AI's Role in Online Learning

It was a few days before the event, and I was a little worried.

I was hosting a virtual panel in October titled "Take It or Leave It: AI's Role in Online Learning" as part of my work at the University of Virginia Center for Teaching Excellence. The event was part of a series that the CTE was co-sponsoring on online education. We wanted to start the series off with a bang, so we decided to use the "take it or leave it" panel format that's worked well on my podcast. I pitch a few hot takes to my panelists, and they decide for each one if they want to "take it" (on the whole agree) or "leave it" (on the whole disagree). It's an artificial binary that leads to nuanced and complex discussion of the issue at hand.

We thought that would be a great format to explore AI's role in online learning, because that's a topic with plenty of hot takes! And it's one that benefits from the perspectives of faculty making informed, intentional decisions about the role of AI in their teaching and in their students' learning. I was excited by the panelists I had recruited for the event: Michelle Beavers, associate professor in UVA's School of Education and Human Development and one of UVA's Faculty AI Guides; Sara McClellan, assistant professor in UVA's School of Continuing and Professional Studies and someone who had impressed me while serving on a similar panel in the spring; and Leo Lo, brand new dean of libraries and university librarian at UVA, whose work on AI literacies in the library world I had been following for a while. I was particularly excited to have Leo on the panel since he had agreed to do it before his official UVA start date!

What worried me is that all three panelists were, as far as I could tell, fairly eager to use generative AI in their teaching and other academic work. Leo has been helping librarians around the world build their AI literacy, Michelle keeps trying out AI tools that I hadn't heard of yet, and Sara, well, Sara was a bit of a "yellow light" when it came to her students' use of AI, but she was definitely exploring her options in this space. The "take it or leave it" format works best when the panelists disagree with each other, at least a little. Would this panel be too similar in their views on AI? And perhaps more importantly, could they speak to faculty colleagues who are more skeptical or even resistant to the idea of AI in teaching and learning?

I spent a couple of days working my network to see if I could find someone at UVA who teaches online, knows a lot about generative AI, and has adopted a "red light" policy toward AI in their courses. Maybe my network isn't big enough, but I came up short. I decided to go ahead with the panel, preparing myself to play the role of devil's advocate if the panelists were too rah-rah on generative AI.

I needn't have worried! Michelle, Sara, and Leo brought an informed but critical lens to the panel discussion.

For instance, when I asked them to respond to Scott Latham's argument that "AI-taught courses will become the dominant paradigm" in higher ed, Michelle and Sara said "leave it," noting that AI can't replace the teacher-student relationship that's so important to learning. Sara, who teaches public policy, pointed out that neither is the technology ready for such a primetime role nor are the "human systems" that would be involved in such a change likely to move that fast. Leo was a "take it," mainly because he feels it's hard to predict where the technology will be in twenty years. Are we just at the "dial up" phase of AI? If so, he can imagine very useful roles for AI in education in the future, particularly in technical topics. But he, too, noted that "humans will always prefer to have human interaction."

When the panelists were asked to respond to Zahid Naz's thesis that "the unchecked use of AI could ultimately undermine the very intellectual rigor that makes higher education meaningful," all three said "take it." Leo pointed to all the pressure students are under to "get the grade," pressure that can lead them to use AI in ways that shortcut their learning. He said there needs to be a check, and Michelle agreed with him. She said that AI can be a "cognitive partner," helping students move toward deep learning, but instructors have to design for the productive struggle that learning requires. That kind of design work is labor, and Sara noted that many faculty in higher ed high course loads that prevent that labor. All three panelists are exploring and sometimes embracing AI in their teaching, but all three argued for the importance of guardrails around AI's role in learning.

We covered a lot of ground during the panel, including discussion of three other recent essays on AI in higher ed. Kevin Gannon's essay called for colleges and universities to include more AI skeptics in policymaking, and all three of our panelists were a "take it" on that point! But they also agreed with Ray Schroeder that higher ed needs to do a better job preparing students to enter a workforce equipped with generative AI. And they agreed with Chad Hanson that the advent of AI is an opportunity to refocus the work of teaching and learning on process over product. While there wasn't as much disagreement among the panelists as maybe I had hoped, the trio certainly explored the topic from many different angles, helping us listening to better understand this complex decision space.

And you can be one of those listeners! You can listen to an audio recording of "Take It or Leave It: AI's Role in Online Learning" on this week's episode of the Intentional Teaching podcast. Just search "Intentional Teaching" in your podcast app, or listen on the website.

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