Cite or Decline: A Strict Course-Grounded Chatbot for STEM Lecture Videos

Abstract: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.

Computation and Language (cs.CL); Computers and Society (cs.CY); Information Retrieval (cs.IR)

arXiv:2609.01846 [cs.CL]

(or arXiv:2609.01846v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.01846 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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