发表机构
University of Houston（休斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究部署搭载检索增强型聊天机器人的VideoPoints平台，实现仅从当前STEM课程视频中检索作答并返回引用，提升课程检索准确率，契合学生学习需求。
AI 中文摘要
录制的课程视频通常配备搜索和摘要功能，是标准的学习资源，但学生难以针对课程提出特定问题或对照教师授课内容验证答案。我们报告了VideoPoints平台一学期的部署情况，该平台搭载检索增强型聊天机器人，可从课程授课材料中作答并返回带时间戳的引用内容。该聊天机器人仅从当前课程中检索内容，利用章节摘要指导转录文本排序，并返回可点击的带时间戳引用。学生用它进行快速查询和考试复习。在833条消息中，70.5%包含引用，且均未跨课程边界；当无授课内容匹配时，聊天机器人通常弃权（不执行）而非作答。在用户中，引用是最稳定有用的功能，而练习题生成是最强烈的未满足需求。我们还在EduVidQA的真实测试拆分上评估了该设计，EduVidQA是面向课程视频问答的公共多模态基准。我们的设计使正确课程检索比仅密集检索提升了6.3个百分点。综合来看，结果表明有效部署依赖于课程隔离、支持性引用以及与学生学习实践的契合。
英文摘要
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.
CommentsTo appear in the Proceedings of the 2026 Conference on EMNLP 2026, Industry Track
