Scaling Equitable Reflection Assessment in Education via Large Language Models and Role-Based Feedback Agents

This paper presents a multi-agent LLM system with five role-based agents (Evaluator, Equity Monitor, Metacognitive Coach, Aggregator, and Reflexion Reviewer) that automatically scores learner reflections using rubrics and generates formative feedback comments while checking for bias and promoting metacognition. The system was evaluated in a 12-session AI literacy program with adult learners, achieving expert-level agreement in scoring and producing feedback rated as helpful and empathetic.

Formative feedback is widely recognized as one of the most effective drivers of student learning, yet it remains difficult to implement equitably at scale. In large or low-resource courses, instructors often lack the time, staffing, and bandwidth required to review and respond to every student reflection, creating gaps in support precisely where learners would benefit most. This paper presents a theory-grounded system that uses five coordinated role-based LLM agents (Evaluator, Equity Monitor, M

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