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

Equitable formative feedback remains out of reach for large or low-resource courses because instructors cannot read every learner reflection. We present a theory-grounded pipeline of five role-based LLM agents—Evaluator, Equity Monitor, Metacognitive Coach, Aggregator, and Reflexion Reviewer—that jointly produce calibrated rubric scores and $\le 120$-word, bias-aware comments. On $84$ reflections from a $12$-session AI-literacy program, the pipeline matches expert raters ($\mathrm{MAE}=0.47$, $\mathrm{QWK}=0.46$, human-AI $\mathrm{ICC}=0.41$) while bounding the worst-ability error gap to $\Delta{\mathrm{MAE}}=0.50$. Automated scoring finishes in $7.7\text{s}$ per reflection—an $11\times$ speed-up over the human mean of $1.4\text{min}$—and complete feedback in $33\text{s}$. A full agentic run costs just $0.0016$ per reflection. Three trained graders rated the AI feedback highly useful (overall $Q(g)=3.97/5$) with top marks for empathy ($4.22/5$). Contributions. (i) A self-consistent scoring scheme with equity safeguards; (ii) a role-based agent ensemble for dialogic, bias-aware feedback; and (iii) the first open dataset, prompts, and codebase for equitable reflection assessment. Together, these advances demonstrate a practical path toward large-scale, fair feedback in real classrooms.