Seventy3: 用NotebookLM将论文生成播客，让大家跟着AI一起进步。

今天的主题是：Using Generative AI and Multi-Agents to Provide Automatic Feedback

Summary

This research paper explores using a multi-agent system called AutoFeedback to improve the quality of automatically generated feedback for student responses in science assessments. AutoFeedback uses two AI agents: one to generate initial feedback and another to validate and refine it, addressing common issues like over-praise and over-inference found in single-agent large language models (LLMs). The study compared AutoFeedback's performance to a single-agent LLM using 240 student responses, finding that AutoFeedback significantly reduced errors and produced more accurate, pedagogically sound feedback. The findings suggest multi-agent systems offer a more reliable approach to automated feedback in education, enhancing personalized learning support. The paper concludes by discussing limitations and future research directions.

本研究探讨了使用名为 AutoFeedback 的多智能体系统来改进科学评估中对学生回答的自动生成反馈的质量。AutoFeedback 由两个 AI 智能体组成：一个负责生成初始反馈，另一个负责验证和改进反馈，从而解决单智能体大型语言模型（LLMs）中常见的过度赞美和过度推断等问题。研究对比了 AutoFeedback 和单智能体 LLM 在240份学生回答上的表现，发现 AutoFeedback 显著减少了错误，生成了更准确且符合教育学要求的反馈。研究结果表明，多智能体系统在自动化反馈中提供了一种更可靠的方法，从而增强了个性化学习支持。论文最后讨论了其局限性以及未来研究方向。

原文链接：https://arxiv.org/abs/2411.07407