用多智能体辩论机制让AI能互动改作文，学生可质疑和追问评分理由。
"My Grade is Wrong!": A Contestable AI Framework for Interactive Feedback in Evaluating Student Essays
- 引入多智能体系统与计算论证，实现可争议的反馈交互
- 在500篇议论文上验证，显著提升AI反馈的推理与互动能力
- 适合需要高质量互动评阅的教育场景，如写作教学与自动评估
交互式反馈（师生双向互动）比单向反馈更有效，但耗时过长难以普及。尽管大语言模型（LLMs）有自动化潜力，但在交互场景中仍缺乏推理与互动能力。本文提出CAELF——一种基于可争议人工智能的多智能体反馈框架，通过多个助教智能体（TA Agents）评估作文，再由教师智能体（Teacher Agent）进行形式化推理整合评价并生成反馈与成绩。学生可进一步质疑、澄清反馈内容以深化理解。在500篇批判性思维作文上的案例研究与用户实验表明，CAELF显著提升了LLM在交互反馈中的推理与互动能力，为突破教育实践中交互反馈的时间与资源瓶颈提供了可行方案。
原文摘要 · Abstract (English)
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.
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