# REFINE: Real-world Exploration of Interactive Feedback and Student Behaviour

arXiv: https://arxiv.org/abs/2603.29142 · HTML: https://mlsanity.com/paper/2603.29142/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 31 Mar 2026.

## Builds on (most cited first, 13 of 13)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge](https://mlsanity.com/paper/2501.18099/) (2025)
- [Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning](https://mlsanity.com/paper/2506.02867/) (2025)
- [Improving the Validity of Automatically Generated Feedback via Reinforcement Learning](https://mlsanity.com/paper/2403.01304/) (2024)
- [Evaluating ChatGPT's Decimal Skills and Feedback Generation in a Digital Learning Game](https://mlsanity.com/paper/2306.16639/) (2023)
- [Help Me Write a Story: Evaluating LLMs' Ability to Generate Writing Feedback](https://mlsanity.com/paper/2507.16007/) (2025)
- [From First Draft to Final Insight: A Multi-Agent Approach for Feedback Generation](https://mlsanity.com/paper/2505.04869/) (2025)
- [Partnering with AI: A Pedagogical Feedback System for LLM Integration into Programming Education](https://mlsanity.com/paper/2507.00406/) (2025)
- [LLM-Generated Feedback Supports Learning If Learners Choose to Use It](https://mlsanity.com/paper/2506.17006/) (2025)
- [SCRIBE: Structured Chain Reasoning for Interactive Behaviour Explanations using Tool Calling](https://mlsanity.com/paper/2510.26322/) (2025)
- [What Students Ask, How a Generative AI Assistant Responds: Exploring Higher Education Students' Dialogues on Learning Analytics Feedback](https://mlsanity.com/paper/2601.04919/) (2026)

## Built on by (most cited first, 1 of 1)

- [PIVOT: Preference-based Intervention Vectors for Pedagogical Tutor Steering](https://mlsanity.com/paper/2608.07509/) (2026)

## Abstract (arXiv)

Formative feedback is central to effective learning, yet providing timely, individualised feedback at scale remains a persistent challenge. While recent work has explored the use of large language models (LLMs) to automate feedback, most existing systems still conceptualise feedback as a static, one-way artifact, offering limited support for interpretation, clarification, or follow-up. In this work, we introduce REFINE, a locally deployable, multi-agent feedback system built on small, open-source LLMs that treats feedback as an interactive process. REFINE combines a pedagogically-grounded feedback generation agent with an LLM-as-a-judge-guided regeneration loop using a human-aligned judge, and a self-reflective tool-calling interactive agent that supports student follow-up questions with context-aware, actionable responses. We evaluate REFINE through controlled experiments and an authentic classroom deployment in an undergraduate computer science course. Automatic evaluations show that judge-guided regeneration significantly improves feedback quality, and that the interactive agent produces efficient, high-quality responses comparable to a state-of-the-art closed-source model. Analysis of real student interactions further reveals distinct engagement patterns and indicates that system-generated feedback systematically steers subsequent student inquiry. Our findings demonstrate the feasibility and effectiveness of multi-agent, tool-augmented feedback systems for scalable, interactive feedback.