Active Learning with Generative AI in Introductory Physics: A Multi-Institutional Study of the P-A.I.R. Framework

Abstract

Generative AI tools have become part of how students study physics, yet unstructured use may reduce students' opportunities to work through problems independently. The Physics AI-Replication (P-A.I.R.) framework structures AI use as a four-step cycle: students identify a challenging problem, obtain an AI-generated explanation and solution, request similar practice problems, and solve them independently before reviewing the AI's solutions. We evaluated P-A.I.R. in 11 algebra- and calculus-based introductory physics sections at four U.S. institutions using a common end-of-semester survey. Most students reported improved conceptual understanding (90.9\%) and found the replicated practice problems helpful (97.7\%), and these two ratings were strongly associated ($r_s = 0.58$). Perceptions of both components, as well as post-engagement confidence ($μ= 7.18$ on a 1-10 scale), were consistent across institutions. Post-engagement confidence was associated with perceived improvement in conceptual understanding ($r_s = 0.37$) and the helpfulness of replicated practice ($r_s = 0.35$), but not with how often students used AI ($r_s = 0.02$) or with self-reported GPA ($r_s = 0.13$). Thematic analysis of open-ended responses showed that students valued AI primarily for conceptual clarification and structured problem solving (63.0\%), while their main concerns were AI accuracy (34.2\%) and the need for explanations that guide rather than simply provide answers (29.7\%). These findings suggest that how students engage with AI matters more than how often they use it, and that frameworks sequencing AI assistance with independent problem solving offer a practical, transferable approach for physics instruction.