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

Department of Physics, University of Central Florida, 4111 Libra Drive, Orlando, Florida, USA 32816 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. I. INTRODUCTION There has been significant progress in developing Automated Question Generation (AQG) and Automated Item Generation (AIG) technologies in education over the past decade. These technologies aim to reduce the time and cost of item creation while increasing t he availability of questions for both assessment and practice [1] . Early AQG/AIG approaches primarily relied on hard-coded, template-based methods, which were often time - consuming to develop and required domain-specific programming [2] . More recent research has shifted toward leveraging large language models (LLMs).