P-A.I.R.: A Structured AI-Replication Framework for Active Learning in Introductory Physics
TL;DR
This study introduces the Physics AI-Replication framework, in which students identify challenging problems, use AI to explain underlying concepts and solutions, generate similar problems, and practice independently before reviewing answers.
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.