用多智能体系统改进AI作文批改，减少夸大和误判。
Using Generative AI and Multi-Agents to Provide Automatic Feedback
- 双智能体协作：一个生成反馈，一个审核修正。
- 在240份学生作答中，过誉和误判率显著降低。
- 适合教育科技开发者和关注精准反馈的教师。
本研究探讨生成式AI与多智能体系统在教育场景中自动反馈的应用，聚焦科学测评中学生自构答案的反馈生成。针对单智能体大语言模型常见的过度赞扬和过度推断问题，提出名为AutoFeedback的多智能体系统，包含一个反馈生成代理和一个验证优化代理。在包含240个学生回答的数据集上测试，结果表明该系统显著降低了过誉与误判错误，提供更准确、更具教学意义的反馈。研究显示多智能体架构可为教育领域自动化反馈提供更可靠方案，对实现规模化、个性化学习支持具有重要意义，为教育者与研究者在形成性评估中运用AI提供了有效路径。
原文摘要 · Abstract (English)
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.
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