用AI批量生成结构一致的物理题，自动验证答案并画图。
Reliable generation of isomorphic physics problems using Generative AI with prompt-chaining and tool use
- 通过提示链和工具调用控制题目数值与空间关系。
- 生成题目质量更高且更一致，支持自动验算和简单绘图。
- 适合教师快速创建个性化测验或自动化教学内容。
我们提出一种使用生成式AI服务（如ChatGPT）生成大量同构物理题的方法，结合提示链与工具调用。该方法可精确控制结构变量（如数值、空间关系），同时支持题干内容的多样化变化。通过调用Python代码解释器，实现自动解题验证与简单图表生成，克服了现有基于大模型方法的关键缺陷。我们构建了两个同构题库，并与两种更简单的提示方法进行对比。结果表明，提示链方法产生的输出在质量和一致性上显著优于非链式提示。此外，我们验证了生成式AI可用于评估所生成题目的质量。本工作展示了对普通教师而言高效、可扩展的题目生成方式，为个性化自适应测试与自动化内容开发开辟新路径。
原文摘要 · Abstract (English)
We present a method for generating large numbers of isomorphic physics problems using generative AI services such as ChatGPT, through prompt chaining and tool use. This approach enables precise control over structural variations-such as numeric values and spatial relations-while supporting diverse contextual variations in the problem body. By utilizing the Python code interpreter, the method supports automatic solution validation and simple diagram generation, addressing key limitations in existing LLM-based methods. We generated two example isomorphic problem banks and compared the outcome against two simpler prompt-based approaches. Results show that prompt-chaining produces significantly higher quality and more consistent outputs than simpler, non-chaining prompts. We also show that GenAI services can be used to validate the quality of the generated isomorphic problems. This work demonstrates a promising method for efficient and scalable problem creation accessible to the average instructor, which opens new possibilities for personalized adaptive testing and automated content development.
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