Gen AI in Proof-based Math Courses: A Pilot Study
This addresses the challenge of integrating generative AI into proof-based math education to support student learning, though it is incremental as a pilot study.
The study investigated student use and perceptions of generative AI in three proof-based undergraduate math courses where AI use was permitted, analyzing engagement, usefulness, and limitations to inform teaching practices.
With the rapid rise of generative AI in higher education and the unreliability of current AI detection tools, developing policies that encourage student learning and critical thinking has become increasingly important. This study examines student use and perceptions of generative AI across three proof-based undergraduate mathematics courses: a first-semester abstract algebra course, a topology course and a second-semester abstract algebra course. In each case, course policy permitted some use of generative AI. Drawing on survey responses and student interviews, we analyze how students engaged with AI tools, their perceptions of generative AI's usefulness and limitations, and what implications these perceptions hold for teaching proof-based mathematics. We conclude by discussing future considerations for integrating generative AI into proof-based mathematics instruction.