so far are where you have near-peer tutors, so like a college student tutoring a high school student. But the AI itself is upleveling the quality of the tutor and providing the data analytics to better understand what's in the zone of proximal development of the student.

Mm-hmm. So that's kind of what I think got misunderstood the first time around, and hopefully, we don't make those same errors this time. But you can already see it playing out. Again, the same kind of stories around Test score chasing with a kid with, uh, headphones on staring at a computer screen. And while one might look at that and say, "Well, I'm willing to have low-income kids do it," whenever you put yourself in the zone of I'm the parent, that's not what you want for your kid.

And I will tell you, low-income parents don't want that for their kids either. So let's stop building a system that we wouldn't want our own children to participate in. Let's actually build a system that's gonna help them thrive in a much more dynamic and collaborative future. 

[00:27:31] Allison Salisbury: Let's talk a little bit about what that would look like on a high school level.

What would a from-scratch AI native high school look like? What would be structurally different? What would be the optimal design in your mind? 

[00:27:43] Ben Kornell: This is the best part, is that it actually looks like a portfolio of school types, and I think a couple models that stand out to me that already exist, that with AI could go next level.

One type of school is the job-embedded high school, where you're getting work experience, work credit, building competencies. There's a organization called Bigger Picture Learning, and they have like a network of these schools, and the schools are basically run by the school district 'cause they're affiliated with Big Picture Learning.

They kind of adopt the pedagogic principles and practices. And in Big Picture Learning schools, kids are job placed. As they get older and older, the ratio goes up, but you know, two to three days a week, they're in job assignments. But they're still getting instruction and support around building core competencies, so that could be one type of model.

Mm-hmm. Another type of model is the classic magnet school model, where interest areas bring kids together that allow them to do meaningful projects in those areas, so an art school or a math and science school. And basically, there's an element in high school where one of the biggest drivers of your performance is actually your peer community.

When you are bringing together like-minded peers, there's lots of opportunity to get that engagement and also to do, you know, outstanding spiky Work. And so I would love us to get back to kind of the 1990s superintendent mindset. At that time, as charter schools were growing, charter schools were nascent but growing.

They weren't quite yet the threat to the public system that they became. A lot of progressive superintendents were thinking, "Okay, we've got these feeder patterns, but ultimately we want a portfolio of six or seven different types of high schools", depending on their size. You know, maybe it's, uh, maybe it's actually two high schools with three different programs within them.

But thinking about, like, different pathways for students, and those pathways are interest-based, competency-based, and give opportunity and choice to kids and families to kind of pursue what's most meaningful. Writ large, I think that that's the right future that we should have, and I think it's a great time to be someone who's passionate about school design 'cause I think there's a lot of room to be creative and locally based while still bringing great rigor and structure to the educational model.

[00:30:04] Allison Salisbury: Let's talk a little bit about ESAs and the unbundling of the learning market that comes along with them, what do you see as, given the context of this vision that you're outlining, what do you see as the big opportunities there and, and what risks would you call out right at the start? 

[00:30:19] Ben Kornell: Yeah. So the vision that we were just talking about is, like, what happens if schools evolve over the next five years to meet the needs of learners so that they are prepared for the twenty-second century?

I think the reality is that some schools will and some schools won't, and it will be very uneven. In many cases, the schools that will be more innovative will come from districts where they already have some advantage or affluency or so on, and existing schools will be struggling with hardest to serve students who have all kinds of academic but also economic challenges.

And if we're actually looking at a very uneven landscape in the future, ESAs are a huge change in how education could and probably will evolve in the US. First, like any industry that's going through an unbundling of payment where the government pays for the services and the government provides the services to the government pays the services, but a network of independent providers provide the service and they prove their eligibility for reimbursement, that is a huge sea change.

And that's basically what happened in healthcare in the United States over the last fifty years. You went from state-provided health, kind of HMO delivery profiles to a range of payment options that have also created a range of healthcare treatment options. The net impact has been really good. Decade over decade, health outcomes have really improved, but the variability is high and access is high.

And I think therein lies the challenge of ESAs. There is no quality accountability in any of these ESA systems. 

[00:32:03] Alex Sarlin: Hmm. 

[00:32:03] Ben Kornell: So you already have this unbundling. You know, Arizona and Florida are first movers in this, but currently I believe there's eighteen states that have some form of education savings account.

Now you have the Trump tax credit, which will be seventeen hundred dollars per family through the One Bit- Big Beautiful Bill Act or whatever it's called. And many people are thinking of that like an HSA where you get a card and you're able to pay for out of school, you know, subsidized learning. So what will that landscape look like?

Well, some people will be all in on the homeschool taking the full ESA tuition. Some people will be going-- switching to private school where the ESA pays for a private school, but they're still in a full school staff, and others will be doing hybrid, where they're going to a school, but they're also supplementing.

And I'm-- I would say generally, I'm very excited about that for innovation in the sector and for pathways for edtech companies. But I think it's going to be very uneven unless there's meaningful quality signal and control. And so what-- if I were sitting with a state superintendent or a state chief Of education, I would say, do you have an assessment system that you can incentivize across your ESA portfolio where you're getting diagnostic and benchmark data to understand which strategies are working for which populations, and therefore you can be a better guide for families to choose the right services and products for their kids?

And if there is that quality layer, I think there's going to be real winners and losers, both academically and impact-wise, but also financially. And there will also be opportunities for the school districts to see, oh, these are the things that are winning out in the marketplace. Are there some of these things that I wanna adopt to retain families within our structure?

But I just wanna underscore, like the magnitude of this change, I don't think that people fully wrap their heads around how big an unbundling of payments can be. Mm-hmm. And if you look at the polls, Democrats, Republicans, African American, Latino, whites, Asians, it's like across every different group income level, eighty percent support for education savings accounts as an option because the current state of education is so broken, parents are just desperate for other options.

[00:34:22] Allison Salisbury: Hmm. Yeah, I mean, it's long been said that all innovation happens either in the unbundling of products and services or in the rebundling of them, and rarely have we had a window to think about what it actually looks like to unbundle the public school system. 

[00:34:38] Ben Kornell: Yeah. And your work with entrepreneurs, I think there's a way in which the legacy companies have a hard time pivoting to that opportunity- 

[00:34:46] Allison Salisbury: Mm-hmm

[00:34:46] Ben Kornell: but new AI native companies have a huge opportunity here, where essentially all you've gotta do is convince a family that your product is best for their child, and they don't even pay for it. It's government-funded. So I think there's a new field of opportunity in K-12, just at the moment where all kind of K-12 funding through schools is receding.

You have this new unbundled funding. Hmm. I think that's gonna be where all the action is for the next five years in, uh, K-12 innovation. 

[00:35:18] Allison Salisbury: Let's talk about philanthropy. Philanthropy sort of occupies this rare ability to move in a way that's aligned with public good but faster than public institutions can do things that markets won't do.

If a major philanthropist gave you two hundred million dollars, or more for that matter, to execute the vision that you've been outlining in this conversation, what would you fund? And I think this is a particularly important question, not just because we have substantial ecosystem of educational philanthropists right now that are having to reorient and reengineer their strategies to be aligned to how AI has changed both the tool set we have to work with, but also the wor- world we're preparing learners for.

We also have a lot of billionaires coming online right here in my backyard, made their money through AI, and as a result, believe education is now even more existential than it's ever been before, and are quite actually open to the idea of reinvesting their billions back into a system that can help their children and, and other people sort of thrive in this new world.

So what are we getting wrong about philanthropy today? And you've got the magic wand, you have the pen. How do you leverage it to execute on this vision? 

[00:36:24] Ben Kornell: Yeah. I'm excited to answer that question, and first to just talk about what the philanthropic landscape looks like. So you basically had the last generation of tech billionaires get royally burned by the education space.

You've got Zuckerberg and Newark debacle, and he's pulled back from CCI, and now it's called Learning Commons. You have Eric Schmidt, who started Schmidt Futures, thinking about AI in education. They've totally taken all the education stuff out, torn it down. You have Bill Gates, who has twice written letters about how basically their strategy over the last ten years has been a failure.

That includes small schools, which I don't think were a failure, but you know, everybody who comes into new money and says, "Man, I'd love to be philanthropic in education," they're surrounded by a number of people who say, "Don't do education. It's like flushing your money down the toilet." And you know, like Benioff gave thirty million to Oakland Public Schools.

Six months later, they're like, "Well, we're at this like financial deficit. We're getting taken over by the state. Where did the thirty million dollar goes?" I don't know. So I just wanna be cautious. Like, yes, there is a moment where a bunch of hundred millionaires are coming online. Meanwhile, we've got mass billions sitting in donor-advised funds, DAFs, just sitting on the sidelines.

Mm-hmm. And the thing that's holding us back the most is that there's profound distrust of institutions to steward any financial donations at this point. And I would include higher ed in that, is also becoming more skeptical about higher ed institution donor stewardship So we have a moment, and we, education, cannot squander this moment because philanthropy fills in what the marketplace itself will not do for itself.

And so coming back to some of the themes we've touched upon, you know, my theory of change is very parallel to the theory of change in healthcare. When diagnostic medicine was invented, that was looking at blood under a microscope. It went from witchcraft to science, and it moved from long period assessment: I did this thing, you died.

I did this thing, you lived. Okay, must have worked or not worked, to I can rapidly administer treatment, and then I can evaluate whether that treatment was effective or not. I think that assessment creates the condition for success. And when I worked at DaVita, a kidney dialysis company of all things, we had the DaVita Quality Index.

It was a score on, from zero to one hundred that would score an individual patient as well as your entire clinic on what the patient outcomes were. Now, there were twenty different inputs into that that I would never understand, because nephrology is, like, a very complex science. But every year, we'd have the best nephrologists in the world recalibrate what the DQI would score and what the blood levels and what the measurements would be.

And literally every three days, our patients would have a DQI score, and you could track not just where they were, but where their movement was. So if I had two hundred million dollars, and I'm thinking, "How do we build the twenty-second century learning system that helps all of our kids reach their full potential and not just meet a threshold minimum floor?"

I would radically rethink designing assessment. 

[00:39:42] Alex Sarlin: Mm. 

[00:39:42] Ben Kornell: And I would be working with the best psychometricians, I'd be working with state-level chiefs, but coming up with a system that allows in very, very short order diagnostic testing, like very short, brief sprint tests, I could have both individual and collective status of student performance.

And from that spine, then you can branch off pedagogical techniques and practices. You can branch off curricular tools and products and services. You can branch off delivery modes, spaces, and places. And with that, you can have a decentralized innovation layer that happens because everyone's running from the same scorecard.

And that ultimately, I think, is what we've missed. You know, the New York Times article talks about the last essentially decade, decade and a half, there's a decline across basically all learning metrics. It blames a variety of functions, but I think one of the things it misses the most is that we lost our accountability system.

And No Child Left Behind was not the right-- They weren't the right measures, but it was the right kind of strategy to create a language around what student performance should look like and what kind of supports we should have. And just to go one step further, I think ultimately, whenever you have a patient in healthcare, it's not they're proficient or not proficient.

It's all a continuum. Are they getting healthier? Are they getting sicker? And would I love to fix healthcare to move it from sick care to more proactive care? I totally would. So it's got things to figure out. But there's this sense of like growth over time or improvement over time that is far more important than absolute measure anyways, because ultimately We now need a nation of learners from K to the grave, or from cradle to grave.

We need everyone to be able to learn and ingest new inputs and develop new skills. And so that's really, you know, my $200 million moonshot. 

[00:41:40] Allison Salisbury: Then always my final question, what's a small signal in the world right now that you think others aren't paying enough attention to? 

[00:41:48] Ben Kornell: 14 to 16-year-olds. One, we parents are just so bad at listening to our kids.

We educators often kids say... You know, of course, it's like nice to see them on the stage singing in the school play and so on, but the kids are ready to build. 10-year-olds are ready to build. And I think the entrepreneurship that I'm seeing from 10, 11, and 12-year-olds, 13, and 14-year-olds inspires me. I feel like now the problems that kids experience don't need to be built by someone else.

They can build the solutions. And so what a world we're going to live in, where everyone is empowered to solve their own problems by building. I feel like we're missing a, a huge story, which is, you know, this four years of high school that we're pretty much currently wasting could be like our zone of innovation, entrepreneurship, and economic mobility because those kids, they're ready to build.

[00:42:42] Allison Salisbury: Ben, thank you so much. What a fantastic whirlwind through what the future could look like. I have been saying this all year, but I think we desperately lack optimistic visions for the future in our sector. I think a lot of the narrative is fear-based. It's things we're afraid will happen or will happen to us, and I believe those conversations are central.

Fear gives us guardrails. Fear helps protect us. It's a, it's an adaptive thing that we do as humans is we're afraid of things. But it doesn't give us something to be inspired by or to move towards. And I can confidently say that this conversation was one of the most cohesively optimistic visions of the future that I have-- I've seen or I've heard.

So thank you so much. 

[00:43:28] Ben Kornell: Thank you, and there's never been a better time to be in education than now. So thanks so much, Allison, for having me on. And for those who wanna check out more of our stuff too, feel free to visit EdTech Insiders. We've got a Substack, and we've got edtechinsiders.ai with some of our writing about AI and the future of education.

[00:43:46] Allison Salisbury: I could not be a bigger fan. Thank you, Ben. 

[00:43:49] Ben Kornell: Thanks so much. Bye-bye. 

[00:43:51] Allison Salisbury: Bye. 

[00:43:52] 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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