aiEDU Studios
aiEDU Studios is a podcast from the team at The AI Education Project.
Each week, a new guest joins us for a deep-dive discussion about the ever-changing world of AI, technology, K-12 education, and other topics that will impact the next generation of the American workforce and social fabric.
Learn more about aiEDU at www.aiEDU.org
aiEDU Studios
Zarek Drozda: Every high school student should be data-literate
What do K-12 students need to thrive in a world increasingly shaped by AI?
Zarek Drozda, executive director of Data Science 4 Everyone (DS4E), believes data literacy must become as fundamental as reading literacy – and he's building a movement to make it happen.
Inspired by a viral Freakonomics podcast that questioned outdated math curricula, DS4E has grown from operating in one state to 29 states in just four years. Their approach isn't about creating an entirely new subject – it's about intelligently integrating data literacy into existing courses in ways that feel relevant and engaging to students. When kids analyze NBA statistics or Spotify trends, abstract math concepts suddenly become tools for understanding real-world phenomena that they care about.
Zarek takes us behind the scenes of building his nonprofit coalition, sharing honest insights about the challenges of education reform and the strategic choices that led to DS4E's rapid expansion. Rather than competing with other educational nonprofits or building curriculum themselves, DS4E have focused on being connectors – bringing together teachers, researchers, policymakers, and curriculum developers to create sustainable change.
You can find out more about Zarek and Data Science 4 Everyone at:
aiEDU: The AI Education Project
Hi, I'm Alex Katran, I'm the CEO of AIEDU and we're here today, rick, to record one of our first podcasts Not the first, but one of the first podcasts for AIEDU Studios and with me I have Zarek Drozda, the CEO of Data Science for Everyone. Zarek's a longtime colleague, friend and co-creator in the space, who's been working to create more opportunities and pathways for kids, specifically, in your case, in the field of data science, and I'm really excited to dive in and I want to hear more about the genesis of, as someone else who's also founded a small but growing nonprofit in the space, what that experience was like, and hopefully we can also nerd out a little bit about artificial intelligence and how data science is a part of that.
Zarek Drozda (DS4E):Awesome. Well, thank you for the invitation to join. I'm just super excited to help trial out AI EDU studios, and launching a new initiative like this is always super fun.
Alex Kotran (aiEDU):I'm so grateful to be part of one of the one of the guinea pigs, so so zarek, why don't you just so tell us about data science for everyone, and and if you could just regal us with the story of, like, how this all came to pass? You're relatively young, um, and so uh, yeah, how did someone like you sort of come to uh, into leading uh, what is now, I think, widely recognized as one of the leading orgs if not the leading org specifically in the field of data science equity?
Zarek Drozda (DS4E):Yes, data Science for Everyone. We're a national nonprofit initiative based at the University of Chicago advancing data science and data literacy around the country, and what we're really trying to do is help teachers and school leaders, and even state leaders, think about how to modernize the curriculum and especially modernize some of the core school subjects. For the world of changing technology, for the world of AI, for the world of big data, for the world of whatever the next emerging technology is going to be, that is our core focus and we've done a lot of work in particular, on systems level change and thinking about how to get the many stakeholders across the full breadth of the education sector to work together, combining research and practice and policy, on that really big challenge. In practice, we have a pretty narrow focus, which is on using data science, data literacy, as a vehicle to think about broader curriculum modernization, and we're in 29 states now or at least 29 states now have a data science or data literacy education program which we're super excited about. That's up from one state in 2019. So we've had huge, fast growth which we've been really excited about. But it's not depth yet right. These are like early stage. Majority of the States are pilot programs. It's like 20 or 40 schools we're not talking all students and you know, all teachers by any means, yet I mean there's there's so much more work to do.
Zarek Drozda (DS4E):Genesis to data science for everyone is kind of a fun backstory and I often give the spiel about it starting from a Freakonomics podcast and we having this viral moment, because I used to work at a center at the university with Steve Levitt, who's the co-author of that podcast and the book series that preceded it. I was in undergrad and Levitt had this crazy idea. He was like I've been doing academic research for 20 years 15 years probably got the number wrong and I feel like the impact has been rather minimal. I've written all these books, podcasts, been in economics and in a very applied part of the economics field and he was like the one real world impact that my work has had was the changing of a very unknown policy in Alaska on drunk walking, because there's a chapter in Freakonomics that relates to the risks of drunk walking and drunk biking as an undercovered issue that actually leads to a lot of traffic incidents. So anyway, he got a resolution introduced. I actually don't think the law even passed but anyway. So he was like the real world impact of my work is rather limited.
Zarek Drozda (DS4E):I would like to go into social impact and think about how to take the principles and ideas and free economics and apply them to the real world, but for social benefit. So he was putting together the center called RISC and it was designed as a quasi-incubation engine, quasi-consulting practice. It was really kind of like a. Does it stand for something? Risc? Radical Innovation for Social Change.
Zarek Drozda (DS4E):Okay so R-I-S-C yeah, R-I-S-C, but we stopped using the acronym a while ago because Steve thought it was embarrassing. It's kind of a corny name, but it was almost like a Y Combinator for nonprofit ideas. It was like a startup incubator for trying to launch and experiment and just try new solutions on old social challenges, using behavioral economics and incentives and data as a vehicle to understand social issues at a deeper level and to unpack maybe hidden issues or challenges or, in a lot of instances, actually applying technology to public sector arenas where there was not great uptake of it. So, to give you an example, one of the other projects that the center is working on right now they're looking into foster care matching and how adopted children can be better matched with foster parents and the technology and the resources that government agencies and NGOs have to facilitate the matching process is actually quite underdeveloped.
Zarek Drozda (DS4E):A simple spreadsheet is not very frequently used in some context. These are small nonprofits with three or five people each where just upskilling, capacity training around that type of skill building can be really helpful and can actually make a huge difference. That's one tiny example there mean they worked on. There was a number of climate projects, criminal justice we looked at. There's a huge project right now in kidney, matching a way to. There's an online high school, a number of different initiatives, but anyway, BS4E, or this initiative for data science or data literacy education, was a bit of a distraction from that typical set of projects and it came from Levitt sitting down with his daughter one night while they were doing math homework and he was like he looked at the homework that his daughter was doing and his daughter was in UChicago Lab High School right.
Zarek Drozda (DS4E):So a great high school. Those kids are going to get into a great college or university someday the folks who get to go there. He was filling out the homework and trying to tutor his daughter I think 10th or 11th grade, and he was like my god, like none of this material relates to anything that I would be teaching my undergraduates, let alone that they would need post-graduation. He was frustrated and just felt a lot of the content that was in the existing curriculum did not relate to the real world, did not relate to college preparation. We were just, you know, because we haven't done a serious look at the curriculum in a long time. There have been a lot of tradition and a lot of you know at the concept level and, like the, how you spend literally class units or weeks hasn't been updated for the huge emerging toolkit that kids need for a very changing world today.
Zarek Drozda (DS4E):We got David Coleman, who's the CEO of College Board, and one or two NSF-funded projects who were working on introductory experiences for data science, data literacy education, and a few math education researchers together to just have a conversation about this, and that podcast then blew up and got viral and it made waves in the education community, specifically in math, because I think there were a number of initiatives that were already ongoing under the surface around a greater focus on project-based learning or trying to look at ways to integrate more technology into the curriculum that were just starting to bubble up. So we really shone a light on some things that were already happening in the field and we were just able to grow it from there things that were already happening in the field and we were just able to grow it from there.
Alex Kotran (aiEDU):Yeah, I mean you. It's interesting because I mean most people and I would include AIEDU in this in this category, you know, most nonprofits kind of it takes time to get to a certain caliber of funder like the Gates foundations, like these sort of like big institutional funders that generally, you know, just have a level of rigor that small sort of startup nonprofits really struggle to be able to cobble together for those really long and arcane proposals. You all kind of like stormed out of the gate with some like really big, heavy hitting funders and I mean I suppose there's part of that is like you were at UChicago and part of that was having Levitt. But I mean, was it? Do you credit that like mostly to just the way that this resonated with people who are paying attention and thinking about math curriculum and it's sort of like the dots sort of connected really quickly.
Zarek Drozda (DS4E):Yeah and I think I mean a lot of it honestly was, I think, definitely attributable to a huge media splash. We had the platform of Freakonomics to just have this discussion about. We never expected it to turn into the initiative that it did At that time. It was a total experiment. We didn't have any funding, there was no project plan for it. We put a podcast episode together because we just felt it was an important issue to highlight and to at least try to build some sort of conversation around the momentum that it generated. Honestly, we spent a lot of time catching up to the exciting kind of I don't want to call it a firestorm, but the, the, the momentum that was generated after. That is when we started to get uh, you know, increase the the film thought will start and then increase the philanthropic support for the project.
Zarek Drozda (DS4E):And we were able to bring in other um uh champions for the issue right. So, like Arnie Duncan was a early um uh, you know a colleague of Levitt uh, and they had Schmidt Futures was an early funder of ours who who came in, who was really excited about the initiative to help grow that conversation and then bring it to the next level. Because I think what we? We started a conversation. We didn't know anything about movement building at the time and we had to do a lot of learning to figure out how to put an ecosystem together.
Zarek Drozda (DS4E):We spent months like just studying the K-12 system and literally a year and a half of conversations with state leaders and district leaders and teachers and curriculum developers and assessment folks and just anyone else who represented some lever or part of the system to understand it much more deeply. And then Data Science for Everyone didn't exist as a proper unit until like two and a half years later or somewhere around there. We didn't start it officially within the university or spin it out into its own unit until like summer 2022. And so the organization is much younger than folks realize, but the general effort it was almost like a campaign in the early days. Um has been around the longest and how did you?
Alex Kotran (aiEDU):I mean you were. When you say we you know, at the time you were had you graduated yet or you were still an undergrad oh, no, no, I yeah, I graduated, I was.
Zarek Drozda (DS4E):I joined risk right around the time the podcast came out I see.
Alex Kotran (aiEDU):So you were basically employed by risk, um, and, but you're fresh out of college, there's some really heavy hitter, uh, researchers, and you know media, um, uh, the word for like, how would you describe levitt? Uh, uh, he's not a, not a podcaster, it's really, he's like a uh, he, well, he's, I guess.
Zarek Drozda (DS4E):Well, I guess he's an economist, but he's currently still a professor emeritus at the university. But definitely a bespoke thought leader is maybe a good description.
Alex Kotran (aiEDU):Yeah. So basically you were fresh out of undergrad, you were sort of around these heavy hitters. I mean, really, levitt was sort of the og. When we think about podcasting, I mean, I remember when I was in college like going for runs and listening to free economics, um, before any of this, like youtube stuff had blown up, um, how how did you go from you know, being relatively low on the ladder which I know that, like, universities can be very hierarchical to you know, right now, this is, this is like you're running this thing, and it feels.
Alex Kotran (aiEDU):It does not feel like you were sort of just, you know, hired as sort of like oh, this is a smart person to like actually get this thing going. But rather, I mean, I feel like from my conversations with you, even before 2022, it was I don't know if I'm over crediting you but it really felt like this was your baby, like it felt like this was something that you were driving and I feel quite confident that if it weren't for you, it might have just sort of like stagnated as sort of like this kind of like side project within UChicago that may or may not have like generated into something you know with real impact, to basically name the tech or education conference with any sort of intersection with education or data science or computer science. And you generally are there. Did you volunteer yourself? Were you voluntold? How did that happen?
Zarek Drozda (DS4E):Well, I mean, first thing, I have to acknowledge I'm super lucky. The opportunity set that I had coming out of undergrad was crazy, I think. Getting this opportunity to join the center that Levitt was putting together around you know, right place, right time was incredible, and I got so much mentorship, so much training, so many people volunteered their time to help me understand the space better and to become a more effective leader, and I'm, like, so grateful for it.
Alex Kotran (aiEDU):I sometimes will get young folks, sometimes in college, sometimes in high school, sometimes actually far along in their career, and they have some big idea they're really interested in. Like how do I start a non-profit? And I actually struggle to give advice because it's it's it's different than starting a company where it's like all you need is a good idea and then you sort of just go and pitch investors.
Alex Kotran (aiEDU):Like the non-profit world is like a little bit more esoteric and arcane in terms of like the way you get in front of it's like much more reputation based, um, so with the understanding that you were in the right place the right time and you had a lot of privilege and opportunity that was sort of laid at your feet because just of the auspices of you chicago, um, but what were some of the things? Sort of like, as you go back and sort of evaluate what you did, right you know, can you abstract any advice to sort of like a young upstart social entrepreneur who is interested in doing something like this and just trying to figure out, like how can I optimize my luck? You know, like obviously some of it's luck, but some of it's also, you know, manifesting and using that luck to your advantage.
Zarek Drozda (DS4E):Yeah, great question, um and again. So super lucky and grateful for the set of opportunities that we had and this breakout moment that we had with the podcast and the launch of this really what became a national conversation. What also folks don't know is that we almost killed the project several times after the podcast came out. You know, because we spent a number of years well, I mean a number of months at least, you know exploring the space, figuring out the way that our team could be helpful, figuring out a way that we could gather the right people, you know, representing teachers and district leaders and administrators, to create a concrete output that actually built on just what was at the time, an idea. Advice for starting an initiative like this. I think you know persistence is definitely an important one. I cannot exaggerate the number of times Levitt came into our office and was like I just think we should kill the project. We aren't well positioned to help here, or I just don't see any momentum anymore, or the K-12 system is too bureaucratic, it's going to take too long and we're going to have to talk to too many people and convince too many uh stakeholders to to keep this going. Um, and I think I saw I was always very excited about uh policy work and in the intersection of economics and data and policy change, and you know, I think I um had a great example from my parents, but also others of of you know, the importance of persistence and that you really just had to go out and talk to people. Um, and I think the one thing that uh was was super important for our work is is I mean, this is gonna sound cheesy, but a lot of listening, right like.
Zarek Drozda (DS4E):We just spent so much time time surveying the field and trying to understand what was needed, and we've always had a very intense focus on the problem.
Zarek Drozda (DS4E):And I just came back from our team retreat and we were talking about how, you know, I think there is a tendency for some nonprofits to like build a very like box or fixed solution to solve a problem, but they might not be capturing you know something that's systemic in the system, or they might be missing a lever that's, you know, in policy, or with a teacher capacity or with a you know a weird bureaucratic red tape at a district that they have to work through and then, if the landscape changes, well, what do you do with that box solution?
Zarek Drozda (DS4E):And we've spent a lot of time in our team doing a lot of strategy work and come back to it regularly so that we are always having a very intense focus on solving the problem of, let's call it, the curriculum not being up to date and not having the capacity to teach a line to new content, and I think having the ability to adapt constantly as we would find new or unearth new challenges was was super important. Advice that generalizes to other nonprofit leaders trying to just trying to start something, I think is really hard because every context and instance is super different, and I love to like throw the question back to you and hear a little bit about, like your origin story and the founding of AIEDU, because I think you built a team from an idea and at least from my lens from scratch into something that is really well recognized now, and I think you have so many incredible partners, so I'd also love to hear about your journey and how you all got to where you are today.
Zarek Drozda (DS4E):Make sure this is a two-way street, but maybe I'll have some better ideas for advice for others.
Alex Kotran (aiEDU):Well, this is actually not a two-way street. This is not a ploy for us to talk about AIADU.
Alex Kotran (aiEDU):I will respectfully decline your kind invitation for me to talk about myself. I will try to draw some connection points, though I think persistence is probably that would be my response as well. And then the other piece is you talk about listening.
Alex Kotran (aiEDU):I think in Silicon Valley parlance there's this idea of product-market fit, and one of the things that you hear a lot from investors is that companies always overestimate their product-market fit. They'll come to you and say, oh, we have product-market fit and it's like no, you have traction, you have some interest, but product-market fit is actually really elusive. Some interest, but probably market failure is actually really elusive. And so I think maybe the way to abstract that is almost like the humility of you know, kind of like assuming that you're wrong until you're, like, really sure that you're right. Because if you kind of go into this with this assumption that you know what people need, like, first of all you probably are wrong and then, second, people can kind of sense it. And I think, especially the work that that we do, that our respective organizations do, which is, you know, many cases, like I'll be, I live in San Francisco and Mo. You know, when we talk about all the travel that you and I do, we're often going to places like you know, I'm going to Austin.
Alex Kotran (aiEDU):I'm going to, um, you know, columbus, ohio. I'm going to, um. You know, colorado, like it's uh, you know we're going to places where, um, there's a sensitivity to like these sort of like coastal uh. You know, experts coming in and like telling us what's right or wrong and so, um, and that's maybe unique to the non-profit space where it's like there's um, you know, part of what you're actually building is like trust and reputation and the the like move fast, break things, like use sheer ambition to get what you want. Like that kind of does work in silicon valley, like and there's a whole separate discussion about whether that's good or not but there is like a reward structure for like just pushing and fighting and just like being ruthless. Um, I think people sniff that out really quickly and in my view, that makes the work a lot more rewarding, because the people that you're around, yourself included, are just like way more down to earth and like legitimately cool.
Alex Kotran (aiEDU):And when I go to education conferences, nobody zero people are at an education conference because they want to get rich, like, if you're trying to get rich. There's like a very long list of industries Like I think the HVAC industry is $140 billion, which is bigger than the K-12, the entire K-12, like like industry, including curriculum and everything else, is smaller than just like HVACs, um, and so I think what that what that produces is a set of people who I think there's still ambition like no doubt, um, but it's like ambition oriented towards like helping kids, ultimately, and that's a very nice sort of place to be, and so I think, just like really leaning into that, and if people feel like like legitimately, that that is what you're trying to do, they'll actually give you a lot of grace to get things wrong, okay, so just to like to say something more about the authenticity piece and not moving fast and breaking things.
Zarek Drozda (DS4E):I think that is a huge difference between the nonprofit sector and Silicon Valley, and then, especially in education, that matters, right. There are so many people constantly knocking on the door of school districts saying you should teach this new thing in the curriculum, or I have this great new, you know tool, tech tool, uh, that will like, change your kids, uh, trajectory and um, I think I think to to build legitimacy in the space and and also to just be a um, to have a effective and lasting impact, you need to build something quite sustainably and with a lot of intention, and slowly because the system is so used to the alternative and.
Zarek Drozda (DS4E):I think we recognize that quickly. It was helpful because it wouldn't even when the the Freakonomics thing launched, you know there were definitely we'd also got criticism. It was you know oh, yeah, yeah.
Alex Kotran (aiEDU):Like let's, what are some of the critics? Like what were the comments? Like going into the, not that you had a comment section, but if you had a comment section, what are some of the, some of the things you'd have read?
Zarek Drozda (DS4E):yeah, well, anyway, I think I mean, uh, you know, there's always the just like, uh, oh, uh, you know, anything associated with like the, the freakonomics brand of, uh you, poc academia, I think is one, and that was a little bit more standard. And I think we had a lot of folks reach out who had been working in ed reform for a while who said you know, here are the mistakes you made and here are the way that you framed it poorly in that initial discussion. And I literally had a great colleague and friend, ray Levy, who used to be in leadership at the MAA, which is one of the math associations, reach out, and she helped me put together the website and do all the framing of the issue and think about the layout and how it'd be most helpful to teachers and researchers in the space, as we were like building our very simple resource hub and so so there were folks who had critiques, but then they came with suggestions and then mentorship and then we turned it into something and then it grew right and like I think that like very authentic, real um relationship building was so critical for the work that we did, um and I I think we were we were always clear about what we brought as an outsider to the sector or to the space and how we were able to provide some value through looking at it holistically for the first time. And then there were so many people who joined who got excited about trying to build it into something larger. And the other thing I was going to say is and I think this is important for both of our organizations and I think you all do a great job at this At the end of the day, if you are a national education nonprofit, you are a service organization helping a lot of individual schools across the country in an incredibly decentralized system where the focus is what am I going to teach on Monday?
Zarek Drozda (DS4E):How am I going to fill my classroom two weeks from now? How am I going to teach on monday? How am I going to fill my classroom? You're two weeks from now. Um, you know, how am I going to get a school bus driver? And I don't like say that any of that to be ingratiating. Like. I say that because, um, I think the, the influence that any of our national teams has on the day-to-day experience of running a school is like so tiny and, uh, the bandwidth to pay attention to like the you know 20 crazy headlines that are in education news is also so minimal, um, and I think, uh, keeping that spirit is is so important right that we are servicing the field ultimately um, I mean, I'd love to actually like lean into that because it's um, um, it's something that I'm like.
Alex Kotran (aiEDU):I mean my, my parents are both teachers and so I often just hear and like most of the folks on our team are, are educators or former educators Um, and you know the stories that, like teachers have literal war stories. I mean my mom when she was teaching at Firestone high school, you know, when we were actually doing our very first pilot at Firestone, the very first curriculum we built, you know they had, I think, two or three incidents of kids bringing guns into school. It wasn't a shooting, but it was still like you know, you can imagine that's an incredibly destabilizing experience to like have to evacuate a school because somebody's brought a firearm. And so that's the context that we're trying to like get folks to pay attention to this new thing which is called AI education and and that is basically like the norm not necessarily guns every day, thankfully, but but challenges like of that caliber of, like you know, you know, during covid, like parents and guardians who are literally dying and kids are, like we're like now, like having to go and live with their grandparents dying and kids are like we're like now, like having to go and live with their grandparents, um, like these incredibly challenging, like destabilizing, um moments that sort of like are are going directly, like, if you think, like mazel's hierarchy, like the sort of most basic needs that kids have, and we're coming in with something that is important, but it's, you know, it's in context important but it's not the most important thing for any individual kid.
Alex Kotran (aiEDU):If you go systemically, it's not even the most important thing. It's sort of on a long list. Bus driving is a great example of that. Many superintendents we've talked to. I think there's actually one example it's funny you mentioned bus drivers. I think there was one example of a superintendent that attended one of our sessions. We followed up with her. She was oh, we got a wait. This is wonderful.
Zarek Drozda (DS4E):I want to make sure we talk about school bus drivers, but I'm also very excited for our third guest.
Alex Kotran (aiEDU):I don't think Beatrix has any. I don't think she's ever even seen a bus. Oh, so her thing is she loves human water. I'll let her have mine. Oh, my goodness, I thought cats are not like. I thought they don't like water. Um, she loves water. She'll, like literally get into the shower. It's like you have to lock her out the bathroom because cj's laughing in the background. Um, his superintendent literally was like, yeah, we just had a school bus driver strike and I'm dealing with that right now. And so it's like, no matter how important you believe AI literacy is or AI readiness is, you kind of have to get kids into school for any of that to matter, and it's hard because there's nothing that you or I can do to deal with.
Alex Kotran (aiEDU):I mean we could shift our organizations and sort of like, try to figure out sort of like transportation logistics, but that's, that's a whole separate. You know, that's a different, that's a different organization that we'd have to build. And so I think, um, I don't have an answer to just like. It's really hard and and I think part of it is like not being aware what I'm hearing from you is like which I actually going for it.
Zarek Drozda (DS4E):I'm sorry you lost your water.
Alex Kotran (aiEDU):Yeah, I can't drink that anymore. Being aware of like that context, so that I think again though, like coming into the conversation as um, I wonder if we should cut this out. This is kind of cute. I really hope you don't cut it Um.
Zarek Drozda (DS4E):I think it adds the. The adds the unique brand of AIEDU Studios.
Alex Kotran (aiEDU):You know we're actually we're going to have, oh no, no, no, no. That's real, that's human water. No, no, no Okay.
Zarek Drozda (DS4E):This is also my daily experience. I'm not bothered. Does your cat drink water too?
Alex Kotran (aiEDU):Yeah, Really, what is this? Like whole subculture of cats that are actually into water and they're like sort of like a counterculture well, so my partner, I've been fostering cats.
Zarek Drozda (DS4E):We did that in chicago, so we get like a new you know cat, uh, every like three to six months. Um, I'm always shocked by how distinctive the personalities they have yeah, she's very she's actually like extremely friendly.
Alex Kotran (aiEDU):Um, we took her to the vet. It was on election night, um, so it was in the veterinarian's office.
Zarek Drozda (DS4E):Thomas was get off your phone, we're getting the first vet visit.
Alex Kotran (aiEDU):She was just having a blast. She was laying down and rolling over and purring. That sounds wonderful.
Alex Kotran (aiEDU):Yeah, it was not what I expected, okay. So, yes, starting nonprofits is hard, doing work in education is hard. I want to talk about data science, though, because I think there's something unique about what you are doing that I also see some sort of echoes in the work at AIEDU, which is it's kind of a simple idea Like a very, very specific intervention, right, like with more data science, education, and and you've identified like a, a really easy way for schools and teachers to start, um, which is like literally just get started, like use some introductory curriculum, like you don't need to completely overhaul your math department just yet. And I wonder, do you feel like there's, there's value in sort of like having like a? Really?
Alex Kotran (aiEDU):Because I think part of the challenge of education, it is so multivariate and complicated and or multidimensional rather, and complicated that I think people sometimes are just like throw up their arms and they're just like I just can't. I can't disintermediate any one specific challenge that I can solve myself, and so it's it's it's very demoralizing. Um, let me do you. Did that have something to do with? You know, I think the success that you had, sort of like building these coalitions and sort of getting the traction, the buy-in from what is now a pretty robust set of orgs that are sort of signed on as partners or sort of collaborators.
Zarek Drozda (DS4E):Yeah, so many thoughts. I think the first advantage that we had is we came in at a time when there had already been research and development on creating data science and data literacy, education, curriculum materials Prior to us doing the awareness, case-making, advocacy, field-building, state and district outreach work. There were NSF projects that started as early as 2013, 2015. There were classroom software tools that had been developed. It had not scaled yet. They were mostly in pilot stages. Software tools that had been developed had not scaled yet right.
Zarek Drozda (DS4E):They were mostly in pilot stages, but they were. You know, there was already a community scattered around the country working on individual projects in different locations or different domains. I think what was helpful is bringing all those people together in the same space with regularity and they started to see ways that they could collaborate. So that was the field building piece that was so critical, I think. On the on the specificity of the intervention, that was another very important factor and I think what has led to some some early success and emphasis on the early we like. I know we've talked about this at prior conferences, but our focus was very intensely on at the beginning, on just high school mathematics. It was a very narrow part of the curriculum. We thought it made sense in a particular location in a particular way. We had like two or three.
Alex Kotran (aiEDU):What was the?
Zarek Drozda (DS4E):first location. We did a pilot with the Khan Lab School and that was directly facilitated by our team. But prior to us there were many pilot projects district research partnerships. Los Angeles Unified was one of the first schools back in 2015 that started.
Alex Kotran (aiEDU):That was one of your first, yeah, but before we entered the space.
Zarek Drozda (DS4E):Right, this was a NSF funded project that was driven by researchers at UCLA and a few surrounding schools who thought that it actually grew out of the computer science education work and it was a sort of a spur out of that movement, and so these demonstration projects were really critical. There were so many people already working on high quality research and curriculum materials in the space. Then I think the specificity of the intervention mattered a lot, and then we grew from there. So we started with this really specific focus on high school mathematics. Then we had folks come in and say, oh, you should be looking at the science curriculum we would really benefit from where data was already like a fundamental part of the scientific process right, and any lab investigation you would do in school. But how could we dial up the focus on project-based learning and having real-world data that you could play with as a student instead of it being fixed in a textbook? Or how could you improve the way that students interact with middle school or high school labs? And then now we're on some really exciting projects. We're partnering with NCSS, the National Social Studies Educator educator professional organization, to look about technology ethics in middle and high school and that that'll be an intersection between data and AI and computer science and some of the other emerging technology areas to figure out what. What does a learning framework look like for for that content over time? But but we've moved in really piecemeal, targeted ways. That I think has been helpful. And then the other thing I think is we provide a really clear service to the little space that we are in.
Zarek Drozda (DS4E):So we are doing the field building work, we're doing the policy work, we're working the state groups and we don't ever intend to build curriculum. That's not our focus. There's so many folks out there who are doing great work, aied included, who have much more expertise in that arena than we do. We don't plan to go into professional development, at least for teachers. We're focused on PD for district administrators, for state leads, for all the folks who have to make those system-level decisions about student pathway design and what credentials should come where and when and how, and so that focus is pretty specific and we really believe in not reinventing the wheel. The last thing I want to do is start. My goal is not to make DS4E like a 100-person or a 200-person team with a $20 million annual budget or something at scale like that. We are trying to work with existing organizations and with existing professional orgs and with the state agencies that already exist, because our strategy is so focused on integration into the existing school subjects.
Alex Kotran (aiEDU):We're not trying to build a new know, just just as you all are not trying to build out a new subject arena I mean, so I hear you and I, I I think we've actually and I was just talking to a funder today and and I literally echoed something, I didn't give a number in terms of the ceiling of our, of our budget, um, but the way I described it is the the scope of, like the challenge that's in front of us is so significant, like there's it. It our strategy is not to try to try to build an organization that single-handedly solves all of it, because it would have to be, I think, way more than 100 people, like I think we're talking like tens of thousands, right. So I think that is um, and there are some non-profits that are at that maybe not quite 10 000 scale, but, like you know, tfa is about like 4 000 something, I think it's like 2 000, and tfa has like a couple thousand teachers that are actually like in schools right now and then like a bigger alumni pace, um, they're what? Like 200 million, something in the hundreds of millions, um, and that's a drop in the bucket. I mean, like one of the districts we work in has 10 000 teachers in just one district, um, so, so I think that there's power in that, because it also kind of like plants the flag for like the rest of the ecosystem, to like don't be threatened because where the goal is not to sort of like crowd out the space and like beat the competitors, just like a stupid paradigm.
Alex Kotran (aiEDU):But here's my other thing. Is $20 million is a drop in the bucket. Like to me, even if you were to say I want to stay small, $20 million should not feel like a big number, because what we are dealing with I mean a single district that you or I have met with can be at like the billion dollar annual budget level, um, and so I think that's like another place where in in the social impact space, we're like afraid of of scale and size and I think that's because just the I don't know I mean, we aren't rewarded for being ambitious.
Alex Kotran (aiEDU):Right, there's like, or there's a fine line to walk, so you're walking it. I guess I would just sort of encourage you, as my team has pushed me is like. You know, there is still a balance of like being. We need to push funders a little bit on this, because every single non-profit doing work in this space could double or triple in size and I think we would still be barely scratching the surface in terms of like, the relative size of like, the solution against the problem set that we're dealing with. But, point taken, I don't you're not wrong to try to stay below 100 maybe 50.
Zarek Drozda (DS4E):You know, you're right that it's the TAM, the total addressable market in the K-12 space, especially because we are a decentralized system with 50 states and 13,000 school districts and tons of intermediate organizations. At the end of the day, teachers and school leaders need more resources. The problem is huge in scale. I think there are the competitive dynamics that folks worry about are way overblown because of how large the problem is relative to how tiny all of our organizations are, relative to how much work has to get done. With our work at Data Science for Everyone, we're trying to actually promote some friendly competition and create a market for data science and data literacy uh solutions, right, and curriculum uh materials uh, more professional development teams who can come in and help the 13 000 school districts that we have across the country.
Zarek Drozda (DS4E):Um, and we, we launched recently a curriculum providers network where we're helping all of the providers that are working on data literacy education or data science curriculum with. Here's what we think the state is going to do next, and we see grant opportunities in this particular area and we think we should be pursuing them right now. Or we think that, hey, there's this really exciting NSF solicitation that just came out, and what if we were able to go in together and fundraise or try to do something jointly? And our team has been trying to promote a little bit of friendly competition, with that being sort of a ridiculous metaphor, because you know, we're in uh. Student enrollment for data science or data literacy programs is around three percent of high school students right now and yeah, we have 97 to reach.
Zarek Drozda (DS4E):Still, right, like there, there's so much room and there are the, the. I think, like the, the myth of competition is pretty, uh, significant in the space right now yeah, I guess there's.
Alex Kotran (aiEDU):I mean hearing you when you, when you talk about curriculum, it does make sense that that is an area where you want some competition. You don't want a any kind of like monopoly and you certainly I think there's value that comes from sort of like the productive struggle of like trying to, you know, like create the best possible curriculum and having better alternatives pushes organizations to do better. I think there's just the power. Dynamics in the nonprofit world are sometimes, I think, propagated by maybe I don't even know if it's funders' faults, I think it's actually peer nonprofits making an assumption that their fundraising is mutually exclusive to fundraising by peers.
Zarek Drozda (DS4E):I think, I think it is that exact norm.
Alex Kotran (aiEDU):And to me like there's, there's a few ways that like to break out of that. I mean one is I think funders can actually break out of that, that paradigm, by not making a decision that we are going to pick just one. But I think it also is dependent on the nonprofits themselves, and I think we've tried that. We tried very, very hard to figure out like, how can we, how can we set up our strategy in a way that like success for us, like almost by definition, means that we're not the only ones out there. And so I think there's actually you kind of have to create the conditions for funders to be able to fund peers. So I think if you go in and say we want to be the, the leading nonprofit that provides all the AI readiness curriculum or all the data science curriculum, then the data science curriculum, then the funders basically have to make a decision.
Alex Kotran (aiEDU):What you've kind of described to me with DS4E is, no, we're actually sort of like movement, building and creating, trying to catalyze the ecosystem to do this work, and so success for us. I mean it's hard to imagine success for DS4E that doesn't result in other nonprofits also benefiting significantly. It almost seems antithetical to what you've described. You need other nonprofits to thrive, by definition, or else you will have failed.
Zarek Drozda (DS4E):I think that's a beautiful setup, because what?
Alex Kotran (aiEDU):you've done is basically said as we raise more money, as we grow, our impact will, in part, be measured by the success and growth of other, whether it's organizations or even like school programs. Um, so I, and I think it's really, I think it's really beautiful and it's it's something that other organizations, the more that they kind of like buy into that, the more we'll sort of have created a system where the philanthropic community can kind of come in and figure out okay, how do we actually build all of this up together? But it does require some like like actually being intentional about being friendly. Um, because there is a little bit of you know like I think just, uh, and it's not bad intention, I think it's just sort of this assumption that you know well, I guess we're supposed to be competitors and sometimes it's because people ask who are your? I mean, I get this a lot lot who are your competitors? People have actually asked me about this.
Alex Kotran (aiEDU):With data science for everyone, ai education and data science. There's a lot of overlap there and there is, but I usually just wave away the question. It's just the wrong question to ask.
Zarek Drozda (DS4E):Yeah, I think it is the wrong question to ask. We just came back from our team retreat a week ago. You know we had the beginning of the year to have some in-person conversations about strategy and where to be taking the group next, and I said something that kind of freaked my team out. I was, like you know, the most successful version of DS4E is that we go away, we go home, we close up and we say you know, we were successful in convincing the field and catalyzing the field to take this new approach and caring about these skill sets for students in the curriculum as part of the standard experience and predictively. They're like what the heck, eric? Like you don't freak us out like that and because of the scale of the problem, we're not going to shut down tomorrow, right? Like there's so much more work to do.
Zarek Drozda (DS4E):But I also do take seriously that, like we want to create systemic impact that ultimately gets integrated in the system and you know that is our approach, right? So we want to be building sustainability amongst a number of organizations that are working with districts every year and training teachers every year. In the long term, it might be a number of the ed organizations, some of the innovative publishers, researchers, et cetera. It's going to be a lot of the folks that are already there. We're just trying to structure and really amplify all their work so it becomes self-sustaining.
Alex Kotran (aiEDU):Because you're not building curriculum, because if you were building curriculum then you would want continuity. But if you're successful, there will be this sort of thriving ecosystem and market of amazing high-quality data science curriculum providers and there will be sort of like the systemic demand and capacity to implement that curriculum effectively across schools. And they don't need the external support, almost by definition.
Zarek Drozda (DS4E):And I think like two things are important.
Zarek Drozda (DS4E):You know, first, I mean, before we ever formalized DS4E the group of us whether it's a number of the curriculum teams you know, myself and some of the other folks who are working at risk with Levitt some of the researchers were just meeting over Zoom during the pandemic that created a really trusting community where, instead of being outright competitors, everyone's actually been learning from each other and they've been like, oh, I figured this out with this district this semester and it was really successful.
Zarek Drozda (DS4E):Or like, hey, we're seeing this trend and we're kind of worried about it. Can we think about a solution to deal with districts being worried about their funding post-esser? We talk about that stuff as a group and it's so helpful. I also think the notion of our two orgs being competitors is a little ridiculous because, as you now know, our strategy is to bring more organizations and self-sustaining, high quality teams into the space. So AIEDU doing well is like a huge success for us, given we're in such, you know, so aligned in the commission and trying to help the system adapt to a really fast changing landscape and helping teachers figure out what do students need to learn, given the whole landscape is changing.
Alex Kotran (aiEDU):So I think that those notions are um fair questions, uh, but yeah, it's pretty quickly resolved um well, I mean, let's talk about, I think, like um one, maybe sort of a different way, of sort of like asking about the relevance or you know what competition looks like could go something like this this is something that we deal with a little bit less because the abstraction of AI readiness is harder to talk about. You're talking about data science. I've gotten a lot of questions from folks who are like, well, okay, ai is really good. The questions have been about computer science, but I think they apply to data science. Ai is increasingly good at coding and writing code. Should we be spending any time teaching kids to do something that machines are already somewhat competent at not necessarily amazing, but they're decent enough, certainly better than a freshman in high school, and by the time those kids graduate, it's probably going to, you know, if you extrapolate the trend line, it's probably going to be extremely competent at coding. Is it even relevant anymore? And I know this is also a legitimate question for data science, because you know I follow the subreddits and I mean there's a lot of people who are PhDs in data science who are talking about how you know the the o1 model basically completed.
Alex Kotran (aiEDU):This was actually I have a screenshot of this. It was like uh, o1 basically completed, um, my phd thesis. That took me a year to write in like an hour um, and so I guess I would. I would love to pose that question to you, like, I mean, how do you think about this? Like, if ai continues to be good and if we basically do find that, you know what it negates the? Um, the value of expertise in data science, because everybody's going to have a data scientist, sort of like, at their fingertips, um, will you even need to know anything about data science if, uh, if sort of like, that expertise becomes ubiquitous and accessible?
Zarek Drozda (DS4E):yeah, no, I'm so glad you're asking this because this this is like the part of the conversation I was looking most forward to is like diving into this exact question. So I think there's like a couple levels to this. First, like, there's the moving target of like, where is AI technology going to go long term? Right, and like, how good is it going to get at coding? How good is it going to get at writing? How good is it going to become at your screen and image recognition that enables autonomous vehicles to scale? And we're already seeing those things implemented, like at a really fast rate, right, and I think you have actually a very good pulse on this. Like I see you regularly posting on LinkedIn or you know other and Substack about. You know what the VCs are investing in, like what they're paying attention to in terms of you know the next big thing in AI. I'm also like a, like a they're paying attention to in terms of the next big thing in AI.
Zarek Drozda (DS4E):I'm also like a huge skeptic on the workforce implications of AI impacts. So I, yeah, I like and this maybe just like comes from, you know, like Levitt being a skeptic and everything in like my economics background, but you know we've been through multiple technology revolutions and, to a certain extent, a quickening pace over the past couple decades the internet, and then personal computing, and then the big data, which I think was really focused in 2010's Now AI. In terms of these technologies becoming marketed to the public, they've existed for decades, going back to the 50s and 60s, but even if you look back at the industrial revolution or you know other like massive tech changes, it's always created more jobs and in and I think some of the best forecasts that I've seen on the impacts of AI on the workforce are less about oh, this job is going to get replaced or that type of work is going to totally go away, and it's more about task composition within existing careers Right, so I think the and McKinsey has great research on this. There's a lot of good econ papers. Like, I think the the day-to-day experience of a coder or software engineer or someone working to build technology applications will change quite a bit, but I don't think it's going to go away completely. And then, within the job, like I think you still need to know how to code in order to find the errors that are coming out of AI tools, how to debug the thing that's in front of you. I think the work becomes more efficient, but it doesn't. You still need to like it's the perfect.
Zarek Drozda (DS4E):The perfect sequitur here is mathematics and calculators. Right, like, students learn how to do things on calculators, but we didn't get rid of arithmetic and we didn't get rid of division or multiplication. We still learn those things in elementary school and in order to know when tool's giving you the wrong thing and when you might need to go fix the tool or apply it somewhere else or customize it for the application that you're now in charge of and I think there are going to be so many of those examples across sectors where I think it's still important to have those skills embedded in the curriculum. The question becomes, just like in the workforce, where the time allocation might change or where the way that you approach the problems might change. No one's whipping out the graphing calculator on the floor at Google anymore, right, because we can do those things much faster on personal computers. And same thing with so many of my friends who now work as software engineers at tech companies. They can get through their day faster and, yeah, you can apply a rate of improvement to the AI tools long run.
Zarek Drozda (DS4E):But I still think you need the ability to work between the tool and your own knowledge in order to solve whatever problem is in front of you every day.
Zarek Drozda (DS4E):So, if you get back to the question about data, I think data has another thing going for it, which is AI. Tools are trained on high-quality data sets. You need to have a pretty deep knowledge of what's going in when you're building a new LLM or if you're trying to customize one in order to get the result that you want. And I think, like the, the, and that's just like a sliver of the competencies that data science covers in the education context. There's also civic implications that we're really, really, you know, focused on and really are quite passionate about making sure that students can, you know, effectively present arguments that are backed by data and there's clear logic and how you've you've found your sources and processed what data sets you have access to, and, I think the also the amount of of domain specific knowledge that you need, whether you're working in manufacturing or agriculture or you know, name your sector where AI tools might be starting to become more part of your day-to-day experience.
Zarek Drozda (DS4E):You still need to tailor it, and I don't think that piece is going to go away either, and so I hear the claims of like oh, maybe we don't need to teach kids to code anymore, because AI might be running at the rate of improvement that it'll just totally replace the need. I'm skeptical of the total replacement argument.
Alex Kotran (aiEDU):I think in your term that's certainly the case. It may be the fact that you don't need to learn specific languages. The specific language you learn in high school is perhaps less important than the sort of the wraparound skills of, let's say, sort of iterative problem solving and almost like that baseline familiarity so that you can sort of like look at lines of code and sort of identify where you know there might have been errors. Although Google is claiming 80% of the code that's written doesn't require any changes from human reviewers, which is kind of wild and I think fully 25% of the code that's written right now at Google is written by AI.
Zarek Drozda (DS4E):Well and so then the problem for us, right, is that we have a 12 to 20 year prediction problem. Because if you think about what are we, you know imagine we do a curriculum intervention at scale in Ohio. Let's take as an example you know the systemic impact that we generate today for today's kindergartner as a K-12 system to respond to a moving target of what skills and thinking habits and you know problem solving frameworks every young person across every sector is going to need by time they graduate, 20 years from now, and that is very challenging and I don't know if the field holistically has woken up to the scale and the complexity of that prediction problem.
Alex Kotran (aiEDU):Right, right, it's like I mean, all bets are off in 10 years and that is a timescale that we're looking at. And I was talking to someone who I really trust, someone who's a very early thinker and investor in the future of work, and he was just like, honestly, if anybody tries to tell you what's going to happen in 10 years, what the jobs of the future are, almost immediately discredit them Legitimately. Everybody who's really paying attention to this does not know. And David Autor is one of the economists that I'm always talking about, because I just think he is incredibly thoughtful.
Alex Kotran (aiEDU):Take on sort of this conversation about expertise and jobs. And even you know, if you I've watched a bunch of his lectures where there's a Q and a section. People try to press him to like kind of like articulate, like what does this actually look like? What are the jobs that get replaced? And and he really demurs, he doesn't, he doesn't give a concrete answer and I think that's you know it. It is it's easy for us to gravitate towards like a really pithy, easy, sort of like a sound bitey response to that, but those seem most obviously the most most probable, uh, wrong predictions of the ones where we're like overly specific.
Zarek Drozda (DS4E):so yeah, we don't know, but I mean it seems well. But I so I also think like humans are like famously horrible predicting things, right, like we suck at predicting the future as a species and like the behavioral econ research just shows this again and again, like we're very bad at predictions, um, and so I think, with that framework, then what I look to is what you go much longer on your timescale Literally look at the Industrial Revolution and look at the impact of the space race and look at the impact of the Internet's introduction. That is the timescale at which the K-12 system should be thinking about, not the year-to-year changes of OpenAI's next GPT model. That's not going to be helpful for us. And when I look at that time scale and you apply like the just like the creative destruction framework, it's, you know, reliably the case that, uh, new technologies always create new types of jobs and the rate of automation is always slower than people predict it will be in the moment-out moment when people are responding to the new tool. That's a bit of a solace. And then I think, also on the longer time scale, you can find things that will be pretty reliably permanent, and I think, students knowing how to use technology devices and being able to adapt to the next tool over time.
Zarek Drozda (DS4E):That's a permanent idea. We're going to continue to have tools that we have to jump between, and so I'm very invested in thinking about how both of our work can support students jumping between adapting to new tools. And I think data is going to be permanent. We're still going to need data on a variety of problems 20 years from now and be able to work with it articulately and be able to extract meaning from it, regardless of what the tool is. That's a that's interacting with it, um, and that I'm also pretty confident in, um. You know like the exact, like software move that I use to merge two data sets with a csv file. You know who knows what's going to happen with you know that might we might develop a crazy great solution that automates that that someday, um. But I think building the intuition to transfer and practice those computational moves will be super important yeah, this is um.
Alex Kotran (aiEDU):I studied history in college and I've I've been kind of obsessing about sort of like the historical look at um industrial revolution and sort of like past, the way automation has played out in the past. I mean, the first thing I'll say is, like past performance is not an indicator of what's going to happen in the future, and so it's not and I think I'm quoting a tour again it's, it's not a rule that automation has to play out in a way that is slow enough to allow a society to adapt. That's A B, I think. Actually, if you look at past industrial revolutions, yes, it's the case that more jobs are created than destroyed. So the net outcome from automation has been generally really good, but it isn't necessarily always good for everybody who gets those jobs, but it isn't necessarily always good for everybody who gets those jobs. And, like I think about, I've been obsessing a lot about sort of you know, seamstresses who were, you know, replaced by the loom and then the steam engine and steam factories, and you know, yes, they got new jobs but they were working in like dark, windowless, smoke filled factories and it doesn't necessarily give me I don't get that much comfort from the idea that well, everybody's going to be employed in some way. Because I think, actually, the question is like, how do we make sure the kids are actually able to command some kind of economic value from the skill sets that they have? And I think that's what brings me back to both computer science, data science, mathematics, like rhetoric, and the humanities. I mean, I think, um, if, like, if all else is equal and everybody has access to the same ai tools, then the differentiating factor will be how much expertise you can add on top of that, and and, yes, we don't know exactly in what form that's going to look like, right, but, um, I, I completely agree with you in the sense that you know the, whatever the digital divide looks like. The kids who are on the right side of that are kids who are probably going to be going to have done a ton of stuff that goes way beyond prompt engineering, and they're gonna, and and, yes, uh, because ai is like, fundamentally, you know data science, um, you know it.
Alex Kotran (aiEDU):It seems quite obvious to me that like understanding sort of like the, the, the, the, the bones of this technology, and like how it works and being able to actually talk sort of meaningfully and thoughtfully about, you know, biased data sets, cause that's like a buzzword now algorithmic bias, um. But at the end of the day, like what that really is is data science. It's like actually like being able to talk about like what are, what is the data that went into these models, how is it weighted and and how do you look at the outputs and sort of like think critically about that. You can get kind of far by just using ethics as like a lens and like sort of like social studies as a lens. But at a certain point, to get to any level of depth and sort of building like a thoughtful perspective on that, you kind of have to understand how data works.
Alex Kotran (aiEDU):Um, computer science is like slightly, I think it's like in that same vein, but it's like a little bit I'm less convinced about data science than that. Like every single student is gonna absolutely need to have. Um, I think way right now, it's like five percent of kids in the us are in an introductory computer science class. I have no idea what the number is for data science we're around 3%.
Zarek Drozda (DS4E):We actually just collated our first estimates and so, yeah, a couple important things that I think you brought up. Just first, on the automation effects I think that is what does add urgency to our work is it's the transition of jobs or who is competitive. Entering the marketplace is going to be super varied by community, by how fast we were able to help our curriculum change and help our students get ready for that, and like we need to talk about our both of our concurrent Ohio roots. Yeah, right, because, like I saw that gap within my own family in a huge way and I think that is a part of what continues to motivate me. And then, on what skill does every student need?
Zarek Drozda (DS4E):We are really invested in making sure every student graduates data literate, which is different than taking the setting the bar low advanced, well, different than, uh, taking that advanced high school course in data science techniques for how to merge a data set or how to, um, you know, uh, do other technical moves that are going to set you up really well for today's job market and will help you transfer later on. Um, but you know, we like being realistic, like not every student is going to take that elective course right, and so we know that we need to do some deeper integration work in the core subjects in order to make sure that every student gets a flavor of this before they graduate, and I think that is the universal thing that we're really invested in to make sure we mitigate digital divide.
Zarek Drozda (DS4E):that is only going to exponentially compound the more dramatic that the AI cycle grows.
Alex Kotran (aiEDU):Yeah, and setting the low bar is not that wasn't meant to be deprecating. I think it's actually really important to do work to reach the kids who are at the back of the line, kids in low-funded schools, kids in rural schools that don't have any honors tracks, or in rural schools that don't have any honors tracks, or in many schools I don't even have a computer science teacher, let alone a teacher that's figuring out how to integrate data science. Um, you know you have to set the bar low because right now the bar is at zero and I think no matter how amazing it would be if every student did go through some sort of like ap or honors data science class, like, if you set the bar too high it will scare away a lot of schools where it's just overwhelming for them. And like, the more you sort of create some like really tangible, easy ways and that's like totally the thesis of our curriculum, curricular thesis is like stepping stones and giving teachers like easy ways to dip their toes in the water and then sure they can sort of graduate.
Alex Kotran (aiEDU):Greenwich County I don't know if you've talked to the folks at greenwich county public schools, but they have this metaphor um, swim, snorkel, scuba, and this is a reference to ai literacy, but it's like, you know, some kids, everybody needs to learn how to swim. You know, a smaller group of kids will go on and snorkel and an even smaller group will go on and scuba dive, and I think that could apply to computer science, I think it can apply to data science, where it's like everybody should know how to swim. That seems sort of unequivocal, and especially in today's world where kids are talking about the algorithm they're talking about, they know that their life is being shaped by these models, but they don't really know what the algorithm is right. They, they sort of like, have this colloquialism, um, and I think there's a lot of power just giving them some sort of fidelity around, like what that actually means and like how they can start to make you know, if not make informed decisions for themselves, at least understand the world around them and understand why things are happening, because I think there's a lot of um, uh, it can be a very scary place when it feels like, sort of you don't have control over, um, these technologies that are increasingly becoming, like you know, fundamental to the way that we communicate and live, um, it feels kind of silly to say that now.
Alex Kotran (aiEDU):But I mean, were you? Were you in? When did facebook come out for you like? Do you remember when you joined Facebook?
Zarek Drozda (DS4E):Well, before I answer that revealing question, I was going to go back to something you said on whether we're lowering the bar or not, because when you say that, I take you to mean differentiated between the level of challenge teachers don't like using the term rigor, right, but the level of rigor or challenge or depth of the content versus the dosage.
Zarek Drozda (DS4E):And I think the strategic misstep is I think you know we've done, the system has done so much over the past 20 years to build out computer science education as a new school subject, right, and I think we need to actually continue making those investments because that's 20 years of human capital development and teacher training and people in school buildings who have pushed really hard for technology education that we need to continue to invest in.
Zarek Drozda (DS4E):And I think the strategic missteps that I'm increasingly observing that CS made as a movement was over-investing in the students who are going to become CS graduates at bachelor's programs to then go work for a technology company, and under investing in the I want to call it like the mid-level you know well, this is the classic like computational thinking, computational literacy versus computer science versus CS debate, right, and I think they under invested in the literacy side of the spectrum as a from some of the field building work that was done, and I think they underinvested in the literacy side of the spectrum as a from some of the field building work that was done, and I think that meant that they there was an underinvestment in cross subject integration, which meant that we have students who are still graduating with identities where they don't identify themselves as a person who's tech savvy or who is good at math or able to access stem right, and that is a perpetuating cycle.
Zarek Drozda (DS4E):Um, and I think that that's a huge challenge that we need to continue to fight, uh, and really make some huge progress against over the next like five years or you know, with some real urgency. Um, what was the question?
Alex Kotran (aiEDU):my question was yeah, just trying to like, because how old are you?
Zarek Drozda (DS4E):this is a a classified uh question that I've only revealed to two people on my team for uh. Really who have drawn it out of me over time. Okay, so don't answer the question. No, no, no, I'm 27. 27. You're?
Alex Kotran (aiEDU):27. Okay, so how old were you when you had, like, when did Facebook come into your life? Like, I'm using Facebook as sort of like that, like barometer of like when social media kind of blew up. I mean, myspace was actually a little before my time. I assume you weren it to my space.
Zarek Drozda (DS4E):I, I, um, I was a earlier adopter of Facebook, though. I got a Facebook account in middle school, okay, or which was like oh, seven or something, yeah, yeah, that seems right.
Alex Kotran (aiEDU):Yeah, um, I mean it's interesting, right, like I cause so you still have like some memory of what it was like growing up before.
Zarek Drozda (DS4E):yeah, a little bit, yeah, tiny amount it but but in few of my classmates had it um my. My parents were a little bit more permissive in terms of my online access interesting.
Alex Kotran (aiEDU):Do you? Do you think that that had anything to do with your like? Did that help to feed your curiosities and help you nerd out? Or was it honestly and like it's weird the parents out there who are wondering should I be reining in my kids? Yeah, yeah, yeah.
Zarek Drozda (DS4E):So today I don't actually spend that much time on social and the part of my day where I go on LinkedIn and give a status update about our work is my least favorite part of the day, because I just hate posting on socials and I was on it early but I think because it didn't feel like something that was prohibited to me, I didn't make a big deal about it. You know it's similar to like uh, but my parents took a similar approach to like alcohol. Like I was, they would regularly let me like drink wine with dinner and then I never felt uh, like there was some sort of like yearning for it. Uh, and then and then in college, right. So I do worry about that. I worry about the, the bands and the prohibitions around social actually backfiring in, in, in.
Zarek Drozda (DS4E):With that mechanism in mind, I don't have an opinion about the overall policy, but like that, that's just something, you know, something to consider Now as as a the Now as the effect on my dispositions or daily life. Again, I don't think I generalized super well because I was a super nerdy kid so I was not using social media as a determiner of my social worth or identity with my friends and, to be fair, it was not at that level of importance yet when I was first. Like when I got first access to it initially and my peers, like it was in person, socializing was still de facto the mode and it was an add on. That was a fun activity or a hobby to do on the side. I didn't think it was like the end all be all, which it is, I think, in many ways now for for young people.
Alex Kotran (aiEDU):Yeah, I mean I'd be curious. I didn't think it was like the end all be all, which it is, I think, in many ways now for for young people. Yeah, I mean, I'd be curious. I don't even know anymore whether social media because I think the paradigm was early on, you know, social media would be sort of this indicator of social worth based on how many likes you had. But I actually think the paradigm of like likes is sort of gone.
Alex Kotran (aiEDU):I think most, most of the usage of social media right now is sort of like this kind of like mindless brain rot, scrolling where you're not really like. It's actually it's interesting because at least before there was a sense of like OK, you're sort of like it's generative, like you're creating something, you're creating posts, you're sort of like telling a story about yourself and there's obviously like ways that that's really bad for your mental health, but nonetheless it was like in some ways almost a creative outlet and there's obviously some small subset of kids who are creating content. But my, my sense is that, like, when most people think about social media use today, they are scrolling and they're doing this and it can sort of like you can spend like two hours sort of just going through and my latest one I use Instagram because I like chat with friends and my my feed is, I think, because I have this like the curiosity, like all the weird corners of Instagram, I get like really, really weird stuff and like the latest one was this influencer with like literally hundreds of thousands of followers that like steps into like macaroni and cheese and like different pastas and it's just sort of this like ASMR, I guess, of macaroni and cheese and like different pastas and it's just sort of this like ASMR, I guess of like feet and pasta and maybe there's some sort of like sexual undertones in that. I don't really know, but it's like I. I look at that and it really it. It scares the hell out of me because it's it's like I.
Alex Kotran (aiEDU):I like I don't understand it and I don't understand it. And if I don't understand it, how the hell is some kid supposed to make sense of this? And yet they're sort of like, almost like, I think. Certainly, if they don't understand how this all works, then it can almost feel like you are a slave to sort of like whatever the algorithm sort of like spits out at you and one of our activities is actually like, how do you train the algorithm? Or it was. I don't know if it's actually still on our website anymore. I think we might've taken it down because we're moving away from like child, like direct, to student content.
Alex Kotran (aiEDU):But, when we had some direct to student content on our website, there was this activity that was, like you know, challenging kids to try to train their TikTok algorithm to feed like certain types of content that they wouldn't normally so, like owls or, you know, badgers or things like that. Um, and the goal being to help students understand like the different levers they have when they're actually engaging in content, like when they comment on something or share something, like those are all going to perpetuate more content like that, and so, while it might feel like I'm getting all this weird sort of like pasta content with feet, it's probably because I'm sending it to my friends, and I know this is the case. I'm sending to my friends, like look at how crazy this is, and my friends are commenting, and so Instagram is like oh, alex likes this really crazy stuff and my ads also are really sort of like off the deep end.
Zarek Drozda (DS4E):Are they pasta and feet themed now?
Alex Kotran (aiEDU):no, I got water fountains for a while like, literally like industrial water fountains, as if I was looking to install it yeah yeah, good kitchen edition, or something.
Alex Kotran (aiEDU):Yeah, but data science to me is it's like an abstraction that maybe it's hard for a teacher to intuitively figure out how to have that conversation and excite students and connect the dots, but I feel like that's a really powerful opportunity in front of us. Social media is crazy. The world is crazy. Data science actually is a way of understanding why some of the stuff is like playing out and it also gives you like a little bit of a, like a shield, because you can contextualize, like when something's happening to you, like like what you have, agency, what you don't. But I'm, I'm in it, you're really deep in this, like how, how easy or hard have you seen teachers kind of like connecting the dots? To simplify my question Do you feel like teachers generally understand or really need to help conveying the relevance of data science to kids?
Alex Kotran (aiEDU):who are not naturally inquisitive and like, let's say, math curious or like really excited about math. I'm sure those kids, um, and you were probably one of them you know they didn't have to actually work that hard to get them excited, but for most kids myself I would have been one of these. Yeah, do teachers kind of figure this out, or is that one of the areas where you feel like they need help?
Zarek Drozda (DS4E):Yeah, you raise a good point and I have a story and then a tweak of your argument. So the story is I worked in the administration for a year and I worked at US Department of Ed and I was there as a fellow researching emerging tech and I remember having this conversation with one of the program officers who runs Gear Up Gosh, she had this great advice and she was like this sounds fun or this sounds important. I can tell you're excited about this topic area. There's a huge risk that this could be boring and you better, gosh darn, make sure it's fun for kids, or else you're not going to get anyone to, you're not going to get any traction and it's not going to sustain itself.
Zarek Drozda (DS4E):And I think that's totally right about, uh, data science, ai, computer science, cyber security, like all the emerging technology areas that seem a little bit esoteric, and so we've been really focused on how to capture that. Now the part where I'm going to tweak your argument or just tweak the framing a little bit. Our team is obviously super invested in the importance of data literacy for all students and in creating introductory data science experiences. I personally and honestly I think our team, is even more invested in making sure that the student experience in our core subjects like mathematics or science or English is really relevant and engaging to kids, and we see data science or data literacy education as a vehicle to help modernize some of those experiences for students rather than the. So you know right relevant math becomes before data science.
Zarek Drozda (DS4E):For our team and and we think it's actually a way to teach math or teach social studies more engaging pick up some tech literacy skills, get some technical software experience. Um, and what we're really excited about is with data science, the data science education approach in particular, is it? It is so, so, so easy for a teacher to make a lesson plan customized to student experiences through swapping in and out the data sets. Is trying to teach exponents or polynomials or a linear model in math, or they're trying to get their students through memorizing a set of battles in history class Often go to the textbook lecture, do rote procedures right.
Zarek Drozda (DS4E):What if, instead, you surveyed your students, asked them what they saw in the news last week, go find a data set on NBA scores or from the game recently, or you found whatever's trending on Spotify through the data set and then have them. And it obviously has to be structured in a way that gets you to that concept, but you're able to create an engagement strategy with students by customizing the content that they're working with and the concepts that they're applying on. That is the fixed learning goal. That's already in your standards and that's what we're really excited about. One of our upcoming projects is going to be to build a data set hub of really exciting topics that are relevant to today's teens. There's already a way to get Spotify data through an API and just report it into Google Sheets.
Alex Kotran (aiEDU):Gosh, I would love to. I mean personally, like I would love to get my hands on that. That'd be so fun.
Zarek Drozda (DS4E):And so many students already have access to the G Suite or into Google accounts and it's not, you know, not every district, but many can access Google Sheets.
Zarek Drozda (DS4E):You could repeat that approach through a classroom-specific software and you preload a call to MBA scores or whatever is trending on Pinterest and you can create custom projects that are really relevant for students, where they can chart their own path in the project that they're given, as long as they are still learning how exponents work.
Zarek Drozda (DS4E):And that is the vision of how we think the integrated approach for this might look like long-term, and we think that the possibilities for getting student engagement up, getting them to see why technology skills and, you know, information literacy and be able to retrieve stuff on the internet is is super easy and feasible for them to do, connected with the formal concepts that they don't often don't see, come to come alive in the same way, um, so so that the approach is slightly different, right, like we're excited about, like students learning, uh, the theoretical approach of data science or how, um, algorithmic bias is, you know, uh, really a result of skewed data that you put into a training model and and having that theoretical lens, we also want to see that engagement and customization come to reality.
Alex Kotran (aiEDU):It's, um, I mean, it makes a lot of sense. I, I do, I do sort of. I think what we have run into is like that vision is really compelling. Um, some teachers totally get it and all they need is just the resources at their fingertips and maybe a little bit of like a shove out the door. Use the Lord of the Rings analogy Um, we're actually a Hobbit, um.
Alex Kotran (aiEDU):But I think there's other teachers that, like what you're describing is, still feels insurmountable, like they wouldn't know what to do with the data set and Um.
Alex Kotran (aiEDU):But I think there's other teachers that, like what you're describing is, still feels insurmountable, like they wouldn't know what to do with the data set.
Alex Kotran (aiEDU):And you know, for teachers, they're sort of I think the expectation or their belief is that they need to sort of be the experts in the classroom, and so if they don't have mastery over something, uh, they, they really lack the confidence, um, and are hesitant to implement it. And so I'm curious, like how hard is it to get a teacher like especially, let's say, social studies, where they're not math people not all, but let's say like many of them, right, like specifically went into the humanities because they didn't like math and like, how hard is it to get them to a place where they sort of have the comfort level to be able to, like, figure out, oh, like, this is how I could use a, you know, a pinterest or spotify data set and connect it to the standards that I have to teach so I think this is why we need to build off-the-shelf curriculum solutions in a decentralized way that connects to this ability right.
Zarek Drozda (DS4E):And this is like another example, like ds3 being an intermediary organization, where we're trying to build an ecosystem or of an approach, rather than like doing the direct content work ourselves, and we're building of a community of organizations who can do that work to make it easy to run off the shelf so you can grab a lesson plan that's connected to your standard. You have to teach two weeks from now and then you can slot in a recent you know up-to-date data set or activity that connects to that idea, but it's already pre-programmed to slot into the classroom experience.
Zarek Drozda (DS4E):That's what we want to get to, and I think that's actually possible. I think we're not that far away from having that reality, and this is going to be differentiated by school subject. I think that's. The other big thing that we're super focused on is not creating the same solution for every school subject area the social studies experience, the dispositions and the thinking habits that you learn in that context, and what that teacher is confident in covering and is really excited to impart to their kids super different than what the math teacher is focusing on that week, their kids super different than what the math teacher is focusing on that week. Right, and so we're.
Zarek Drozda (DS4E):We're in this process of building national learning progressions for k-tall data science and data literacy and we're building them pretty granularly by grade span. We're building the pure version first, where it's, uh, it's subject neutral, uh, we're just thinking about data science and data literacy in a vacuum. But that's not the end of it. We're over the next, uh. The second stage of the project, which we're going to kick off in the second half of this year, is then building subject aligned versions. So there's going to be a math version for data science and data literacy, a social studies version, a science version that will solve that challenge of? Oh well, okay, this is all fun and great and seems a little bit complicated, but I have to teach mitosis for the next three weeks. What am I going to do? We want to make that tailored to each of the subject areas and their approaches, rather than trying to make a one-size-fits-all.
Alex Kotran (aiEDU):Yeah, this is so similar to our approach.
Alex Kotran (aiEDU):I think we're a little bit less.
Alex Kotran (aiEDU):I don't think we've actually thought through what the marketplace of AI readiness curriculum looks like, because it's a bit more of an abstraction where there's less clear goalposts, and so I feel like we have been working a little bit more on the front end to kind of define what it even looks like.
Alex Kotran (aiEDU):But I think we've also landed in a similar place where our goal is not to provide the end-to-end solution for ai readiness and going back to this idea of like, not trying to be the one with the one organization right, like, like our curriculum will be sort of a like, sort of exemplar for teachers to use as a reference and to get them started. Um, and and I guess that's where like a little bit of competitiveness makes sense you actually want some curriculum to be surmounted by others because it's actually more engaging, and you want to have a system such that there's actually ways for better curriculum that has better outcomes to rise to the top. I'd be really interested in the platform that you're building, because it seems like a really powerful way to get this thing going. You were going to say something.
Zarek Drozda (DS4E):Well, do you want to hear my crazy idea? And so this is the first time I'm sharing it, so you get to be the first and then you know whoever whoever's made it to this point in the in the episode can can also listen. I cause he mentioned, see, not having like a. There's not a large marketplace, right, like in some ways, like you all are one of the primary and there's a few other curriculum development projects out there, but it's really early days and just it's just like reflecting in just where the landscape is right now and all the competing demands on the curriculum. I think there needs to be some sort of like confederation between a lot of the modern literacies that have emerged as part of the toolkit for students to graduate from high school that combines data literacy, ai literacy and the computational literacy or computational thinking, but then also media literacy and financial literacy and civics literacy or civics education, and financial literacy and civics literacy or civics education, and I think that confederation of practical everyday skills.
Zarek Drozda (DS4E):Those movements have been operating separately from each other in silos and in some ways competing with the school schedule, because the financial literacy folks come in and say, oh, you should teach financial algebra, and then data science comes in and says, oh no, no, no, you should be doing more stats modeling.
Zarek Drozda (DS4E):And the other thing in the third year of high school, focus a little bit towards more application and practical skills and thinking frameworks, rather than the very theory and procedure-based curriculum that we generally have and knowledge-based curriculum that we currently have. And it's not a complete replacement, because both of our orgs are really focused on integration at small to medium dosages. We're not trying to build a whole new school subject area and I think all of those movements are generally aligned on this idea of, given where the world has moved, the curriculum at large has not kept pace with some of the larger shifts that we've seen and there needs to be a redialing of time allocation within the curriculum, and so I don't know how that works at the operational level and I think a lot of it might start actually with like joint advocacy work and probably becomes most important at the state level when you're working with state level leaders who are writing state standards. But I think there's other manifestations of that that could grow over time.
Alex Kotran (aiEDU):Yeah, it makes a lot of sense, I think AI literacy and we think about it more as AI readiness, which is sort of like AI literacy plus sort of critical thinking skills, and that's where, so I think, our curriculum really neatly fits alongside computer science and data science, in part because when we talk about readiness, we're talking about critical thinking skills, which are, and like you know, iterative and creative problem solving, which are actually really effectively built via data science and computer science education, and so to me that's a shoe in. I mean, I have a wondering about this because I think there's a, there's a danger in trying to go too big, too fast and this sort of like to our to our earlier point about sort of like baby steps and like making it as easy as possible for schools to opt in.
Alex Kotran (aiEDU):I think the more that this becomes sort of like this wholesale re-imagining of school, it's like we need to get there and so I just have this I have an anxiety about you know, there, and so I just have this.
Alex Kotran (aiEDU):I have an anxiety about you know, if the goal is to sort of like reach as many students as quickly as possible, um, there that perhaps there is danger and like thinking too big and too ambitiously, um, and yet it seems so obvious. Right, it's like you like financial literacy and data literacy seem extremely connected. Um, civics education and AI literacy and AI readiness are extremely connected, and arguably, maybe that's one of the most important areas for schools to be honing in. I just don't know if schools have a demand at all for civics education right now, and so there's almost a risk of we have this sort of AI zeitgeist moment. I think data science benefits from that. I just don't know if this becomes sort of of like do you want to shoehorn everybody in and try to bandwagon it, um, into schools and at some point will they just be like whoa, like we, this is, this is. You know they always have like bad experience. I don't actually, I need to do more research into civics education. That's probably my next, if you have any recommendations for any papers on this, because it's kind of like it wildly failed. I mean, like the amount of schools that have taught civics has like decreased significantly, and that's despite people sort of like increasingly talking about how important this is and how relevant, you know, civics is to day to day. I think we're going to cut this part anyway, so let's go back to no.
Alex Kotran (aiEDU):I think it's a really admirable idea, I think in the spirit of baby steps, you know, data science, computer science and ai literacy seem like the obvious places to start, and and I think what I'm hearing from you is also that none of this actually has to happen at the expense of some of these other sort of like fundamental parts of like operating in society, not just as a worker, but as a citizen, as a consumer.
Alex Kotran (aiEDU):Most people agree that we need to have these things, and so I guess, as your point, take this as an opportunity to kind of seize the momentum that the AI revolution has sort of created, where people are ready to actually talk again about reimagining school, when, I think three years ago they're like literally they had thrown their arms up in the air, and this is like you know, during covid right, people were like code was almost like the straw that broke the camel's back and there was just like no interest anymore and sort of like dealing with any of this other big sort of big thinking stuff, um, but now people are having those conversations again yeah, and I think that and I and I'm all for leveraging the AI hype investment cycle that that technology community is in and I think we both need to be both of these movements, these ideas and all those practical high school toolkit skills need to be leveraging the attention on AI as a technology to get overdue ideas into the curriculum, including literacy about AI, because we know that's going to be a 20 year thing that we can count on, that it's not going to go away, and so I think it's a huge opportunity for the whole space to be thinking about.
Zarek Drozda (DS4E):Yeah, and totally hear you on going too big, too fast with this like five literacies confederation idea. I think it has to start in baby steps and I think it can start in baby steps in a few specific areas, and I actually think you named one of them, which is learning about prior movements and prior field building and prior just education, you know, reform efforts jointly, rather than doing in silos, like my team's been doing this recently, like looking at, like other organizations or your prior movements and trying to see, like, what we can learn from them. It'd be good to do that in conversation, right, like we shouldn't be doing that in isolation. Are you gonna publish any of your? Is this sort of like internal research?
Zarek Drozda (DS4E):they're like they're like lunch and learns okay, like do on Google Slides. It's not at an academic level, it's at a super practical level for building an org strategy or a roadmap for a year or setting OKRs or those types of things. And I also think that there are some opportunities when you go to a state standards process and the Department of Ed is looking at doing standards revision across three subjects. That should be a collaborative project where everyone is able to see their framework getting integrated into the space. They think it makes most sense at the same time, and I think that would help district leaders then respond to cause.
Zarek Drozda (DS4E):What's happening in the status quo is that all these different things are coming in, uh, piecemeal, at different times. Yes, one district administrator gets excited about one topic and then that person leaves and another one comes in and it switches. Um, or the math person is working on data science and the, you know, the CS person's working on data science and the CS person is working on AI literacy and the social studies coordinator is looking at technology ethics, and it's happening in isolation because of the way that the curriculum is siloed, and so I think that's where there are opportunities in pilot scale to think about some coordination, but hard to do when all of our teams are small nonprofits that are bandwidth constrained. There's also that reality that I think makes it tricky in practice.
Alex Kotran (aiEDU):Um, so for, for anybody who's managed to get this far along in the conversation.
Alex Kotran (aiEDU):Um, you know, my guess is that they are uh pretty motivated by the work that you're doing and they might be asking themselves, like what can I do? And maybe they're they're even thinking about, like, oh, I want to try my hand at creating some curriculum, like what is the action that someone can take to plug into your movement? Um, and maybe you can share for different personas, like whether it's a teacher, administrator, like some of the different folks, like where do they start?
Zarek Drozda (DS4E):um, so if you're if you're a teacher, you should, uh definitely check out our resource hub. That's like the easiest on-ramp. We have a library of online lesson plans and curriculum resources Everywhere from like.
Alex Kotran (aiEDU):I don't know. Give you the website. I don't know.
Zarek Drozda (DS4E):From a lesson plan to like a whole high school course, the whole spectrum is there.
Zarek Drozda (DS4E):What grade bands, all grade bands. So we have K-5, middle school, high school and we're going to get more granular over time If you want to get more nerdy and you have some extra time on your hands, which is like a. You know that's already a pretty small set of folks in the education space. But we're going to release the draft learning progressions. We're targeting the summer for the full public release and there'll be a few feedback rounds leading up to that.
Zarek Drozda (DS4E):We want folks to suggest amendments and we're going to have a process where, either every one or two years, the national learning progression that we create for all you know, between K2 to 910, gets updated as new education research emerges and as technology changes and as some of the tools change. And so our idea is to create an adaptive version of standards rather than have it be updated every seven to 10 years, which is the typical state cycle, so that the exemplar for all the states and districts of draw-in at least is always up to date. And we also want to make this the point at which there's really clear, you know, research to practice translation. So we're going to be incorporating both teacher anecdotal evidence of of real classroom experiences and also like published academic research and have those two be the justification for like an amendment that anyone could propose, as long as it's, you know, like data sharing.
Zarek Drozda (DS4E):Of course.
Alex Kotran (aiEDU):Thank you, you got the fun um excellent. Well, zarek, I mean I I am excited to actually dive into. I mean, especially, let me know when the spotify data set is available, because, um, literally, like with my friends, we've talked about this like how we would love to get behind the scenes I don't know, I didn't realize how easy it was.
Alex Kotran (aiEDU):Um, or potentially soon to be easy, it will be. Yeah, thank you so much for coming on. It's very cool to have you. You're like five blocks away from me, which is exciting, so I'm excited for more walk and talks and maybe even having you back here and, yeah, guest number like three maybe so yeah, we have, I think, as of now, three followers, so it's going to be huge.
Zarek Drozda (DS4E):We're going to make some waves, got to roll out the red carpet but, seriously, thank you for inviting me into this. I'm truly honored to be one of the first as you build out this series, and I think it's important, right, like I think it's important that the field, you know, has deeper conversations like this, like you said, in longer form, because we covered so much ground, I was able to share like three crazy ideas that I've been trying to tell you about for a bit, and you know, now we got to do it in this, in this format, and I think, uh, talk about it in a super reflective way. Um, I hope we have more opportunities to do this. This is great.
Alex Kotran (aiEDU):And and also I would love for ideas from you and, um, maybe any one of our three followers to. If there are other folks we should be talking to that, especially teachers, especially folks that have actually gotten their hands or gotten their hands dirty, rolled up their sleeves Increasingly. I think it's gonna be important for those folks to get a spotlight, because at a certain point you're just sort of like talking amongst yourselves and so it's a very small again like arcane community of you know, social impact nonprofits again like arcane community of um you know, social impact nonprofits. The education space, like I think. I think if we're all in one gymnasium it'd be like a couple of hundred people, you know, um, probably bigger than that, but not that much bigger Um, maybe just go to ASU GSB Um. Anyways, derek, such a, such a pleasure, um, and you know, hopefully have you back on very soon.