# "My Grade is Wrong!": A Contestable AI Framework for Interactive Feedback in Evaluating Student Essays

arXiv: https://arxiv.org/abs/2409.07453 · HTML: https://mlsanity.com/paper/2409.07453/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://mlsanity.com/paper/2308.07201/) (2023)
- [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023)
- [Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate](https://mlsanity.com/paper/2305.13160/) (2023)
- [Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation](https://mlsanity.com/paper/2404.15845/) (2024)
- [Argumentative Large Language Models for Explainable and Contestable Claim Verification](https://mlsanity.com/paper/2405.02079/) (2024)
- [A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science](https://mlsanity.com/paper/2403.14565/) (2024)
- [Interactive Explanations by Conflict Resolution via Argumentative Exchanges](https://mlsanity.com/paper/2303.15022/) (2023)
- [Assessing the Quality of Multiple-Choice Questions Using GPT-4 and Rule-Based Methods](https://mlsanity.com/paper/2307.08161/) (2023)
- [(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court](https://mlsanity.com/paper/2403.13004/) (2024)

## Built on by (most cited first, 5 of 5)

- [LLM Agents for Education: Advances and Applications](https://mlsanity.com/paper/2503.11733/) (2025)
- [SketchMind: A Multi-Agent Cognitive Framework for Assessing Student-Drawn Scientific Sketches](https://mlsanity.com/paper/2507.22904/) (2025)
- [RubiSCoT: A Framework for AI-Supported Academic Assessment](https://mlsanity.com/paper/2510.17309/) (2025)
- [Rethinking AI Evaluation in Education: The TEACH-AI Framework and Benchmark for Generative AI Assistants](https://mlsanity.com/paper/2512.04107/) (2025)
- [Specialists or Generalists? Multi-Agent and Single-Agent LLMs for Essay Grading](https://mlsanity.com/paper/2601.22386/) (2026)

## Abstract (arXiv)

Interactive feedback, where feedback flows in both directions between teacher and student, is more effective than traditional one-way feedback. However, it is often too time-consuming for widespread use in educational practice. While Large Language Models (LLMs) have potential for automating feedback, they struggle with reasoning and interaction in an interactive setting. This paper introduces CAELF, a Contestable AI Empowered LLM Framework for automating interactive feedback. CAELF allows students to query, challenge, and clarify their feedback by integrating a multi-agent system with computational argumentation. Essays are first assessed by multiple Teaching-Assistant Agents (TA Agents), and then a Teacher Agent aggregates the evaluations through formal reasoning to generate feedback and grades. Students can further engage with the feedback to refine their understanding. A case study on 500 critical thinking essays with user studies demonstrates that CAELF significantly improves interactive feedback, enhancing the reasoning and interaction capabilities of LLMs. This approach offers a promising solution to overcoming the time and resource barriers that have limited the adoption of interactive feedback in educational settings.