Using Generative AI and Multi-Agents to Provide Automatic Feedback

This paper develops and evaluates a multi-agent AI system (AutoFeedback) to automatically generate feedback on student constructed responses in science assessments, specifically addressing issues of over-praise and over-inference found in single-agent LLM feedback. The system uses two agents—one to generate feedback and another to validate and refine it—and demonstrates significant improvements in feedback quality when tested on 240 student responses.

This study investigates the use of generative AI and multi-agent systems to provide automatic feedback in educational contexts, particularly for student constructed responses in science assessments. The research addresses a key gap in the field by exploring how multi-agent systems, called AutoFeedback, can improve the quality of GenAI-generated feedback, overcoming known issues such as over-praise and over-inference that are common in single-agent large language models (LLMs). The study develope

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