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Welcome, Vijay Kumar.
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It’s really a pleasure to have you again
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at Universitat Oberta de Catalunya
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at the Digital Universities Europe,
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organized by Times Higher Education and UOC.
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You have a large experience
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and knowledge in the area of educational innovations
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and technology strategy.
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So, it’s a privilege to have the chance to
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dialogue with you about the main challenges
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in the digitalization of higher education,
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such as the role of artificial intelligence,
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student support, feedback, assessment,
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and new entrants or new roles at the university.
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So, in a world where change, resilience,
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and innovation are integral parts of our daily lives,
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I would like to delve deeper into how educational innovation
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is implemented or articulated in your institution.
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In another interview, you pointed out that what you really love
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about working at MIT is the combination of looking over
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the horizon and looking under the hood,
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and also that people always tend to look at the hardest problems.
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So, my question is, how do you effectively organize
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and manage educational innovation
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in order to be effective and engaging,
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but also manageable?
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First of all, thank you very much for this opportunity. This is an institution that I’ve had a long association with, a very good, proud, productive association.
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And it’s nice to come back to this incarnation of UOC, this facility that we have. It’s a beautiful facility and very nice to meet you too.
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It’s a complex question, you said, because one of the things that, and I’m surprised that you remembered or you found that piece about looking under the hood
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and looking over the horizon because I think a large part of innovation is having an orientation.
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It’s like having it in your DNA about innovation.
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And innovation, and I want to pick on some of these words because what does having an orientation mean?
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And you said one of the things at MIT that I find is nothing goes unchallenged.
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So if they say, oh, the students are not learning a particular thing the way we want them to, do they have conceptual misunderstandings that we can correct?
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And the impact of that, the result of that might be, should we change the way we teach that topic?
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Should we change the learning experience?
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So all of these are subject to innovative possibilities, which is essentially saying can we change the input parameters in creative, imaginative ways so that we can get better outcomes?
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And there’s a whole lot of things you can do for that.
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For instance, sometimes when we think innovation, we say, oh, it must be complex and digital.
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You shift chairs in a classroom and have a different element, that’s an innovation.
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You say, can I use a little bit of technology to create some applets for visualization?
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That’s an innovation.
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Can I use AI for better learning opportunities?
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That’s also an innovation.
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But the underpinning over there is that you look to say, how can I imaginatively use the resources that I have to arrive at different learning outcomes?
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And this is, we are at a time when we have such immense possibilities.
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Yesterday I was noting in a panel here that if you look at a course or what we taught, every aspect of it is prone to innovative possibilities because of digital technologies,
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because of what we are learning from learning science and because of what openness brings, the aspirations of opening.
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So for instance, we say, well, a lecture is no longer a lecture.
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It’s a six-minute video segment, which can be immediately followed by some formative testing, formative assessment,
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which can be, if you don’t understand the concept, suddenly you can have a visualization.
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So you have changed all the parts of delivering a lecture into these different kinds of components.
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And you’re doing all this because you’re learning something from learning science about how to do it better.
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And then you’re saying, oh, I can use technology in an imaginative way in order to come up with a different kind of a possibility.
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Now, there’s two things that came out over here.
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One is, I have to try it.
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So part of innovating is that I have to experiment with it and assist it, right?
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Because I make a conjecture saying, if I do x, y will follow, y might not follow, but I have to assist it.
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Or I might have to say, I have to tweak this.
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I have to make the video longer, shorter, or maybe I have to change the sequence order.
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Or maybe for this group of learners, that’s not working because [they’re] not yet ready to understand from visualizations.
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So I have to experiment and honestly assist and make corrections, not just in how they learn alone, but how I teach also.
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The other part of it, which I will say, which sometimes, because you introduce a very loaded term,
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you said innovation, and it’s a complex term because often we confuse the invention with the innovation, right?
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That technology, the particular thing is an invention.
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The minute we say innovation, for me, my research work is an innovation diffusion, right?
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So suddenly I think about a very systemic phenomenon, meaning in order to apply an innovative intervention and invention,
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I need to think about what does the classroom look like, what does the infrastructure look like,
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is the teacher prepared to do that, are the learners prepared.
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All that, for me, is in the realm of the innovation.
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Suddenly we have taken it from a point process to a systemic process, right?
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And so both those we think about pretty deeply and nowadays we even say that, okay,
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it’s not just about you, the teacher and the learners, it’s also about the ecosystem, you know.
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For instance, at MIT, we do place a lot of value on active learning, hands-on learning.
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And they say, well, today maybe I don’t, I mean, I value active learning, I bring it into this thing,
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but maybe hands-on practice, they can actually do through an internship in an industry.
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I care about it, but maybe I’m not the best person to deliver that, you know.
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So you can look at innovative combinations on the supply side to deliver the outcomes also.
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So anyway, so yes, so the aspect of inquiring about what’s going on, experimenting, assessing, right?
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And then thinking that you have to really look at it through a systems approach,
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that’s the orientation that makes us very innovation-pro, you know.
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That’s a good point, you’re assessing all the process.
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You know, and this is a good, I mean, I’m glad you asked this also because sometimes MIT, you say innovation.
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You know, many years ago, when we launched OpenCourseWare, you know,
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and people have heard me tell this story many times, when we launched OpenCourseWare,
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in the US, there were some, you know, initiatives that were being launched to commercialize educational content.
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Some universities were doing something called Fathom, some others were doing something called UNext,
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and our leaders at MIT, I remember our president, you know, Chuck West, very dear man, the late Chuck West,
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a provost, Bob Brown, who till very recently, he was my boss when I was assistant provost,
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and he became the president of BU.
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You all said, hey folks, what are we doing about the internet?
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Have you missed the opportunity? There are all these institutes doing what?
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And our faculty said, in order for us to respond to that question about how do we think about the internet,
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we have to really think about what is the quality of excellence at MIT.
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And we, at that time, we said, there was a lot of discussion, a lot of discussion, words like intensity, etc., etc., were thrown around.
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And people sort of said, and I’m simplifying this a bit, said the real value of quality of excellence at MIT is a very high bandwidth of interaction
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between great faculty and great students, because we take a lot of trouble recruiting great faculty, we take a lot of energy getting great students.
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So, and then we also said, you know what, we are a research institution, and we want to bring the practice, the tools, the joy of research into the teaching-learning experience.
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So this notion of experimentation is very much in the research DNA of MIT.
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So I said, what is this? Let’s try it, let’s measure it, let’s assess it.
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And then if it’s not good enough, try to correct it or throw it away.
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So, I mean, and so it brings in, but to finish that chapter, that’s why at, in OCW, when we launched OCW, we very, very clearly said,
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this is a publication of MIT courses, it is not an MIT education, right?
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Because we said all that stuff, interaction, research, we know only how to do in face to face.
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Of course, when it came to 2012, we said we can do excellent education, but we can’t do online, you know, online learning.
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But the notion of, when you said, you know, it’s a place which likes to look at hard problems, and when I say hard problems, they need not all be world-sized problems.
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Sometimes the hard problem is, why can’t I get this concept across to this learner?
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It’s a hard problem, it’s an intractable problem because we have tried this, we have tried that, you know, I used to joke sometimes.
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When my father used to try to teach me mathematics, you know, and sometimes in my talks, I’d say, you know, he tried to teach me probability and statistics,
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and all I saw was an angry guy yelling at me, because, you know, do this, do this, do this,
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and you’re trying the same thing and expecting different results, and he said, maybe there is another way of doing this, you know, that will yield better results.
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That’s innovation, and you want to experiment with that, and you want to assess that.
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You mentioned artificial intelligence, but the emergence and widespread adoption of generative artificial intelligence in higher education [means we] need to profoundly reconsider the processes involved in teaching and assessing.
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In the students’ acquisition of knowledge, skills, competencies, at UOC, we think that this is really an opportunity,
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but how do you believe universities can adapt to this new context? How do we need to rethink teaching and assessment, and probably how do assessment models need to evolve in this context?
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It’s an excellent question, I mean, as being in the role that you have, it’s not a simple question, it’s a complex question, and you’re doing things.
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Then I will tell you, so my PhD is in Future Studies in Education, and I got, when I got introduced to some wonderful work that was happening in AI those days, okay?
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So I was, there was wonderful work going on at MIT. I was at UMass Amherst, and I actually had to come to MIT to see some of the work that was going on, Minsky, Papert, and John Seely Brown, who influenced my own work in many ways.
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But we were designing intelligent tutors in the early days, and there were programs, there was one program called Buggy, okay?
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Buggy was to see how kids were doing a math program, and to see the pattern of mistakes they were making, because the patterns of mistakes reflected the conceptual misunderstandings that they had.
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So that’s a very, very interesting use of AI. Nowadays we say intelligent tutors. So intelligent tutors is really looking at an individual level, you know, how is this person navigating the solution space,
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how are they approaching it, and AI helps surface those kinds of things, so that you can intervene appropriately. You can say, maybe I need to change my teaching strategy, or get to some other kinds of experiences that will clear those conceptual misunderstandings.
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And so, and now when you look at AI, and we will certainly talk about the elephant in the room, ChatGPT, but the thing is, one of the things AI does, and you know, it is so, it’s coming as part of commoditized products, whether it is learning management systems.
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Yesterday you had the gentleman talking about Salesforce and all that. It’s embedded in many ways. We all do formative testing, right? There’s a lot of AI that’s being used. Three years ago…
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I do not know if you know or remember this particular incident. Watson, which was IBM’s AI system. So there was an instructor in Georgia Tech, who was using an intelligent AI tutor for his online courses.
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At the end of the semester, he did a survey amongst the students to find out which tutor, which they liked best, you know. So the most, the person who got the highest rating was Jill Watson. Jill Watson was the AI tutor, right?
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So, I mean, so for tutoring, you can tutor because, you know, they can look at problems, they can respond very quickly. They have much access to…, because of the data analysis and processing capabilities, they can deal with a very large solution base to get to appropriate personalized kinds of tutoring.
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And that’s good. That’s a good use. So AI is being used constructively in various ways. Personalization, adaptation. There is an associate of ours, a colleague of ours, who was a member, a supporter of, a sponsor of our Jameel World Education Lab.
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He has this company, I worked pretty closely with him. It’s, in fact, he has come and we had some discussions with him two years ago when he had come to UOC. You know, it’s a product platform called Gyan.
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And it’s an AI platform which allows you to create pathways between skills that you have and skills that you need. It allows you to create different kinds of thematic content bundles.
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And it’s, you know, if a simplified version is, you might have your résumé, your LinkedIn profile, your transcripts, it will extract your competencies, your current level of skills.
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Then on the labor market side, there are various people who say, for different kinds of jobs, what are the skills needed? What this program will also do is, it will harvest.
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For instance, they have ingested all of OpenCourseWare and say these skills, treatment of these skills is available in these kinds of courses.
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And it’s more than just doing a Google search and just on keywords because it just, it finds material within courses.
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For instance, if you want to look at entrepreneurship competencies, it will find courses, programs, content collections, which will take you from where you are to where you need to go.
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So that is a very, very imaginative kind of AI use.
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And I think because we all want to provide more and more personalized, customized learning experiences, I think it has great opportunity.
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So I’m very bullish on the use of AI as a partner in the instructional process because I get tired of this conversation, is it me or is it AI?
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It is me, like we say in good first language, it’s me and AI.
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So, and we can think about how do we judiciously employ,
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eyes wide open because there are issues, how do we judiciously employ the capabilities that it provides for things that we do well that we can do better, more importantly for things we have not been able to do.
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We have not been able to do, so if you say learning science, can I use learning science and employ AI
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to provide more informative pathways, learning experiences and the answer is certainly yes, how can we do that?
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You know, there are issues around plagiarism, all this stuff with ChatGPT, but there’s also the issue that it provides simpler micro worlds for people to learn.
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So if I can point a generative AI program like ChatGPT to the right sets of content resources and if I can supervise it in some way so that people can actually have simpler incremental pathways to learn particular things, that would be a good thing.
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I will tell you, at a micro level, analytics, this platform that I talked about, when we first looked at, Gyan, when we first, when I first saw it, its biggest use was analyzing essay type answers.
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Now that’s a challenge in our things because yes/no kinds of things we know how to do.
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How do you analyze and a lot of good learning on even résumés and all, you want to like essay type questions and it did very well over there.
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So it provides opportunities to do assessment in more profound, better ways, particularly in areas which have been difficult to assess, this subjective kind, essay kinds of assessments.
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And I think that’s, that’s a real value.
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So micro level assessments, it will also allow us to learn about learning better, I believe, because it will surface connections that we might not have obviously seen.
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But there is another thing that’s particularly relevant, I mean I’m familiar, you know, UOC was doing open learning, online learning, you know, like I said yesterday before it got fashionable, right?
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So, and were doing MOOCs and things like that. Many of our institutions who, what we did in the first blush of online learning, we took what we were doing on site and planted it online, which is, you know, we all know which is suboptimization, which is, could be a bad thing also, right?
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We are missing the opportunity of rethinking courses and providing different kinds of experiences.
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But there is another thing, the help that we need to provide, more and more, I mean when you look at MOOCs, when you look at even things like Google Tools, you know, more and more of a learning is getting to be autonomous, right?
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Even in a classroom, you walk in, you mention a name, people are looking in. It’s like you said, you knew from some interview before more about me than I remembered, right?
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So, we do a lot of self-learning, autonomous learning, not in the computing science term of the word.
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So, how do you provide help scaffolding and support to a learner population which is getting more and more autonomous in its learning?
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We cannot meet all their needs. We have to create facilities for them to learn on their own, right?
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Now, that’s because right now the kinds of help and support we provide them is based on what we know from face-to-face experiences.
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Meanwhile, we have millions of users who are using online experiences. We have click-stream data, we have page-turning data, we have, we can identify data on how they find groups.
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So, we can come up at a macro level by using AI to mine the data to see how can we provide better support for online learners, better scaffolding for autonomous learners.
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I think that’s the kind of potential we have.
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You mentioned many things, but I completely agree that if a lecturer or teacher can be substituted by your machine, I mean, let’s substitute the teacher.
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I wouldn’t say that very loud, but that’s a, yeah. Why are we doing that?
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The teacher provides other aspects.
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You mentioned one thing, you know, when we talk about artificial intelligence, probably it connects with authorship and plagiarism in the assignments.
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What do you think are the main challenges in this aspect? And also, what role do ethics and morals play in these contexts?
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Well, ethics, certainly. I mean, the thing is, look, you know, I’ll come to a statement that I want to make, but before that, you know, I mean, there are challenges.
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I mean, you know, I mean, we say garbage in garbage out, you have to be very careful.
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Like I said, just before coming, I did a ChatGPT search, because I had to do a presentation last week on innovation diffusion.
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There was a model that I had used in my research work. I went and did a ChatGPT to bring out stuff. It cooked up stuff. It was all lies.
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Okay. So, and I’m saying here, I could tell that it was a lie. To the uninitiated, they’re going to take it as a gospel truth.
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Right. So this, you know, what they call hallucinations.
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So there’s a lot of making up and going because it is, I mean, it’s just like saying, you know, ChatGPT will tell you when it says it is hot or warm.
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It’s not because the AI has experienced heat or warmth, because you’ve seen a whole lot of instances where in that situation, hot is the right answer.
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It’s the most frequent, not even the right answer, most frequent answer. It says hot or warm.
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You know, it’s not. So it is deprived of any experiential basis. It is derived notions from derived notions.
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So it’s, you know, levels of derived notions. And that’s, you know, we are in the business of people who have to investigate the truth when we create.
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And this is not to diss all the capabilities, but we have to be very mindful of the lies and especially, you know, in our business, we talk about novice learners, a lot of people who are first time coming into education.
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There’s a lot of trust that they place in the institution and the mechanisms that they come. They’re taking a big risk by coming to education.
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It behooves us to make reasonable guarantees that they’re not duped. Okay?
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So the data that they point to, the supervision, you know, that is, that’s a responsibility we have taken on as educators. That’s a covenant practice.
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And this is related to the quality and reputation of the universities and institutions, no?
00:25:10:00 – 00:25:19:00
And you mentioned ethics, morals. I mean, so the little statement I was going to make was there was a colleague of mine who would say that you cannot legislate morality.
00:25:19:00 – 00:25:33:00
I mean, you can bring all the, these things. But I think you also don’t want to make it easy for people to confuse unethical practice with ethical practice.
00:25:33:00 – 00:25:41:00
Right? You want to have sometimes hard lines, but sometimes difficult thresholds so that they realize, you know.
00:25:41:00 – 00:25:49:00
And, you know, I mean, then we can go into some, you know, philosophical discussions about what is the truth and so on.
00:25:49:00 – 00:26:01:00
But I think we have to be careful. Even if you just treat it at the level of equity and fairness, you want to make sure that people who are striving to get to the truth are not distracted
00:26:01:00 – 00:26:06:00
because somebody is getting it in a devious manner. Right?
00:26:06:00 – 00:26:20:00
And, or I think there’s the risk of getting, you know, the bias of data. And we know those cases where it, the data samples it pulls from, it’s getting more and more sophisticated.
00:26:20:00 – 00:26:28:00
When you look at all the programs, image, DALL·E, you look at ChatGPT 4, it gets more, you know, more and more.
00:26:28:00 – 00:26:35:00
And we have deep, in my own house, we have deep discussions about this. My wife is an AI, you know, computer scientist.
00:26:35:00 – 00:26:44:00
We have discussion. But the fact is that as educators, there are risks that we have to be mindful of.
00:26:44:00 – 00:26:56:00
But there are opportunities we should not disregard. Even ChatGPT, when we talk about it, the fact that it provides simpler micro worlds or paths towards, to synthesize information,
00:26:56:00 – 00:27:05:00
it might be used even for language learning and things like that, you know. So, if we can harness it.
00:27:05:00 – 00:27:10:00
Let me shift now our focus to lifelong learning.
00:27:10:00 – 00:27:19:00
The demands of today’s labor market mean that professionals require upskilling, you mentioned it already, and re-skilling.
00:27:19:00 – 00:27:29:00
They need lifelong learning to stay up to date. In our context, at the European Union in June 2022,
00:27:29:00 – 00:27:36:00
there were recommendations on the European approach to micro-credentials, to lifelong learning and employability.
00:27:36:00 – 00:27:45:00
And at MIT, we have the micro masters programs. But what role will micro-credentials play in the medium term?
00:27:45:00 – 00:27:57:00
For instance, what do you think? What do we need in order that they are recognized by institutions or companies?
00:27:57:00 – 00:28:04:00
That’s a good, very good thing. First of all, are they valuable? I’m going to offhand say, incredibly so.
00:28:04:00 – 00:28:13:00
I mean, for all of us who were in and we, I mean, there’s a bunch of issues or considerations in your question.
00:28:13:00 – 00:28:22:00
Are micro-credentials valuable? How do we advance their use? I will even add what are some of the cautions that we need to take.
00:28:22:00 – 00:28:29:00
And then there’s this whole issue that there are micro-credentials and then there are digital credentials, right?
00:28:29:00 – 00:28:41:00
Because we are also working on that at MIT. The thing is, when we started doing open, you know, and when we, even the old open days,
00:28:41:00 – 00:28:48:00
and I’m talking about the early 2000s, when we talked about the value of open, we said boundarylessness.
00:28:48:00 – 00:28:57:00
One thing that courses, you can take courses from anywhere, courses travel across the ecosystem, industry, education.
00:28:57:00 – 00:29:09:00
It makes that boundary blurred, right? And in fact, was it you who told me or somebody who mentioned that people are allowing industry offered courses
00:29:09:00 – 00:29:14:00
and giving credentials at their institution? Maybe it was one of the attendees yesterday.
00:29:14:00 – 00:29:16:00
Industrial doctoral programs.
00:29:16:00 – 00:29:21:00
Yeah. So, in fact, industrial doctoral program, we are working with Latvia in a thing.
00:29:21:00 – 00:29:31:00
But people saying that the training programs that industry offers, universities are giving credits for that, you know, which is, which is that bidirectionality is a very nice thing for a thing.
00:29:31:00 – 00:29:44:00
But we also talk about modularity. The fact, in fact, the project that we did here in UOC with OKI was saying, how can we take content applications and move it from institution to institution, right?
00:29:44:00 – 00:29:51:00
So, modularity and moving was there. And if you can attach credentials to the modularity, why not?
00:29:51:00 – 00:30:03:00
Because people want some indicator of completion, and the marketplace, when it’s recruiting people, hiring people, wants that stamp of approval or the stamp of accountability.
00:30:03:00 – 00:30:07:00
And if you have micro-credentials, there are two advantages, and we are seeing this.
00:30:07:00 – 00:30:24:00
First of all, yesterday when I talked about the micro-masters, the fact that the Walmarts and the IBMs and all these industries saying, OK, we’ll recognize that, we’ll give you a job, which means people are voting with their feet, saying, OK, we recognize these credentials.
00:30:24:00 – 00:30:36:00
There are programs now, we all talk about stackable credentials, that we can do different combinatorials of these, because they’re micro, they can be stacked more easily for different kinds of pathways.
00:30:36:00 – 00:30:38:00
So, companies are taking a new role in this.
00:30:38:00 – 00:30:44:00
No, recognizing this. We want the competencies with these kinds of arrangements.
00:30:44:00 – 00:31:05:00
Look, I think what we are doing is create, you know, from the, let’s say if the learner is a customer, we are giving the learner many more options and combinatorials to compose their experience and their profile, so that they become useful and attractive to different…
00:31:05:00 – 00:31:12:00
Now, you mentioned lifelong learning, and that is really critical, because, you know, I talk about displaced learners.
00:31:12:00 – 00:31:19:00
Displaced learners are not just people who are geographically displaced, but also people who are vocationally displaced.
00:31:19:00 – 00:31:28:00
I’m in a job, I’m stuck, I can’t leave, and I don’t have enough time to go for a whole semester, but I can go in the evening and do this.
00:31:28:00 – 00:31:38:00
So, that’s displacement. I’m vocationally displaced, and I can take these modules, modular courses, I can get credentials for it, I can do it over time.
00:31:38:00 – 00:31:44:00
So, it says that you don’t have to do education in that period of time.
00:31:44:00 – 00:31:54:00
You know, I was telling, I think, Àngels, or maybe, you know, all the right-hand side of the stuff, you know, that she had on the slide saying, here’s what the future could look like.
00:31:54:00 – 00:32:00:00
All those things we can do around modularity, micro-credentials, and that’s a good thing.
00:32:00:00 – 00:32:08:00
We also, some of the challenges we look, we had this in the old model of multi-campus universities.
00:32:08:00 – 00:32:14:00
Courses even won’t transfer within the campuses in the same university, and we have come to a different place.
00:32:14:00 – 00:32:21:00
Now, we have a different kind of challenge. How did a module from here with this credential interoperate with that credential over there?
00:32:21:00 – 00:32:26:00
Right. And there’s a lot of work going on with credential interoperability.
00:32:26:00 – 00:32:31:00
We have started something called the Digital Credentials Consortium, group of institutions.
00:32:31:00 – 00:32:37:00
You want to look at that. There’s a platform you have created for credential interoperability. How can credentials move?
00:32:37:00 – 00:32:46:00
So, now that you mentioned the challenges, and we need to close this conversation. I would love to continue, but to close it.
00:32:46:00 – 00:32:55:00
What are the main challenges you are currently facing at MIT right now? What are the main points that…
00:32:55:00 – 00:32:57:00
In the whole orbit?
00:32:57:00 – 00:32:59:00
Specifically in education.
00:32:59:00 – 00:33:09:00
Well, you know, I mean, this specifically in education, look, and this is again something related to AI, right?
00:33:09:00 – 00:33:16:00
It’s an opportunity. We want to say, how will AI help us to do better what we do?
00:33:16:00 – 00:33:24:00
How will it allow us to extend what we do well to places that we don’t typically engage with, serve?
00:33:24:00 – 00:33:32:00
And that’s a very, that’s an important challenge because both those are tied to our mission.
00:33:32:00 – 00:33:42:00
And, you know, I mean, it is one of the things that we all recognize is that, and this is another part of the AI thing.
00:33:42:00 – 00:33:50:00
It’s content is changing, the landscape is changing, the labor market is changing, right?
00:33:50:00 – 00:33:56:00
How do we focus on what is, what are the invariants? What do we keep that we value?
00:33:56:00 – 00:34:03:00
You know, we talk about problem solving behavior, active learning, all these things are things that we want to keep.
00:34:03:00 – 00:34:09:00
How do we recontextualize them, given the new opportunities that technology brings and take it to them?
00:34:09:00 – 00:34:16:00
You know, that is how do we think about this post-pandemic institution?
00:34:16:00 – 00:34:23:00
There are some habits, I mean, I’m sure you experienced that, you know, what people coming back to the workplace, right?
00:34:23:00 – 00:34:28:00
Students coming back over here, but we learned a thing or two which are very exciting during the pandemic.
00:34:28:00 – 00:34:33:00
How do you bring that in? So how do you navigate this new world?
00:34:33:00 – 00:34:40:00
It is genuinely a new world. And, you know, but the good news is that it’ll give us some interesting innovative possibilities.
00:34:40:00 – 00:34:42:00
That’s, that’s, that’s a thing.
00:34:42:00 – 00:34:53:00
Thank you so much Vijay Kumar.
For sharing these insightful, these valuable insights and perspective on these challenges.
00:34:53:00 – 00:35:00:00
My pleasure. You provoked some, through some very good questions, you know, and as you know, there are complex responses to all this.
00:35:00:00 – 00:35:05:24
Thank you so much. My pleasure. My pleasure. Absolute pleasure. Thank you.