AI Agents Improve Math Visual Aid Generation for K-12 Education

Current AI tools, including large language models, often struggle to produce accurate and pedagogically sound mathematical diagrams, particularly for K-12 education. This paper proposes an agentic workflow designed to overcome this challenge. The system allows LLM agents to generate visual aids, then evaluate their quality against specific criteria, and subsequently use this feedback to iteratively refine their outputs. The research investigates whether LLMs can effectively generate quality assurance questions for visual aids and if Vision Language Models can utilize these questions to iteratively enhance diagrams. Initial evaluations demonstrate the potential of this approach, though areas such as spatial reasoning and comprehensive coverage of diagram features in quality assurance questions require further development. This method offers a promising direction for creating more reliable and educationally valuable AI-generated mathematical visuals.