2025/08/20 by Zhongzhou Chen, Chen, Zhongzhou · 1 voice
Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Physical sciences #Intelligent Tutoring Systems and Adaptive Learning #Parallel Computing and Optimization Techniques #Physics Education (physics.ed-ph) #cs.AI #physics.ed-ph
paper · pdf · doi:10.48550/arxiv.2508.14755
openalex publication_date 2025/08/20 · arxiv published 2025/08/20 · arxiv updated 2025/10/15 · openalex created_date 2025/10/16 · openalex updated_date 2026/07/28
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.