# Using Generative AI and Multi-Agents to Provide Automatic Feedback

arXiv: https://arxiv.org/abs/2411.07407 · HTML: https://mlsanity.com/paper/2411.07407/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 88 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Placed in

- LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/)

## Builds on (most cited first, 9 of 9)

- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks](https://mlsanity.com/paper/2305.18365/) (2023)
- [Simulating Classroom Education with LLM-Empowered Agents](https://mlsanity.com/paper/2406.19226/) (2024)
- [MEDCO: Medical Education Copilots Based on A Multi-Agent Framework](https://mlsanity.com/paper/2408.12496/) (2024)
- [Generating Continuations in Multilingual Idiomatic Contexts](https://mlsanity.com/paper/2310.20195/) (2023)
- [Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPU](https://mlsanity.com/paper/2311.00368/) (2023)
- [Batch effect correction with sample remeasurement in highly confounded case-control studies](https://mlsanity.com/paper/2311.03289/) (2023)

## Built on by (most cited first, 15 of 15)

- [LLM Agents for Education: Advances and Applications](https://mlsanity.com/paper/2503.11733/) (2025)
- [Evolution of AI in Education: Agentic Workflows](https://mlsanity.com/paper/2504.20082/) (2025)
- [A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents](https://mlsanity.com/paper/2508.01503/) (2025)
- [Towards Adaptive Feedback with AI: Comparing the Feedback Quality of LLMs and Teachers on Experimentation Protocols](https://mlsanity.com/paper/2502.12842/) (2025)
- [Multilingual Performance of a Multimodal Artificial Intelligence System on Multisubject Physics Concept Inventories](https://mlsanity.com/paper/2501.06143/) (2025)
- [Artificial Intelligence Bias on English Language Learners in Automatic Scoring](https://mlsanity.com/paper/2505.10643/) (2025)
- [SketchMind: A Multi-Agent Cognitive Framework for Assessing Student-Drawn Scientific Sketches](https://mlsanity.com/paper/2507.22904/) (2025)
- [Teaching According to Students' Aptitude: Personalized Mathematics Tutoring via Persona-, Memory-, and Forgetting-Aware LLMs](https://mlsanity.com/paper/2511.15163/) (2025)
- [Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most](https://mlsanity.com/paper/2605.16207/) (2026)
- [UniEDU: A Unified Language and Vision Assistant for Education Applications](https://mlsanity.com/paper/2503.20701/) (2025)
- [Developing a Multi-Agent System to Generate Next Generation Science Assessments with Evidence-Centered Design](https://mlsanity.com/paper/2602.18451/) (2026)
- [Agents in the Large: Perception-Centered Architecture for Persistent Agents](https://mlsanity.com/paper/2608.30478/) (2026)
- [Rethinking AI Evaluation in Education: The TEACH-AI Framework and Benchmark for Generative AI Assistants](https://mlsanity.com/paper/2512.04107/) (2025)
- [The Landscape of AI in Science Education: What is Changing and How to Respond](https://mlsanity.com/paper/2602.18469/) (2026)
- [More than a Judge: An Empirical Study of Agent-Human Interaction in Crowdsourced Testing Assessment](https://mlsanity.com/paper/2606.06301/) (2026)

## Abstract (arXiv)

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 developed a multi-agent system consisting of two AI agents: one for generating feedback and another for validating and refining it. The system was tested on a dataset of 240 student responses, and its performance was compared to that of a single-agent LLM. Results showed that AutoFeedback significantly reduced the occurrence of over-praise and over-inference errors, providing more accurate and pedagogically sound feedback. The findings suggest that multi-agent systems can offer a more reliable solution for generating automated feedback in educational settings, highlighting their potential for scalable and personalized learning support. These results have important implications for educators and researchers seeking to leverage AI in formative assessments, offering a pathway to more effective feedback mechanisms that enhance student learning outcomes.