Educating for the Future: Post-Secondary Innovation

John Stackhouse: [00:00:00] Hi, it’s John here. Welcome back to Disruptors and CDL: The Innovation Era. A big welcome back to my co host, Sonia Sennik, who’s CEO of Creative Destruction Lab.

Sonia Sennik: Great to see you, John. In this special series, we’re exploring the transformative ideas and technologies shaping Canada’s future and the people leading that change.

And if you’ve been listening to The Innovation Era, you know we’ve been especially focused on AI.

John Stackhouse: If you’re on a college or university campus, or just happen to know someone who is, you know it’s midterm season. That’s a good time to pause and assess what’s been learned. And you could say the same about the Gen AI revolution.

Most of us have probably tried ChatGPT, but if you’re like me, you probably feel you’re nowhere near ready for final exams.

Sonia Sennik: Nowhere is the Gen AI revolution having more impact and with it potentially more controversy than in our schools. And that’s what we’re focusing on today.

John Stackhouse: I came across a really interesting new research report, Sonia, from KPMG [00:01:00] Canada that shows a majority of students are now using Gen AI in their schoolwork.

Now, it’s largely for generating ideas, researching, and editing. Yet few are willing to tell their teachers that that’s what they’re doing.

Sonia Sennik: Two thirds of the students that were surveyed felt that using Gen AI was actually a form of cheating and they were reported feeling kind of ashamed that they were using it.

John Stackhouse: This is so fascinating. Well, there is that guilt factor. A majority of students say they want their teachers and their schools to incorporate Gen AI more and more in everything on campus.

Sonia Sennik: One of the things I found was very curious was that faculty reported being less inclined to use it, but a few are trying to catch up.

They’re seeing its value in not just tracking students. But reducing the often mindless administrative inefficiencies of the teaching process itself.

John Stackhouse: Now, before we get to our first guest, maybe a last point from the KPMG study, which I found fascinating [00:02:00] that almost three quarters of professors and instructors are adjusting their curriculum because of AI.

So to make sense of what’s happening out there, we’re now joined by one of Canada’s most respected and outspoken academics, Janice Stein. Janice is the founding director of the Munk School of Global Affairs at the University of Toronto, where she’s also Belzberg Professor of Conflict Management. She holds honorary doctorates from five universities around the world.

She’s written eight books and hundreds of articles rooted in her research. That sits at the intersection of cognitive science, psychology and international politics. Janice was also in some ways an inspiration for this series. She’s helping shape a new AI strategy for Canada with a lot more emphasis on adoption.

Janice, welcome to the podcast.

Janice Stein: Pleasure to be with you, John, and with Sonia.

John Stackhouse: You’re renowned in the field of conflict studies, so what got you interested in AI?

Janice Stein: AI [00:03:00] is the future of conflict studies. If you’re thinking about the future battlefield, every weapon of any significance is going to be powered by AI.

AI, space, satellite, target identification. It is impossible to think even five years out of a battlefield that is not transformed by AI. And I needed to understand the technology. It’s not good enough just to read about it. You actually have to understand.

John Stackhouse: That makes you a great teacher if you are always a student.

Sonia Sennik: And Janice, speaking of the classroom where you’ve been teaching for nearly six decades, what are some of the more enduring lessons that you’ve learned about the intersection of technology and education?

Janice Stein: People learn best by doing. They don’t learn best by listening. Despite 800 years, faculty standing at the front of the classroom, that is not the ideal learning environment.

So, if you can transfer the leadership to students, if [00:04:00] they can feel it, if they can touch it, if they can experience it, if they can imagine it, it doesn’t matter what we use, that is what I would call deep learning, as opposed to just listening.

John Stackhouse: I love that definition or redefinition of deep learning. Take us further, Janice, into the deep learning, the real learning experience.

Sonia and I were talking in the introduction about some new KPMG research, which shows, and maybe this is not a surprise, the majority of students are using ChatGPT. And maybe that’s not new. Students tend to innovate first with technology. You’ve probably seen this in a few go arounds in technology.

What can we learn from past experiences that we can and maybe should apply to AI in the classroom and around the classroom?

Janice Stein: I think the easiest analogy here, John, would be to a calculator. When calculators first came on the scene, you had a whole bunch of elementary school teachers [00:05:00] panic about what this was going to do.

to people’s capacity to multiply, right? Well, nobody would take that argument seriously right now. I think we’re in the same place with AI. There was, in the early days, right after ChatGPT was first released, all the university staff turned to issues that are very familiar to both of you. Privacy, safety, things that administrators love to talk about.

And there were seminars and workshops right from the beginning. We had several serious sessions with students, how to use it. And the first assignment in the course was, do an essay on this subject, ask GPT first, and then you rewrite it and tell us how much time it took. How did you verify whether anything in it was accurate?

Now that’s, relatively speaking, time consuming, but the easier question. The harder question for them was, how did GPT build the argument? Are you okay with that? Would you construct the argument differently? [00:06:00] Because that’s a skill. It doesn’t matter what age we’re in. If people can’t do that, they are not going to be leaders in their field.

John Stackhouse: Early days, Janice. But how is this changing the nature of students? Are they different coming out of your courses?

Janice Stein: To some extent, students are freed up, but some of the more routine things, ChatGPT is really helping them, and that’s great. But when we come to the kinds of challenges that we’re talking about, I get two quite different reactions from students.

Oh darn, I only read the summaries, I missed the point, I gotta go back, right? If they haven’t read the argument along the way, they feel at a disadvantage. And that’s what the early data on students are telling us. They want to use it. We need to take away, by the way, any discussion of penalties or ethics.

We have to take that off the table entirely. But at a [00:07:00] deeper level, they’re worried that they’re missing out on some of the higher level learning if they are overly reliant on AI.

John Stackhouse: So you’re saying take away the penalties. I think I get the logic of that, yeah. But are you okay with a student just asking ChatGPT to write a paper for them and they submit it?

Janice Stein: Yeah, as long as they tell me that they have and that’s where it gets really interesting for them. And, you know, this is harder than writing the paper, John, right? ChatGPT wrote this paper. I am confident in the validity of the data. I am confident that this is a well constructed argument, and you should have to tell me why you can answer those two questions, which is not unreasonable.

It’s what you would expect in your editorial role. You would want to know that the evidence is reliable and that the arguments are valid. That’s really hard work. You gotta go back. You gotta look. You gotta read something. And you can’t do that with ChatGPT itself. You can go some way, but [00:08:00] you can’t go all the way with it.

And so my view, there’s a contract between somebody in front of the classroom and somebody in the classroom. It’s not about cheating, and it’s not about catching cheating. So we reached an agreement right at the beginning. This is how we’re doing things. I’m going to use chat GPT. So bring it out in the open.

Sonia Sennik: From a teaching perspective, one of the most common questions I’m guessing a professor or a teacher asks themself is, okay, what do I teach next? What does this group one to many? What do I need to guide them to next? And large language models and generative AI is taking away that one to many. A student can have an engagement with a large language model and ask it questions.

As you said, get it to coach them through some things they may not understand yet. So with that utility on a personal level for students, we’re still seeing that faculty and admin at post secondary education institutions seem to be a bit behind the curve on adopting it or embracing it. What do you think they need to do to catch up?

Janice Stein: So I’m going to answer this question this way, Sonia. They’re behind the [00:09:00] curve, there’s no question, and they’re ahead of a curve. In other words, for coaching, for tutorials, for individual customized pathways to learning, AI is great. And I have no doubt that within five years, the classroom will look very, very different.

But here’s one way the classroom is probably not yet looking good enough, that as we move through higher and higher levels of responsibility, team based decision making is absolutely critical. And in the university, in the classroom, I’m getting more pushbacks from students about doing their work as part of a group, in a team, than I am about technology, frankly.

That’s the real hill to climb. Students cannot accept that their grade, their performance, is partly dependent on the performance of others. And they don’t understand that part of what they have to learn is not only the what, but how to get the [00:10:00] best of everybody and not to worry about that person is not doing as much as I am, but that person has skills that I don’t have.

They’re studying high level decision making. They see that happens to teams and groups all the time, but they can’t get themselves there.

John Stackhouse: I think you’ve just nailed a critical point that we’re living in a hybrid world. And the more technology we have, we also need a lot more humanity and those human skills of working with others, collaborating and communicating as people.

How are you finding that balance? And where in the education system do we have to really focus on creating that balance?

Janice Stein: You have to be very intentional in the way you design the learning environment. So you have to build in that teamwork and it’s got to be part of the requirements. What good is it if you do great research and you have great ideas but you can’t communicate?

These to me are the critical skills that we’re going to need going [00:11:00] forward. The capacity to de silo and to really work in a larger group and to get the best ideas from everybody, and then to communicate over those barriers. University has to prepare people to live in that world. It has to. So how did we do this?

First of all, it’s going to be slower than we like. We’re going to try some things that are going to fail, great. That’s another thing we need to model, that it’s okay. Because if you can’t fail in the classroom, you’re sure not going to fail when you take your first job in the private sector or anywhere else.

We have to build a culture of experimentation. Try it, see what works, adopt what works, and students are partners. And the best universities do that, they allow you to experiment. You know, I noticed in reading some of the research on this, that there’s a big focus on what university policies should be. I wouldn’t invest a [00:12:00] huge amount of effort there.

What you want them to do is have an enabling environment. Ultimately, let the faculty on the ground do the work, partner with students and see how much AI will change the learning environment. We will get the best results that way.

Sonia Sennik: So Janice, what do you see is that right balance between having these structures and institutions, places curious people can go, and the agility and flexibility for these institutions to keep up with the constant change?

Janice Stein: You know, I’m not big into binaries. It just doesn’t work. It’s and, right? I think universities can do everything they’re doing now and so I push our alumni all the time. We need you back for a weekend. And we have some in place and we have some all over the world and we can do hybrid and we could do all forms of learning, but they have to come back.

And that’s, I think, the biggest message. We need leaders like you, John, you know, to send that, you know, your [00:13:00] own teams that you lead, but more generically, as you look out at Canadian society, we need you back. And it’s a message that if you’re not learning in this world, where the pace of change is probably faster than it ever has been, how can you lead?

John Stackhouse: That’s a great message Janice. Gen AI is not about replacement, it’s about addition. I wonder as we move to close Janice, you mentioned how the classroom is going to be different five years out. Take us out to the 2030s. What in your imagination and maybe your vision does the learning experience as well as the classroom look like in that age?

Janice Stein: 2035 is an eternity away where we will have technologies, I think, that you and I can’t even think of right now. So how is the classroom going to be different in a fundamental aspect? I think it’s going to be the same, that it is the place where people come to argue and to think. And [00:14:00] to refine what they’re arguing, to walk out thinking differently than they walked in.

That, to me, is the essence of a classroom. Now, everything else I think we can design differently, depending on the technologies that are available to us. But that probably can’t go away, nor should it go away, actually. Because we’re social beings. We think better on some issues in a group, some issues we think better alone.

We want to be able to preserve that flexibility for people, but that social component of learning is so important and communication. That’s not going to change.

John Stackhouse: Wow. Listening to you speak, I’m just thinking Socrates had it right.

Janice Stein: It’s about dialogue. It’s about dialogue.

John Stackhouse: Exactly. It’s about dialogue. So we have the Socratic approach and dialogue.

But a heck of a lot more information thanks to the internet and then ways to synthesize, to understand, to curate, and maybe organize that [00:15:00] information in ways that were kind of overwhelming just a few years ago. And that’s one of the advantages of Gen AI. It’s an organizational tool, yes. Not necessarily a thinking tool.

Janice Stein: That’s right. It’s a huge organizational assist, and in an ideal world that I can imagine, it frees up time for more thinking.

John Stackhouse: Let’s free up time for more thinking. I can’t think of a better summation of this wonderful conversation, Janice. That should be a motto on all our walls. How can I free up more time for more thinking?

Janice, thank you so much for being on the podcast.

Sonia Sennik: Well, great to be with you and Sonia. That’s a great perspective on what AI is doing to our classrooms. But that’s just the classroom. Learning takes place in all sorts of ways, in all sorts of places, and few Canadians have done more to advance digitally enabled learning than John Baker.

John is founder of Desire2Learn, or D2L, one of Canada’s most successful and enduring edtech companies. He created the company in 1999 while studying at the University of [00:16:00] Waterloo. Today, D2L is a global software company and John is one of Canada’s most respected tech entrepreneurs. John Baker, welcome to the podcast.

John Baker: Thank you very much. I’m looking forward to the conversation.

Sonia Sennik: You founded D2L over 20 years ago with a vision to transform education through technology. Can you share a bit about what inspired you to start the company and how the landscape of digital learning has changed over the last 25 years?

John Baker: So I was in my third year of university, and I was wrestling with one key question.

What’s the most important problem that I could solve that would have the biggest impact in the world? And I couldn’t think of anything bigger than transforming the way the world learns. And we’re at a stage today where, because of the technologies in place, enables us to do that at scale, where in the past we wouldn’t have been able to do it.

Things like competency based learning, where instead of just simply passing or failing an exam, you get the ability to demonstrate mastery on the specific learning outcomes that you’re striving towards. So probably a good example to really understand that is if you’re going to go in for a heart surgery, you want the surgeon that’s demonstrated mastery of that procedure many [00:17:00] times, not the one that just passed their medical exam.

But what I’m probably most excited about is AI. AI is very much like the internet in the early days, a new way of doing things that’s going to really have a big impact on education globally.

John Stackhouse: And John, that’s a perfect segue into the theme of this episode. How is AI changing and challenging the way we learn?

So maybe give us a sense of how it’s changing D2L and where you see AI taking the company.

John Baker: The first thing is a lot of people get hung up on the risks attached to AI. That’s a natural tendency. If you’re a skier and you’re skiing down through the glades on a big mountain, what you want to avoid is looking at the trees for too long.

Otherwise, you wind up in one. So I break it down into sort of like five key paths that universities and schools and companies all over the world need to follow. One is, yes, working on the risks, understanding academic integrity issues, understanding if students are using these technologies to just have a shortcut or are they, Are they using it as a productivity tool?

Second is doing the research around this new [00:18:00] intersection point between AI and the scholarship of teaching and learning. AI is going to change how people learn. It’s going to change how people get assessed. It’s going to change how we do tutoring. And so there’s a lot of work that needs to be done in terms of, well, exactly how do we embrace this new technology?

Just like we did with the internet in the past. It was another disruptive technology that came in and changed how we taught, assessed, and tutored folks. Third is, now that we know that, how do we change our curriculum? How do we change how we teach students computer science or engineering or nursing now that this new technology is in place?

Fourth is how do we upskill the workforce that’s already there, not just prepare the current generation of students, but the workforce? And then the fifth, how do I personally start to use this technology to improve my own workflows and get better at the things that I do each and every day?

John Stackhouse: I wonder if you can also take us a bit deeper into how this is working at D2L.

You’ve got a new platform, D2L Lumi, maybe give us a sense of what that is seeking to do.

John Baker: So with Lumi, what we’re doing is really starting with the productivity enhancers for educators. So how do I take [00:19:00] your course content and now turn that into interactive content that would engage and inspire people?

How do I then take that interactive content and turn it into assessments that could really help address whether the students have actually learned what they’re being taught? Virtual tutors is another example of that. The application of AI to support the student experience so they can query just like they would a chatbot, if you will, to understand the material that they’re being taught.

Sonia Sennik: Folks that are listening may be thinking, okay, these AI tools seem great, but when I was in a classroom, my connection with my teacher mattered so much, or my connection with my tutor mattered so much. And you can think back to that person who made the difference in your learning journey. What would you say to folks who are thinking through whether or not AI erodes that human connection aspect of learning?

John Baker: I think that is at the heart of what we’re doing. AI is being used to support giving you more time to build that human connection. More time to build a connection with your classmates, with your faculty member, your teacher. It is going to free you up to be able to build better relationships, get [00:20:00] better feedback, and be more inspired.

That’s why we’re doing it. We’re not leveraging this technology for the sake of the technology. We’re using it to actually build a better educational experience, and at the heart of a better educational experience is more human connection.

Sonia Sennik: So how are your teachers and educators adjusting to this Gen AI revolution?

John Baker: So we’ve been using Lumi now just for a few months, but so far it’s been a home run. Faculty that are using it are loving the fact that it can help them to do things that would normally take them hours or days or weeks and do it within minutes. So the idea of taking a PowerPoint or a PDF or Word document and convert that into beautiful, engaging, interactive, inspiring content used to take a long time.

Now we’re doing it within, in some cases, seconds or minutes. And then being able to craft really good assessments of learning is also hard work. And many educators were never trained on how to do that. And so this starts to fix those issues. And what we’re seeing is More students getting A’s and B’s, more students completing, but more time for giving feedback.

It really is liberating in terms of looking at it as a [00:21:00] productivity tool versus as a replacement for the educator. I think it elevates the opportunity for the educator to do more group work, problem based learning, case studies, all kinds of other things that are going to be more engaging in that class experience.

John Stackhouse: D2L is a global company and you get to travel the world and meet educators in all sorts of countries. I think you’ve just come back from Asia. What’s most exciting out there in the world in terms of AI applications in education?

John Baker: I’ve been at this for 25 years. John, and I’ve never seen more willingness to embrace a new technology coming in than I am seeing it today.

I’m on a Strive AI task force with the State University of New York, where the whole university system’s rethinking how they teach and how they support workforce upskilling and changing almost everything. If I go to Singapore or Hong Kong, or I was also just in South Africa, or I was even just with one of the top universities here in Canada this morning.

Everybody is talking about it. What they don’t know is how to actually go through the transformation. And that’s where we’re trying to come in and be a partner on that journey. We’ve been working on machine learning and [00:22:00] AI for over a decade. So it’s bringing that expertise to the market now and bringing to life real technology that really has a big impact.

And then working with our clients almost as design partners. Let’s test this out. Is virtual tutoring going to work for you or not? Why not? How do we get it to be tuned such that you will want to deploy it for everybody? I think, unlike internet, I think we’ll see a transformation here much, much faster.

Internet really broke down time and place. You could take learning from anywhere, get it at any time of the day. So that was huge in terms of impact. But with AI, all of a sudden, we can maybe do a four year degree in six months. Because we can adapt learning to you. There’s another dimension. Maybe we can become better at the profession we’re pursuing so we can build better mastery.

Like I gave you that as an example with doctors being better because they’re able to to demonstrate mastery of every learning outcome because they have more time to actually perfect their profession. And then there’s a third dimension to this too, which is maybe we can teach things differently. So instead of just teaching how to remember or understand something, we can now do things like create something [00:23:00] or code something.

or analyze a whole bunch of data that we could have never imagined analyzing if we didn’t have AI superpowers, if you will. And so it allows us to not only do what we were always doing in the past with greater efficiency, but allows us to redefine what higher education, what learning could look like in the future.

And that to me is probably the most exciting part of this.

Sonia Sennik: So John, in the context of reimagining education and giving educators or students superpowers, what do we need to most protect in the next two decades of the development of education?

John Baker: Protect? Oh, that’s an interesting question. I think at the heart there, you’re getting at the risks.

The way I think about it is almost like a design challenge. And so we can design our AI to be used in education, to know everything. Even including the answers to the exam questions, or we can choose to limit its knowledge. We can design AI to just be responsive to your demands, or we can design AI to give you a grade for how you interact with it.

We can design AI to have more authority or, or to [00:24:00] care more about you as a student. And so I’m most concerned about making sure we get that design right. To me, that’s critical. I’m also really concerned about shortcuts. The risks are real. And I think the more that we could leverage this as a productivity tool, but without students just taking the simple shortcut.

So, for example, there are AIs that could give you the answer to every question on an exam almost instantly. Well, clearly we don’t want students using those. So, how do we redesign assessment, uh, is going to be a big question for the next few years to support authentic experiences for students, while at the same time not slowing down the educational journey.

John Stackhouse: So John, one of the design opportunities is to rethink the academic calendar and our schools are still, some people like to say they’re designed around the agriculture calendar still, even though very few students have to work on the farm. You mentioned that maybe we could take a four year program and do it in a few months.

AI would allow that. How much do we need to think about the business model and the operating model [00:25:00] of education, both secondary, but particularly post secondary?

John Baker: Well, I don’t think every university is going to support every type of experience, all with lower price points. I think what you’re going to see is more variety of types of education that we can receive. There’s no question these technologies can help make us more productive, which should help address the cost concerns, especially if you look at some of the universities where costs are really skyrocketing. This would be a way to save, in some cases, millions, maybe tens of millions for a university in their overall cost model, which would help them relieve some of that pressure.

But I think at the core, it’s like, well, what do you want that education experience to be? If you want to have the best nurses and best doctors and best engineers coming out of the program, you’re probably not going to get them through the program within six months. You’re probably going to want to spend that time to help them become better researchers, better scientists, better entrepreneurs, better nurses, better engineers.

And so, that will be a choice you make as a university and the cost for those types of programs will probably go higher because you’re spending more time building that better experience for those students. But for others, like let’s say for example you’re a working professional [00:26:00] and you’ve been in the industry for a long time and you just want to go back and get a degree.

Well, you have a lot of experience, so why can’t we just quickly assess what skills you already have and then provide you a personalized pathway that gets you through a four year program in six months. So the ability for us to support a wide variety. of circumstances and needs for the market, that’s going to be exciting versus everyone kind of doing it the one size fits all kind of approach, which is what we’ve been used to for the last few hundred years.

I think that will start to address some of the economic pressures that folks are under.

Sonia Sennik: Fantastic. John, thank you so much for joining us on the podcast.

John Baker: You’re very welcome Sonia.

John Stackhouse: What a fascinating and frankly inspiring conversation. I’ve known John Baker for years. He was an early guest on Disruptors. I don’t think I’ve ever heard him not excited about something.

Here we are in frankly, a post secondary education crisis in the country. It’s really challenging right now to run a university or a college. And along comes an innovator who sees [00:27:00] opportunity here with AI to transform the business model, to improve the education experience.

Sonia Sennik: These conversations were, dare I say it, educational and reinforced how the future of education is at such a pivotal moment.

And as you mentioned, John, post secondary crisis, but also massive opportunity for post secondary innovation and transformation with technology playing a critical role in equipping students and educators with the skills they need to thrive in our very rapidly changing world.

John Stackhouse: I also love how AI is changing the competitive playing field.

This is a chance for small schools, big schools to rethink what they’re doing and loss can go to first pretty quickly.

Sonia Sennik: And what I love is when Janice mentioned, learn by doing, and how important it is for students to get practical experience in the classroom. That practical learning and human connection truly is at the heart of innovating our education systems and processes and both Janice and John Baker reinforced that human connection.

John Stackhouse: What great words to [00:28:00] attach to AI. That’s all for today’s episode of Disruptors and CDL: The Innovation Era. A big thank you to our guests, Janice Stein and John Baker, for their incredible insights into the future of education. And how innovation is reshaping, not just our institutions, but also how we prepare for the challenges ahead.

Sonia Sennik: If you like this episode, leave us a review wherever you get your podcasts. And be sure to subscribe to Disruptors and CDL: The Innovation Era, for more conversations with industry disruptors, innovators, and thought leaders.

John Stackhouse: I’m John Stackhouse.

Sonia Sennik: And I’m Sonia Sennik.

John Stackhouse: This is Disruptors, an RBC podcast. Talk to you soon.

This article is intended as general information only and is not to be relied upon as constituting legal, financial or other professional advice. A professional advisor should be consulted regarding your specific situation. Information presented is believed to be factual and up-to-date but we do not guarantee its accuracy and it should not be regarded as a complete analysis of the subjects discussed. All expressions of opinion reflect the judgment of the authors as of the date of publication and are subject to change. No endorsement of any third parties or their advice, opinions, information, products or services is expressly given or implied by Royal Bank of Canada or any of its affiliates.