[MUSIC PLAYING] Welcome to Ejakast 3000. It's the most transformative time in the history of education. So join us as we break down the fourth wall and reflect on what's happening, the good, the bad, and even the chaotic. Here's your hosts, Melissa Lobel and Ryan Lufkin. Hello, and welcome to another episode of Ejakast 3000. I am your co-host, Ryan Lufkin. And I'm your co-host, Melissa Lobel. And today, both Ryan and I are super excited to have our guest join us. We've known this guest for quite a long time and are really inspired by the work he's done. So we're thrilled to have Sunday, Shridasta with us today. He is CEO of Vocarium, but has a long history in both technology and education. And so we're going to pick his brain today around the intersection of technology, education, the future of work, and coding. And what does this all mean for educators and how we think differently about perhaps what the future of teaching and learning looks like? So Sanjay is so good to have you here. Thank you so much. I'm really excited to be here. Thanks for inviting me. Awesome. So before we jump into the hard-hitting questions, give us a little bit about your background. So our audience gets to know you a little better. Yeah, sure. So I took a somewhat normal path to Silicon Valley, which I did my undergrad in India and IIT. And then I was very, very fortunate to get invited to the computer vision lab for my PhD in University of Illinois Urbanish Campaign. I was there for about 3, 3 and 1/2 years. And at that point, I realized instead of finishing my PhD, I really wanted to go court for a living. So those days, this is like 1990 or 1989. And I asked a friend of mine in Silicon Valley to send me a classified section of San Jose Mercury News. And he sent it to me. Those are deep-- you know, the moment of deep recession. So I sent 50 resumes to anyone who wanted to say coding. I didn't care about what it was, what language, what the domain was. I sent about 50 resumes. Thankfully, I got one interview. There's a company called Vantage Analysis Systems. They were doing semiconductor design. And I gave them to Silicon Valley. And I got really lucky. In hindsight, as you can see, none of it was planned. But I got really lucky in a couple of ways that my first boss was amazing. So and I do think that your first boss can actually make or break your career in a lot of ways. They can really build their confidence or not. And the company was awesome. They were like, you know, when you're so about four years later, they were 45 people in the company. And I was looking counting later on about 18 of them went on to have titles of CEO. I mean, this was just crazy entrepreneurial culture, with lots of smart people. So I knew I wanted to start a company. So I four of us got together. Between us, we had $10,000. So we pulled it and started a company called Denali Software. Two people quit. The two of us who were the youngest one had the least to lose, we kept going. And that one semiconductor design space. In 2010, I was fortunate enough to have a, you know, sold the company for $350 million. It's still looking for that return. Before I-- [LAUGHTER] And then in 2012, I sold another company called Envilo, which was a system software to Samsung. And that was the moment I realized I was too young to retire. But I wanted you to combine-- I want to continue my passion for building companies. But this time, I wanted to do something which had a real sense of purpose to it. So ended up going and saying, OK, let's build something in education space. And that's what Vokirium was formed in 2014. And here I am, you know. That's the journey. I love that. I love that. And the comment about your first boss, I can think of my first education boss and how much that influence to I am today. Yeah, that's great. Other things that influence us can be teaching or learning moments. So we're curious. We ask all of our guests, is there a moment, a favorite moment, or a moment you remember in your life, where you were learning something? And it was impactful, where you were teaching something. It was impactful, or where you observed something. And you saw the impact of teaching and learning. So would you mind sharing maybe a favorite moment with us? You know, so mine is probably going to be a little unusual, right? So it was not a moment, but it was an environment. So you know, saying that when I was there in India, you know, we had a computer center, and you could one mainframe, and you had to reserve a terminal. So at late at night, you go there just so that you can do some programming on this, you know, like a really solid terminal, which is really slow. And then end up coming to our ban on champagne, and the computer vision lab. And I was dropped in this like environment with Vok Station all around me. And then, you know, we just had acquired a Pixar machine, you know, which was before Steve Jobs bought it and converted it to animation movie. And it was-- I just realized I've had so much fun coding. There-- Yeah. So having the access to that hands-on space. So for me, that was a big deal. So when I had the opportunity, I went back and contributed to something called a design lab back in Alba Martra, because to me, that was a big deal. Having peers around, your PhD advisor around, your research assistant, but more importantly, all in space where you can actually do real work. I love that. That's really-- And while you may think it's unusual, I think that's really timely and actually really connected to our conversation, because so much what we're hearing about the change in education is does that need to have it be experiential, right? And you need to be embedded, and you need to be able to code and practice and get excited about that. So-- I need that jump from theory to actual practice. Exactly. And just have fun. Like, I think that's so cool that you're having just fun in learning. So it's a good place to start. So we're all hearing. And maybe this is a little bit of an elephant in the room. But we're all hearing-- I mean, there's AI coding assistants are here, right? So much. There's this debate in this space around what role will be able to code play in the workplace. But even more than that, I've heard a number of department heads of CS ask, what's my role now? What should I be teaching? So what are some of the big or what's the fundamental shift that you're seeing we're living through right now in computer science education more broadly? The education system, as you can imagine, I mean, still has to react, because things are moving so, so fast, right? But in some ways, I think about when I started vocation, it was like we had a first set of policy in 2014. The big deal in those days used to be about computing literacy, right? Everyone was talking about everyone used to learn how to code, and especially in high schools, the states were talking about making at least part of the requirement. And then the computer science enrollment was exploding in the space. So at least at that moment, we went through one set of transformation. The second one, at least from what we'd observe around data science and data literacy. So there's, again, a lot in the especially business school thing, OK, we all need to become data literature, right? Which did mean some element of coding. So lots of business schools. And then we are going through business schools-- sorry, to finish my sentence-- here introduce computing and computational literacy, right? So the moment, I think we are going through-- it's one of those, at least, moments in my life, where you see extreme euphoria and extreme extreme panic both at the same time. Right? Yes. No. So at least from a literacy perspective, it was never really about learning how to code. I mean, because that was the easy part, right? So it was about how you code in the context of a system. So that's kind of when you learn your foundational computer science literacy, right? You worried about scaling and cybersecurity and all that, right? And then over time, SaaS, you start worrying about, like, how do I think design thinking into my coding process, right? And right now, it's about AI, right? And I do believe that the-- and I'm sure we'll talk further about it, but it's a big moment. But there are lots of components to it, right? Number one, the easy one is we all have to get productive, right? We all have to know how to use AI. There are some crazy things that you hear, which is like anthropic, especially coming from anthropic, that all these stories about their hiring 100 computer engineers, but nobody's going to code. They're all going to be just telling someone else, like the regions to code. But I think the AI literacy is a big deal, but I do believe that the change is still from a computer science discipline perspective. I think you still need to learn the systems, and you need to learn AI deeply. So we hear that analogy of programmers now are really more the conductor of the orchestra, rather than the players of the individual instruments. But how are we-- we still need to teach students how the instruments work, how the instruments play, right? What is that shift in teaching look like? Is that an app analogy that they're going to be the conductors? So again, the computer science, if you think about it, especially going forward, I mean, the computer scientists will be needed for lots of different things, just as in the context of AI, right? You will need people, of course, who will be building AI. So you have to have a deep understanding about how to build AI, not only just a generative AI, but they are the future. Obviously, there's an autonomy, but more and more, we stop thinking about physical AI. So that's one element to it, right? But going back to the agent orchestration, what is exciting is I think how our customers, especially in the people who are using technology, now have the ability to write agents and orchestrate agents, right? So as far as if you're a computer scientist yourself, I think the pressure right now is to get whatever, 25, 30% in a productivity from AI, right? And which means which could mean 40, 50, 60% of their code is written by AI, but you still being charged. And I'm just telling you, you will not let AI just say, go do this whole thing for me, but you say suggest.
just to architectures and you'll approve, you'll manage it. So that's certainly a skill, right? But it still needs a deep understanding and you'll be doing it at a level at which you need a deep understanding that AI is doing the right thing. Now, I do believe your customers. I think that's where it gets a lot of fun. So for the first time in like about 30 years, I decided to go, you know, build some agents myself, right? Like, so one of my problems used to be in business, like, you know, you send all these emails and you're sometimes people don't respond and you're like always on the pressure, did I track this fun? So I said, you know, let me write an agent to go go through my emails every morning and say, hey, who has not responded? And give me a quick summary about them, right? And another agent was like in a slack, if your customer lead is coming in, go make some, you know, guesses about estimation about how important this lead is and tell me research on the customer. And the third one was, I wanted to write, try writing a mobile app for Bokeryam, just something simple. And what is amazing about all these three projects, it took me about two to three hours to do them each. And I touched about eight or nine different pieces of technology, you know, Slack APIs, Gmail APIs, the, you know, going back, deploying on AWS and it was just, it was a eye popping moment over the world, right? You know, so going back to your question about agents, I do think our customers, you know, if you're sales and finance in everyone on customer support, you would, so again, the realization was amazingly easy to write it. The second realization immediately afterwards, like all man, it's so risky, right? Because you're giving these agents all these credentials to go do the mobile app. - To the mobile app. - Yeah, yeah, yeah. I don't know what the world will look like, but the agents will have to be written and it can be done without the proper kind of controls and guarders and knowledge and processes. And going back to the question, come to the science, I do think they'll play a critical role in how these agents get created and how they get measured, how they get orchestrated. That's awesome. Speaking of computer science and thinking about that, let's say you were gonna hire students and or you're gonna hire students after they've graduated and they have a computer science degree. What skills would you expect them to have? You're starting to touch on that, right? Some of this is around being able to build and leverage the technology, but what are some of the skills that you would expect now out of a computer science student as opposed to that might have been needed, you know, 10, 15 years ago? - So if I was, I mean, that's an interesting question. And also in some ways, it speaks to one of the real stress in the system, right? I don't know whether you guys saw that that you see is the first time the computer science enrollment decline. - Yeah. - You know, and I think partly it's happening in some ways pretty unfortunate, but partly it's happening because the jobs market for new graduates is like really bad. Now it's bad across the board, but because we live in such a kind of tech heavy world that what's getting really empowered is the struggle with the computer science graduate started having, right? But going back to your question about like what skill I would want to them to demonstrate as much as possible deep understanding of AI, which means what AI is capable of, but equally as important, a more sophisticated understanding of what AI is not capable of, right? And when, you know, it's not replacement for, you know, in a human judgment, you know, it's a, it's just a tool, you know, and you demonstrate that by hopefully having done project just like we used to live for apps in the GitHub, hopefully the project you'll be looking for is agents, you know, have you built agents, can you talk intelligently about it? So I think that's what, you know, we'll be looking for. And if they don't have it, come and we'll bring them over and we'll train them, but that's kind of the starting skill. - That's really important. I think there's an oversimplification of AI and the level of oversight it still requires that I think a lot of people need to be educated on. But when we talk about education, an AI and education specifically, the debate is reaching right now around whether AI is a shortcut to learning, you know, is creating paths around actual learning, or whether it lowers the barriers to access and democratizes access to learning. What is your take on that and where do you see that going? - So this is actually a starting question. You know, we got very, very fortunate that about two years ago UCSD, you know, they were doing some research around, a very specific problem, which is the math literacy. How do you go and how do you go address what they were seeing and this increasing problem of people coming in not having sort of a high school or high school level math literacy and they're trying to ask the question, "Could AI help?" right? And it was really exciting to go through this process and obviously, and, you know, they're not talking about it that in the last version of this pre-cal course, you know, with all the cohorts being, so you're going the same cohort, same sort of entry score. And the failure rate went down by something like 70% over the world. So I do believe that I don't think as an endowed in my mind and I think there's enough research out there already to say that AI, if implemented with the proper scaffolding and the intentionality, and obviously you can talk about more detail, it is enabled, right? Especially in terms of, you know, just providing access and improving outcomes, yeah. - I'd like to dig into that a little deeper. So how is it enabled? 'Cause to Ryan's point, we're also hearing a lot of, and I've been in conversations where education leaders are like, how do we just make them stop using AI? Or they only use AI in the agents that I give them in my context. And the cats are the bag, like, they're not stopping to use AI. So you talked about enablers, like, how are you thinking? And how does that actually maybe even show up with Boqueria? I'm like, how do you think about getting students to use AI in the right and more meaningful ways to actually add to their learning experiences? - So yeah, the experiment which we did, and if you find what does not work in some ways, you know, start with, right? So the two things in my opinion, they don't work, right? If you put the AI tutor out there, whatever, then the students will just not use it, you know, or they will use chatbot, I mean, you know, and it might be slightly controversial, take, and maybe people would not like it. But the whole idea that my tutor, all that it is doing is making sure that the domain of knowledge it is using to serve you is more restricted than what's available. So the entire website joke word, all the time, give me some money, and all that I will do is make sure it does less, you know, so that's it. (laughing) So that was, that was okay. That was a version, you know, not one point over, but O.1 version of deploying AI, right? The other thing which I don't think it works, now that was kind of an eye opening, right? You know, there's all sorts of research out there, which can say, you know, the student engaged with this tutor, or this chatbot for 30 minutes or so or a week, their education outcome, you know, is increased by a grade or something, right? But then what I think people start discovering it, and some people are calling it a 5% problem, that if you just leave it there, then only 5% of the students will go use it, right? And those are the 5% students who don't need it, right? So the conclusion is the AI needs to be deeply integrated into the teaching and learning environment, right? So the way at least I'm just deployment that I was talking about, what the educators decided, the way they do the deployment and the first assignment which is given, they have the scaffolded AI tutor deeply integrated with the assignment. So when they try to do a work, it pops up and saying, "Hey, you know, can I help you here?" And by the way, and it's personalized, it knows what they know, it has a full context. So then we see that the engagement with the tutor just goes through the roof, right? The students who are afraid to ask questions or might, you know, will actually ask the AI, right? So we have seen the numbers and pretty exciting. And these are the students who would otherwise might actually, the failure rate on some of this possible, like 50%, 60%, right? And we have dropped it down to whatever, 10% whatever. So that was, since the deployment was, you do an assignment with maybe AI tutor there, maybe you do a practice test with AI tutor and then, but you still have, you're accountable for your learning because then they will be your test, which is higher stakes, where there'll be no AI there or whatever, right? And it's personalized, it's actually tracks, but you're the digital twin. And again, there's a whole architecture that we have implemented, right? On what the student knows and what doesn't know. So if you integrate, again, the long ass, you know, so it's basically you have to integrate into the current learning architecture, right? Where there are assignments and there are grades and AI sitting there as an aid, right? Rather than something on the side. - And I love that aspect of it because the next question then is, we've seen very recently, there was an article about someone that created an agent to take their course for them, take their quiz for them. We know that there's tools like Dothaei that you can literally take a picture of your screen and they'll give you the answers. How do we evolve how we're measuring mastery of skill in this day and age? Like what is the, there's no silver bullet, but how do we need to evolve that approach? - That's a really complicated question, right? And I obviously, I mean, I don't know the answer, but it was kind of exciting. So I went back to University of Illinois, my alma mater, right, about a couple of months ago. And they had invested, and this is obviously all pre-AI, but computer-based testing facility, what they call CBTF, right? In this engineering school, and they're, in any faculty could ask us, like, you know, go ahead and take this test there and it's completely monitored and locked down. And the use for that has just gone through the roof, right? So for summative assessment at least, I think that's what people are doing. But in my opinion, what is exciting about it is that the formatting assessment is now, it's like, let's go out there, focus on learning, who, you know, because, you know, and I see the two concrete examples of that. One is what I just mentioned. In the summative assessment is of now, a formative assessment, now about you mastery, you know, you're getting AI-driven mastery of a concept. The teacher says, you know, I want you to go out there and demonstrate mastery of this, you know. And by the way, if you cheat there, you know, it doesn't matter because your assessment, which would most of it constitute,
the grade is will be completely proctored and all that. So that's one formula. Some other customer asked for it, which we haven't implemented. We're excited about it. It's like you will write code and then you will get an auto-graded in a answer to for grading. But then you will be asked AI driven, of course. It to explain different portion of your code and the AI will provide you with feedback on that one. So that's kind of exciting again. It's not, it's again, it just helped me student learn. And of course, how do you grade that? It seems like the only answer for now is just to go lock down. Yeah, lock down browser, blue, go back up, blue books. I was looking for a stat on how much this cell of blue books had increased since over the last three years. I'm still trying to find that one. Because I do think there's a number of, we're going to go analog because that's the only way to figure out. I think we're, there are better solutions than that we just haven't quite figured them out. Right, yeah. And I think some of that comes from authentic assessment. And I would argue that in a lot of the computer science work, and now I am not a computer science, I do not hold that degree. So but in watching and learning from this, I think authentic assessment has been embedded in that discipline in certain ways. I certainly know, Vicarium is very much about authentic experiences, but I think a lot of other disciplines haven't gone that direction. They're still using the same multiple choice test from 20 years ago because they feel like those facts are the most important things to know. And maybe in some cases they are, but where does authentic assessment play in your mind? How do you think about that? Or how have you thought about that? As you've tried to solve, in particular, computer science and coding education. So as you pointed out, so one of the biggest things which we focused on, Vicarium, the only thing which we focused on is providing experiential environment, which is as close to real world as possible, which means that you get real tools, you do real projects, then you enable the faculty to go in and assess that, for students to work on that and assess that. There is, I mean, still a challenge of scale. If you do, and that's somewhat unsolved problem, that if you have 20 or 30 students in a class, that's one thing, right? But if you start having, which is an unfortunate thing about our lot of our events, that the beginning, lower division classes tends to be in hundreds of students. I remember walking to, you know, like a computer science department, a one of major R1s, and they had an org chart of their CS1 class. It was something like 1400 students. This was the bigger, this was way bigger than any company I have worked for, right? Yes. There's a head TA, there's TA below there, there are like seven lecturers. And you know, the assessment, obviously, adapt skill has always been a challenge, but I completely agree with you. I think we need to find a way, you know, to make it real for by just lowering the, lowering the student to teacher ratio, right? Yeah. Yeah, scale has been such a problem and experiential and authentic assessment for a long time. Maybe AI will give us a chance for this. Sorry Ryan, please go. No, that's great. I mean, it's right in line with, you know, it was a really good report from Digital Education Council towards the end of last year. Then I actually said, okay, 61% of educators are actually using AI. Like they've done something with AI, right? The bar was pretty low. But that meant 40%, you know, not too sure I have hadn't, right? So what is this kind of shift with AI mean for faculty? Are they learning these tools? I also have a weird kind of bias because I go out and talk to universities and I tend to talk to the ones that are using AI most aggressively. And so I'm a little biased on how much I think people are using these. But what is a teacher's role using the AI tools? How does that evolve? You know, I think that's a lot of people's questions. So you know, one thing which Google came out is the block from Google, which was kind of actually shocking that I've forgotten exact numbers, but they sort of went out surveyed all the users, not just education users, right, for AI. And actually it's turned out that the super users were both educators and students, right? So very, very, now students were using more, but it was maybe off like by more than 10 points or something, right? It was, so I do believe that, you know, in one place, all of us start using AI, especially the first generation of it, just the productivity tools, right? Just go out there, let me help me create my lesson plans, my assessment, my study plan, if you're a student. - Yeah, study guys, yep. - Yeah, rubrics and study guys. So I think that sort of happened, right? But I do believe that all the disruptions, the fun, you know, if it stays with productivity, improvement, that's only, you know, so interesting. At least in case of education, you know, I'm optimistic that AI will help the educators deliver. What I think has been the holy grail, is like personalization, right? So for example, you know, in the last change, I was visiting one of, I was really surprised to see, like in a computer science department, maybe I shouldn't have been that surprised, but you know, it's a few hundred percent plus, and the teacher has already moved to a flip-flip classroom. So that was kind of the first attempt to personalization, right? So the student do all the, you know, videos in a, whatever in their dorm rooms come there, she will go out there, have these groups of four or five students, all through the classroom working in a project or whatever, and you know, and she's, she and her TAs are trying to sort of help us needed. So I'm hoping that the superpower AI will give to the educators now, is to do the personalization, you know, at the next level, right? So just as a person of the story, you know, my grandson who's like 14 year old, when I had the chance, I influenced so that he joined a school called Fusion School. I don't know if you guys are heard of it. - Oh yeah. - They are a canvas user. - They are a canvas user, right? - Yeah. - And I love the fact that it's everything is, every educate, you know, is one on one, right? And my grandson is thriving in it, and I just, and that's for obviously, there is a, there is a, to scale the sub, there's a cost issue, but in, so at least going back to question about the educators, I'm hoping AI is the one just, you know, gets the closer, right? To deliver prosplization exactly what, you know, what tools they use and VR experimenting with that. I'm sure others are too. - Yeah. - I think that's one thing that gets lost a lot too, is the idea that you don't have to be a graphic designer, you don't have to be a videographer. You can create tools that help engagement in your courses in ways that you've never been able to do before. I think that gets lost a lot in this as well, so. - Well, and I want to hold on to this idea. So personalization excites you. What else excites you next five to 10 years ahead around what AI can bring to either computer science, education, or education in general or both? - So computer science, I mean, generally right now, it excites me to be in this moment, even though there's all this human gloom around it, right? I mean, because, personally, I don't think it's a model-t moment, right? It's not like, oh no, you know, we have to start worrying about all the people who are, you know, care about the horses and could drive, you know, driving carriages, like what will happen to them, right? I do think it's one of those moments, more like a calculator moment, or a little bit more boring, is that, you know, you came up with a higher level language in a computer programming. And some is, you know, programming was due for a disruption. We have been doing the same thing, like, you know, like 30 years, I mean, you know, when the, when we went from, there's somebody like called to higher level programming. So I didn't think what excites me right now that the computer science is going to, you know, in various forms is going to start touching every part of our society, either maybe, maybe in terms of physical AI, in terms of going, you know, I'm hoping, like data science was interesting, right? Few years ago, there were no data science programs. Now, their data science programs are, their people are building colleges, because that's an intersection of computer science and say, math, the statistics, right? Now, I'm hoping that, you know, the computer science will see maybe, you know, at the intersection of, maybe learning. We wanted to be amazing that if we start seeing the learning science and AI in a computer science, becoming this big sort of area of focus, because finally, in all the stuff that we have been learning, learning science, you can actually deliver it, right? So it comes back to personalization, but driven by computer science. So, the other, I'm going to ask you about the flip side of that, 'cause there is a lot of doom and gloom, there's a lot of the scary stories. In fact, why you're just ran an article, I loved my open-cloth AI agent until it turned on me, right? We heard the story of an open-cloth agent that couldn't make a dinner reservation online, 'cause the forum was broken, so it gave itself a voice and called the restaurant, right? You mentioned earlier, that level of autonomy comes with risk, but what do we need to be cautious of moving forward? How do we make sure that we don't go down this dystopian path? No, I think we need a lot to be cautious about, because if you think about it, at least from a technology perspective, that technology's job was to automate some function, right? So it was supposed to drive productivity. So as a consumer, you knew when you got into a car or made a phone call, you knew what it was, you know, roughly what it was doing, whatever, right? So you said, make me more productive, it made me some of that faster, you know, connect me to someone. But, you know, this is the first time there's a risk that we can actually outsource, you know, ethics to a tool or we can outsource judgment to a tool, or whatever, and not realize, so I mean, you know, even a tech company, I occasionally get challenged by, hey, but the AI said that, my decision making, the someone is using it. Yeah. Yeah. I have to go compete with, you know, there is a real, real high risk of actually, yeah, as you said, too much autonomy. There's a real high risk of both in terms of our judgment and our biases, but there's a real high risk of course, which giving AI, you know, just like CloudBard, you know, giving, I mean, I'm sure you guys are seeing all the stories about it. Well, it started letting your emails, it started doing random stuff, including the example that you give, and we all need to understand deeply. I mean, we, that AI is just a tool which was trained on a set of data and the human beings were involved, which is, you know,
coding the AI to say, "Hey, this is a good answer. This is a bad answer." And this was used to train it, right? So the risk is we have to all deeply, deeply concern about it is that if you don't educate the people, I mean, the risk, you know, is large. I mean, and if you think about from a sovereign, in a country's perspective, you know, if you're an Africa or if you're in like India, you're asking the AI to pass judgment on a specific period of history and it is all, you know, and it has a, you know, a specific lens. And I think, you know, that bias, that bias aspect, you know, how it was trained. And then I think our own interpretation that our tendency to anthropomorphize these tools is, is increasingly there repeatedly. Yeah, yeah, yeah. And I'm not, I remember going back on my grandson's story that like a few months ago, he was like, "Hey, you know, do you think I did that with AI and do you think it's cheating?" And I was like, and it really stopped dead me in the tracks or whatever. I'm like, I don't know. I said, so I could give some really vague answer like, "Hey, you would know when you're cheating, but very deeply I'm not fine." But, yeah. But, but, but there are these questions like, am I, you know, it's like, how do I interact with the AI which we just turn now and we just need to evolve as a society, you know. Yeah. I wish we could come up with all the answers on this podcast, but we will probably end it there. Sanjay, this has been an incredible conversation. I was, you know, your insights are fantastic and we're kind of shaping the future together. So these conversations are super valuable. Yeah. Thank you so much for being here, Sanjay. So much about this conversation is inspiring and I hope our listeners can start to think about how they're approaching computer science education, education in general and the intersection of AI. So thank you. Yeah. Some will include some links in the show notes below so we can link out to some of some of the stuff that you mentioned. But this has been a great conversation. Thank you, Sanjay. Awesome. Thank you so much. I really enjoyed it. Thanks for listening to this episode of EGECAST 3000. Don't forget to like, subscribe and drop us a review on your favorite podcast players so you don't miss an episode. If you have a topic you'd like us to explore more, please email us at
[email protected] or you can drop us a line on any of the socials. You can find more contact info in the show notes. Thanks for listening and we'll catch you on the next episode of EGECAST 3000. [BLANK_AUDIO]
PodcastTranscript AI