AI 中文总结
研究利用大语言模型代理工作流程生成同构物理问题，通过结合提示链等技术，在公共网站实现。开发评分标准并测试，89%生成问题可直接用，虽有局限，但显示出基于LLM系统在教学问题生成上的潜力与挑战。
AI 中文摘要
本研究提出了一个基于大语言模型（LLM）代理工作流程生成同构物理问题的人工智能系统。该系统围绕三个实际目标设计：与原始问题保持相同的概念和解决问题结构，改变场景和数值等与结构无关的特征，生成无需专家修订即可直接使用的问题。工作流程结合了提示链、基于代理的验证和在树莓派托管的公共网站内的自动LaTeX编译。为评估该系统，开发了一个八项评分标准，并使用基于微积分的牛顿力学入门课程的13道选择题进行测试。结果表明，89%生成的问题被评为完全明确且可直接使用，但系统也有局限性。结果表明基于LLM的系统在可靠的教学问题生成方面有巨大潜力，同时也凸显了未来发展的重要挑战。
英文摘要
This study presents an AI-powered system for generating isomorphic physics problems using large language model (LLM)-based agent workflows. The system is designed around three practical goals: preserving the same conceptual and problem-solving structure as the original problems, varying construct-irrelevant features such as scenarios and numerical values, and producing questions that are directly usable without expert revision. The workflow combines prompt chaining, agent-based verification, and automated LaTeX compilation within a publicly accessible website hosted on a Raspberry Pi. To evaluate the system, we developed an eight-item rubric and tested the system using 13 multiple-choice questions from a calculus-based introductory Newtonian mechanics course. The evaluation results showed that 89% of the questions generated were rated as fully specified and directly usable. However, the system also showed limitations. The results suggest that LLM-based systems have significant potential for reliable instructional problem generation while also highlighting important challenges for future development.
