Reliable generation of isomorphic physics problems using Generative AI with prompt-chaining and tool use

TL;DR

This paper introduces a method using generative AI with prompt chaining and tool use to reliably generate large sets of isomorphic physics problems, improving quality and control over variations.

Contribution

The paper presents a novel approach combining prompt chaining and tool use in generative AI to produce high-quality, diverse, and verifiable isomorphic physics problems.

Findings

Prompt chaining yields higher quality outputs than simple prompts.

Using Python code interpreter enables automatic validation and diagram generation.

Generated problem banks are comparable or superior to existing methods.

Abstract

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.