用大模型聊天机器人收集课堂反馈，让教学改进更及时有效。
Listening with Language Models: Using LLMs to Collect and Interpret Classroom Feedback
- 用大模型构建对话式反馈系统，引导学生深度反思教学
- 相比传统问卷，反馈内容更丰富、上下文更相关、参与度更高
- 适合希望实时优化教学的高校教师和重视表达的学生
传统期末问卷常无法为教师提供及时、详尽且可操作的教学反馈。本文探讨如何利用大语言模型（LLM）驱动的聊天机器人，通过与学生开展反思性对话，重新构想课堂反馈流程。我们设计并部署了一个三部分系统——PromptDesigner、FeedbackCollector 和 FeedbackAnalyzer，对加州大学圣克鲁斯分校两门研究生课程进行了试点研究。结果表明，基于LLM的反馈系统在信息丰富度、上下文相关性和学生参与度方面均优于传统问卷。教师认可其可适应性、具体性和支持中期调整的能力；学生则赞赏其对话形式及深入表达的机会。文章最后讨论了使用AI促进高等教育中更具意义和响应性的反馈设计启示。
原文摘要 · Abstract (English)
Traditional end-of-quarter surveys often fail to provide instructors with timely, detailed, and actionable feedback about their teaching. In this paper, we explore how Large Language Model (LLM)-powered chatbots can reimagine the classroom feedback process by engaging students in reflective, conversational dialogues. Through the design and deployment of a three-part system-PromptDesigner, FeedbackCollector, and FeedbackAnalyzer-we conducted a pilot study across two graduate courses at UC Santa Cruz. Our findings suggest that LLM-based feedback systems offer richer insights, greater contextual relevance, and higher engagement compared to standard survey tools. Instructors valued the system's adaptability, specificity, and ability to support mid-course adjustments, while students appreciated the conversational format and opportunity for elaboration. We conclude by discussing the design implications of using AI to facilitate more meaningful and responsive feedback in higher education.
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