Preparing students for artificial intelligence in education

Laurah Turner, PhD, associate dean for artificial intelligence and educational informatics at the University of Cincinnati's College of Medicine, recently joined the For The Love of EdTech podcast to discuss the usage of personalized learning and AI coaches to enhance educational experiences.

Host: You mentioned applied AI. Can you explain how you would define applied AI?

Turner: Yeah, so I think you know, there’s a lot of different AI techniques, but when we’re applying AI, it is, it has to be a specific use case, right? So applied AI and medical education, for example, would be one project that we, a tool that we’ve built, which is to create simulated patient experiences using a variety of AI techniques. So we are creating an experience using AI. So we’re applying different techniques to solve a problem, and that problem is, you know, the experience of interacting with patients very early in medical school. There’s not a lot of opportunities to do that, and it’s also very expensive, and so one way to solve that problem is to use artificial intelligence. Potentially, we’re still exploring the utility. So that’s one application. Another application could be something like curriculum mapping, right, where we have to align the different assessments with the learning objectives and the program objectives, right? It’s very tedious. It’s very prone to error. People hate doing it, at least the teachers, you know, and professors I work with. And so it’s a really good use case. And so we’ve created different, what we call AI agents, but you can think of them as bots that go in and do the mapping for you, and then the human just has to review it. So it’s just any time that you’re, you’re taking different AI techniques and creating a tool or a system to solve a problem. The problem that you’re solving is the application. And so that’s what a applied AI would be.

Host: Okay? So how do you see applied AI making an impact in the day-to-day learning experiences? And how might those innovations inspire K-12 education?

Turner: Yeah, I think, I think that really, any of the tools that we’re creating, even though they are very niche to medical education can be translated across many different domains, including K-12. In fact, a lot of the ideas, or the foundations of what we build, I test out on my own children. First, I have a third grader, and so we’ll, we’ll kind of test certain things and see how she likes it, right? But really, I think the most, the most viable and possibly the most exciting, is the ability to personalize the educational experience in medical education and oftentimes in K-12, it’s very cookie cutter and very rigid, and it’s not flexible for individuals who may have learning delays or may just have a different learning approach or need, right, and or a different timeline, and that’s really, really challenging, and we know that back in the 80s, found by looking at K-12, actually, that if you provided every individual with a personalized tutor, you could augment the performance by up to two standard deviations, so ultimately, making a below average student above average or an average student exceptional. But that’s cost prohibitive, and even if we had piles of money laying around, there’s probably not enough humans to actually do that. So could we augment that experience and solve 2 sigma problem with artificial intelligence, we’ve created, or we have created, and we’re we’re testing AI coaches right now. Imagine having your own, you know other, right, or your own knowledgeable other to go through your entire educational experience and help you. To identify when interventions are needed, because a lot of time, I think the gaps in the deficits our medical students feel like they have to hide, and then that leads to, you know, impacts in patient outcomes down the line, because they’re really concerned with being able to match into a residency program. I can see that same thing, where students in the K-12 environment are probably going to be a little anxious about gaps in their knowledge or their skills, but we’ve seen already that students are much more open with the bots than they are humans, because they don’t have that same kind of anxiety around sharing deficits. Now, of course, there’s always kind of that surveillance culture that has to be addressed, and we’re working on really trying to understand where that sweet spot is, but I think that we’ll be able to figure that out and ultimately break down a lot of the barriers that we’ve had before that were either financial or just, you know, human in nature, right? We don’t have enough humans, and we can augment that with artificial intelligence?

Listen to the rest of the podcast featuring Turner.

Featured image at top: iStock/Vertigo3d.