P-A.I.R.：一种用于基础物理主动学习的结构化人工智能复制框架
P-A.I.R.: A Structured AI-Replication Framework for Active Learning in Introductory Physics
浏览论文内容
中文总结 AI 辅助
研究针对学生使用生成式AI工具引发的教学问题，介绍P-A.I.R.框架，学生借此识别问题、借助AI理解概念等并独立练习。调查显示学生概念理解提升、自信得分高，结果支持框架设计，为AI融入物理学习提供实用方法。
中文摘要 AI 辅助
学生中生成式人工智能工具的使用日益增加，引发了一个重要的教学问题：如何构建人工智能以促进主动学习而非被动寻求答案？本研究引入了物理人工智能复制（P-A.I.R.）框架，学生在该框架中识别具有挑战性的问题，使用人工智能解释基本概念和解决方案，生成类似问题，并在查看答案之前独立练习。来自39名基于代数的物理专业本科生的调查数据表明，几乎所有参与者在整个学期参与P-A.I.R.后都报告概念理解有所提高，自信得分始终高于量表中点（平均值 = 7.03；范围5 - 9）。概念清晰度与复制帮助之间的强关联（斯皮尔曼r = 0.582，p < 0.001）支持了该框架的设计。定性研究结果突出了概念澄清和结构化问题解决。这些结果表明，P-A.I.R.为将人工智能整合到物理学习中提供了一种实用方法。
英文摘要
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.
