We present a scalable dual-agent architecture translating multi-source assessment data – integrating performance with process logs – into formative data insights. Decoupling classification from text generation, embedding expert rubrics, and optimizing latency enables rapid first-draft generation. These insights reveal underlying learning behaviors, facilitating targeted intervention without increasing teachers’ cognitive burden.

This study uses multi-AI agents to accelerate teacher co-design efforts. It innovatively links student profiles obtained from numerical assessment data to AI agents in natural languages. The AI agents simulate human inquiry, enrich feedback and ground it in teachers’ knowledge and practice, showing significant potential for transforming assessment practice and research.