AI 中文总结
本研究通过调整框架评估OpenAI的o4-mini生成的物理解答，发现明确提示可提升解答完整性，MAPS指导能让学生更精准评判AI解答，为物理教育研究提供了新视角。
AI 中文摘要
本研究从两个相互关联的视角考察人工智能（AI）生成的物理解答：提示设计如何影响这些解答，以及如何培养学生评判这些解答的能力。针对一个转动力学问题，我们调整了问题分类框架以考察提示变体，采用明尼苏达问题解决评估（MAPS）量表评估OpenAI的o4-mini的响应。明确的提示提升了解答的完整性；不明确的提示和多模态提示暴露出物理推理和正确性方面的缺陷。在学生评估阶段，24个入门物理实验小组在独立解答相关问题或通过基于MAPS的反思问题评判AI生成的解答后，对o4-mini针对该问题的解答进行了评估。仅进行问题解答的小组提出了无批判性或基于错误概念的评判；接受MAPS指导的小组识别出更多符合专家标准的问题，包括省略数值步骤和符号未定义。总体而言，我们的发现为物理教育研究做出了贡献，表明AI生成的解答可作为模型推理基准的基础，并通过基于MAPS的反思提升学生对该推理的评判能力。
英文摘要
This study examines Artificial Intelligence (AI)-generated physics solutions from two connected perspectives: how prompt design shapes these solutions and how students can be prepared to critique them. Using a rotational-mechanics problem, we adapted a problem-classification framework to examine prompt variations, evaluating OpenAI's o4-mini responses with the Minnesota Assessment of Problem Solving (MAPS) rubric. Well-specified prompts improved solution completeness; underspecified and multimodal prompts exposed weaknesses in physics reasoning and correctness. In the student-evaluation phase, 24 introductory physics lab groups evaluated an o4-mini solution to this problem after either independently solving a related problem or critiquing its AI-generated solution with MAPS-based reflection questions. Problem-solving-only groups exhibited uncritical or misconception-based critiques; MAPS-guided groups identified more expert-aligned issues, including skipped numerical procedures and undefined notation. Together, our findings contribute to physics education research by showing how AI-generated solutions can ground both model-reasoning benchmarks and improved student critique of that reasoning through MAPS-based reflection.
Comments6 pages, 1 figure, Physics Education Research Conference 2026
