“The increasing capability and availability of generative Artificial Intelligence (AI) tools have brought new challenges in our classrooms, especially for computing courses,” says astrophysicist Yao-Yuan Mao. “New pedagogical approaches are clearly needed, and while general guidance does exist, specific implementation depends on our understanding of how Gen Z students use AI tools in class.”

Mao, an assistant professor in the Department of Physics & Astronomy at the University of Utah, was recently selected as a Martha Bradly Evans Teaching Fellow. The fellowship will allow them to develop these new approaches in two phases, the first involving a "field study" in the Computational Laboratory for Classical Mechanics in the Department of Physics and Astronomy.

In the lab, explains Mao, their team will observe how students utilize AI tools for computational tasks. “An undergraduate researcher will document these interactions as a bystander, without participating in student evaluation at all.”

In the second phase, Mao further explains, their team will develop pedagogical guidelines, recommendations and materials based on the field study results, specifically tailoring them for physics computing instructors. “The final product will be a well-structured document containing the field study findings, the detailed pedagogical guidelines and recommendations and a collection of adaptable example course materials.”

Using AI 'responsibly and productively'

Mao’s colleague Jordan Gerton sees the work Mao is proposing as developing a deeper understanding of how AI is being used by students and instructors, “to help students learn to use AI responsibly and productively."

Another colleague Kyle Dawson agrees: "This award recognizes the foresight that Professor Mao has beyond the material for those classes and into how advances in computation such as AI impact our overall educational mission."

Outside of the classroom, Mao’s research work advances the discovery and understanding of low-mass galaxies, Mao’s use of the powerful Rubin Observatory allows them to search for these faint objects, likely increasing the number of known low-mass galaxies by a hundred-fold over in the coming years.

Ben Bromley, also a professor in the department of Physics and Astronomy, explains how these galaxies are “cosmic gems” as they are composed of considerably more dark matter per star than other galaxies more familiar to us like our own, much larger, Milky Way. “That makes each elusive low-mass galaxy that Yao discovers a great laboratory for exploring dark matter’s properties,” says Bromley.

'No-risk, high-reward effort'

Bromley further explains his colleague’s intriguing finds that they serve as key building blocks of bigger galaxies. “Yao's low-mass galaxies together will help transform our understanding of galaxy formation and the emergence of the cosmic web of structure that extends across the universe.” Despite their small size, he says, “Yao’s galaxies can track where mass is, where it’s going and how it is organizing into larger and larger structures. In this way they are like weather balloons, giving key bits of information that help us paint the big picture.”

That Mao is equally adept as a researcher as they are as an instructor and mentor in the classroom, for which they are being recognized by the Bradley Fellowship, perhaps provides the perfect combination for exceptional undergraduate education and learning. This project also fits in nicely with the ongoing discussion of AI in Education hosted by the College’s Center for Science and Mathematics Education.

Concludes Bromley, “The project envisioned by Yao for the [Martha Bradley Evans Center for] Teaching Excellence award, is an inspired no-risk, high-reward effort that will help guide both students and us faculty through uncertain straits ahead.”

By David Pace

For a full list of this year’s 2024-25 Fellows awarded by the Martha Bradley Evans Center for Teaching Excellence at the University of Utah, click here.