P-A.I.R.: A Structured AI-Replication Framework for Active Learning in Introductory Physics

Abstract

The growing use of generative AI tools among students raises an important pedagogical question: how can AI be structured to promote active learning rather than passive answer-seeking? This study introduces the Physics AI-Replication (P-A.I.R.) framework, in which students identify challenging problems, use AI to explain underlying concepts and solutions, generate similar problems, and practice independently before reviewing answers. Survey data from 39 undergraduate students in algebra-based physics indicate that nearly all participants reported improved conceptual understanding following engagement with P-A.I.R. across the semester, and self-confidence scores were consistently above the scale midpoint (mean = 7.03; range 5-9). A strong association between conceptual clarity and replication helpfulness (Spearman r = 0.582, p<0.001) supports the framework's design. Qualitative findings highlight conceptual clarification and structured problem-solving. These results suggest that P-A.I.R. offers a practical approach for integrating AI into physics learning.