28 minutes | Apr 23rd 2020

Education and AI

For David Guralnick, education, AI, and cognitive psychology have always held possibility. With many years of experience in this niche, David runs a company that designs education programs, which employ AI and machine learning, for large companies, universities, and everything in between.   David Guralnick: Somehow what’s happened in a lot of the uses of technology and education to this point is we’ve taken the mass education system that was there only to solve a scalability problem, not because it was the best educational method. So we’ve taken that and now we’ve scaled that even further online because it’s easy to do and easy to track. Ginette Methot: I’m Ginette, Curtis Seare: and I’m Curtis, Ginette: and you are listening to Data Crunch, Curtis: a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company. Curtis: First off, I’d like to thank everyone who has taken the Tableau fundamentals zombie course that we announced the last episode. We’ve been getting a lot of great feedback from you. It’s fun to see how people are enjoying the course and thinking that it’s fun and also clear and it’s helping them learn the fundamentals of Tableau. The reason we made that course is because Tableau and data visualization are really important skills. They can help you get a better job, they can help you add value to your organization. And so we hope that the course is helping people out. Also, according to the feedback that we have received, we’ve made a couple of enhancements to the course, so there are now quizzes to test your knowledge. There are quick tips with each of the videos to help you go a little bit further than even what the videos teach. We’ve also included a way to earn badges and a certificate so that you can show off your skills to your employer or whoever. And we’ve also thrown in a couple other bonuses. One is our a hundred plus page manual that we actually use to train at fortune 500 companies so that’ll have screenshots and tutorials and tips and tricks on the Tableau fundamentals. And we have also included a checklist and a cheat sheet, both of which we actually use internally in our consulting practice to help us do good work. One of them will help you know which kind of chart to use in any given scenario that you may encounter, whether that’s a bar chart or a scatter plot or any number of other more advanced charts. And the other is a checklist that you can run down and say, “do I have this, this, this and this in my visualization before I take it to present to someone to make sure that that’s going to be a good experience.” So hopefully all of that equals something that is really going to help you guys. And something also where you can learn Tableau and have fun doing it, saving the world from the zombie apocalypse, and the price has risen a little bit since last time. But for our long-time listeners here, if you use the code “podcastzombie” without any spaces in the middle, then that’ll go ahead and take off 25% of the list price that is currently on the page. So hopefully more of you guys can take it and keep giving us feedback so we can keep improving it. And we would love to hear from you Ginette: Now onto the show today. We chat with David Guralnick, president and CEO of kaleidoscope learning. David: I’ve had a long time interest in both education and technology going way, way back. I was, I was lucky enough to go to an elementary school outside of Washington DC called Green acres school in Rockville, Maryland, which was very project based. So it was non-traditional education. You worked on projects, you worked collaboratively with people, your teachers’ role was almost as much an advisor and mentor as a traditional teacher. It wasn’t person in front of the room talking at you, and you learn how to, you know, you really learn how to think creatively and pursue your own interests and learn by doing, and so all of that stayed with me as I got older and I developed interest in technology from a really young age. I had my first computer at 13, which was at a time when people did not normally have a computer at 13 and was interested then through that in how computers could learn and what did artificial intelligence mean. And it was a field that was, was a bit of a mystery and ended up as I was finishing college, getting interested in the work of an artificial intelligence professional named Roger Shank who was at Yale. And Roger was just at the time leaving Yale with some faculty to start an Institute at Northwestern university that brought together a cognitive psychology, computer science and AI and education to apply artificial intelligence techniques to education. And so I did my PhD at that program and ended up being asked to focus particularly on business problems in the corporate world and work with some corporate clients through Accenture, that was in Anderson consulting and ah, it’s kind of what, what, you know, the work that continues to this day. Curtis: Yeah. That’s great. What, what year around were you’re doing your PhD, just so I get a. David: PhD for me was starting in ’89 and so wrapping up in ’94. Late ’80s early ’90s. Curtis: Before the AI wave hit everything, right. You guys were working on this stuff on the cutting edge it sounds like. David: Yeah, absolutely. It was, it was, um, we were considered on the cutting edge was a cutting edge lab. We were, you know, written up in the early days of wired magazine and all that kind of stuff. And it was really interesting place to be, it was a tremendous group of people. We had, I mean, some of them I still work with to this day. We had people who were excellent writers. We had people who were really cutting-edge thinkers in AI and in education and, and in cognitive psychology, which sometimes almost like cognitive science side sometimes gets left out, right? It’s, you know, how do you, how do you think and learn? How do you, how do you understand what your, you know, what you’re experiencing. And all of that goes into designing the experience. So yeah, those were, it was a really a really fascinating place to be and built on a lot of the principles that, that I kind of believed in from my formative years and couldn’t work out any better. Curtis: Yeah. That’s awesome. Now, now you’ve seen this whole progression of, of AI machine learning . . . What’s your perspective on that since you’ve, you’ve lived this entire cycle now? David: Yeah, I’ve lived a, yeah, I’ve lived a few cycles. When, I mean, when I first started doing it, it was kind of, you know, the, uh, you know, the almost, almost became the dying days of, of AI at one point, right? Like we were doing really interesting things I think in applying it to education. But as a field AI was considered, it was considered a failure. The years since my PhD were mostly what’s considered AI winter, you know, really it just didn’t had high hopes. We expect it to be in a Jetsons like world and we are not. What happened? And you know, now I’ve seen the Renaissance and the Renaissance has been certainly interesting to see. There’s obviously a lot more computing power now, which has helped. There’s sort of a lot more public interest and understanding of what AI could be. And some of that’s, you know, there’s probably more, more good than bad though. Sometimes it’s a little scary. We also are in danger of being over-hyped once again. And I think that’s the thing that we, we look at. I mean I’ll talk to people sometimes even about what’s possible, what kind of conversations online systems can have with people, and there’s usually an overstatement of, of what the reality is. And so I think that’s something to be cautious of as we move forward and keep thinking about where AI techniques and machine learning, which, which to me, which the traditionalist is a subset of AI can fit in and not, you know, not overstate and not necessarily feel like the goal has to be a fully functional human replacement. I don’t know that that’s a societal goal for a lot of reasons, but even in terms of technology, it’s not clear that that’s what we need. And in particular in the world of education, it’s not clear that that’s what we would want. Curtis: Right. Now, can we talk a little bit about cognitive psychology and the angle that, that that takes in your work? That’s not a topic we hit very often on this show, but I think it’s really interesting as it applies to to the work you’re doing. David: Yeah, absolutely. There’s, I mean to me, and it’s always been a critical part of what we do. You’re not looking at just putting technology out there, you’re looking at technology that in some ways on one side might mirror some of human thought processes. So that’s part of what we were doing back in my old research lab at Northwestern was thinking about how technology could, could reflect human thought processes. But then on the, on the end user side, so on the more practical side, we need to develop technology experiences that really do help people accomplish their goals, whether they’re educational goals or whether they’re otherwise. In order to do that, we need to have an understanding of how people think, how they learn, how they process information, how they acquire skills. Some of that borders on education research, but a lot of that is the cognitive side and it all, to me it really is all interdisciplinary, right? You
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