基于课程内容的严格问答机器人，精准引用讲义时间戳。
Cite or Decline: A Strict Course-Grounded Chatbot for STEM Lecture Videos
- 仅从当前课程材料检索，用章节摘要优化文本排序。
- 833条提问中70.5%带时间戳引用，无跨课程回答。
- 适合备考复习，尤其需要精准证据的学生。
录制的讲座视频通常配有搜索和摘要功能，是标准学习资源。但学生难以针对课程内容提问或验证答案是否来自授课内容。我们报告了在一门课程中持续一学期部署VideoPoints平台的经验，该平台配备一个基于检索增强的聊天机器人，能从课程讲义材料中回答问题并返回带时间戳的引用。机器人仅从当前课程中检索，利用章节摘要引导转录文本排序，并返回可点击的时间戳引用。学生用于快速查询和考试复习。在833条消息中，70.5%包含引用，无任何跨课程回答；当讲座内容无匹配时，机器人通常拒绝回答而非猜测。用户反馈显示，引用功能最实用，而生成练习题是最强烈未满足需求。我们在EduVidQA公开多模态基准的真实测试集上评估了该设计，相比仅使用密集检索的方法，正确讲义检索率提升了6.3个百分点。结果表明，有效部署依赖于课程隔离、支持引用和与学生学习习惯对齐。
原文摘要 · Abstract (English)
Recorded lecture videos, often enhanced with search and summarization features, are a standard study resource. However, students cannot easily ask course specific questions or verify answers against an instructor's lecture. We report a semester-long deployment of VideoPoints platform with a retrieval-augmented chatbot that answers from course lecture materials and returns timestamped citations. The chatbot retrieves only from the active course, uses chapter summaries to guide transcript ranking, and returns clickable timestamped citations. Students used it for quick lookups and exam review. Across 833 messages, 70.5% included citations, none crossed a course boundary, and when no lecture evidence matched, the chatbot usually declined rather than answering. Among the users, citations were the most consistently useful feature, while practice-question generation was the strongest unmet request. We also evaluated the design on the real-world test split of EduVidQA, a public multimodal benchmark for lecture-video question answering. Our design improved correct-lecture retrieval by 6.3 percentage points over dense-only retrieval. Together, the results show that effective deployment depends on course isolation, supported citations, and alignment with students' study practices.
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