Explore/agent app/Reliable isomorphic physics problem generation with large language models
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Xian Wu, Lindim Ismaili/Reliable isomorphic physics problem generation with large language modelsUnknown

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.

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55 / 100
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42C
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58C
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2026-07-262026-10-15
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✓ Best for

  • Research and experimentation
  • Prototype development
  • Learning agentic patterns

◎ Strengths

  • Active community
  • Open source
  • Well-documented API

✕ Not ideal for

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  • Teams without AI/ML expertise

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AgentHub Score

55
Score 55/100
Below average

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