Teaching and Learning with AI: Three Priorities

Gerry Bayne: Welcome to EDUCAUSE Exchange, where we focus on a single topic from the higher entity community and hear insights, perspectives, best practices and more. Generative AI is pushing teaching and learning away from a model centered on producing answers, papers, and other academic artifacts, and toward a model that puts more weight on process, judgment, reflection, and applied thinking. If students can generate content more easily, then the harder question becomes what they understand, how they make decisions, and how they use knowledge and context. In these conversations, that shift surfaced again and again, not as a reason to retreat from AI, but as a reason to get more intentional about what learning should look like now. Here are three themes that emerged.

Brian Williams: Right now, I could produce the essay, right? I can produce the report. I'm in accounting. They can produce the accounting reports. Right. Like they can't and they will. And so it's not so much about the end report, although it is still important, but it's about how you got there in that thinking process. And one cool thing I've seen is, you know, some professors doing more verbal things there, but also there are some cool uses of AI on the kind of front end of, having students actually kind of explain or create videos of saying like, this is what I'm doing and here's how, and kind of live learning back and forth and that way,because at the end of the day, like, anyone can use these machines, but what you bring is your own unique experience and knowledge and so that's like a pretty cool thing. But there is a change, right? It's a change for us as professors. It's a change for students.

Claudia Arcolin: Faculty are modeling the use of generative artificial intelligence. They ask students to document how they're using artificial intelligence, what prompts they use, what they've accepted or rejected, how this changes their thinking process? This is like a good starting point when we think about artificial intelligence in assessment. So again, the key is to use artificial intelligence as a way for, critical thinking, deeper thinking. And, brainstorm ideas, helping our students to maintain their voice, to maintain their perspective.

Brad Wheeler: And that's why the artifact economy has to collapse. Because right now, the artifact A, I give you an assignment, you give me a piece of homework, and students start to view AI is just the transporter. You know, it just takes you from work to do. Now, I don't have work to do. I came to play soccer. It has to become more meaningful, more engaged. I have no faith whatsoever in hide the calculator, we're going to lock him in a small room. The blue box. How do we start thinking about, accrediting and assessing? Learning more in a game tape developmental way? And how do we do that at scale? Big public places that may not can do boutique kinds of arrangements. So bringing ministry to the point of view, I think you invest in those things that retool a rebuild the core of the Academy in our teaching and learning mission to deal with the fundamental pace of change.

Lance Eaton: One of the things I'm excited about is reflection. Reflection is this metacognitive workhorse of learning, and it's a practice. So if we can have students engage with a chat bot, have a ten-minute conversation, ten minute reflective conversation with a bot that's going to keep asking those probing questions. It meets the goal of reflection. It also teaches the meta skill of reflection because if you're early in your reflection practice, you don't know those questions to keep probing yourself and moving through. So it's just like that as an example I think is really important of like that. That unlocks a lot, that changes the relationship. It changes the idea of like staring at a blank white page and actually just conversing and figuring out and lowers that bar of like, it has to be formatted and blah, blah. No pass in the chat log.

Claudia Arcolin: A cool project that we're working on is to use artificial intelligence to create different modalities of how content are presented to students. I believe that artificial intelligence can be enhanced and, can use the same framework of universal design for learning. So I encourage faculty to use artificial intelligence to create, interactive, lessons to create opportunities for students to add content.

Brian Williams: You still need those fundamental facts from your disciplinary domain, right? Like to know when it's right, when it's wrong and to guide it. But once you have those, then it can really amplify what you already know. So, what I've seen happen is like the most effective users of AI across many areas, right. There are people who have a pretty good knowledge base in that area, and then they're using add amplify, and then they can say like, you know, create a draft of this or give me ten ideas or, you know, critique ideas, but then they're the human in the loop and they're able to say like, okay, well, this is really good. I never thought about this. You know, like my career is like in business, right? So if I was go for business stuff, there would be things I've never thought of that are great. There also be things I'd never thought of that sound awesome that are like, bad, right? You know, they're unethical or illegal or impossible, but you might not know that unless you have that background. And so that's kind of where I've seen the like, a lot of effective use is still emphasizing the importance of domain expertise. But then understanding these tools really can amplify that in a really big way.

Brad Wheeler: The human's got to be smart enough to know where the machine is contributing and saving time, creating more value, or where, you know, the, the machine is, frankly, being deceptive with you or making stuff up or flattering you. So, we're in a journey to develop that. But think about that. A year ago, we were both conversations were rather knees. A year from now, they will be deeper and deeper. Two years hence, the remake of the Academy and our ability that the competencies in the faculty to structure the curriculum, to have these experiences in sequence, that build on each other, hugely important. And that's why the artifact economy has to collapse.

Brian Williams: So, I kind of think of it as having kind of three area. So, there's foundational product engineering. Then on top of that, there's using AI as a thought partner and then using AI as like a productivity amplifier. So for example, we do teach fundamental prompting principles. But on top of that there's also lessons like how to get truly creative ideas from AI. Right. Because if you ask for creative idea and someone else ask it for creative ideas, I'll give you both the same idea. Right? So like how do you get past that? In some use cases like that of, like getting, you know, getting actual practical help out of it and your day to day work and trying to make your life better, but also focusing on, to make sense for all of that, like, where does it do? All right. And so just trying to let people know the limitations. Because so right now there's an issue with AI literacy where people are trusting the AI is output a little too much. And so trying to, you know, show these are tools are very powerful for good. But also you do need to have some skepticism in their output.

Claudia Arcolin: It's important to create a safe space for our students and for our faculty as well, where they can share their perspectives, concerns, fears, ideas about generative AI. This is something so new that both faculty and students are learning together, and this is a peculiarity of this, of generative AI. And this is something that we can leverage to create a collaborative, learning process, loving experience for both.