Mitigating AI Risks in Computing Education via LLM-Driven Lecture Video Curation
2026-08-17 • Computers and Society
Computers and Society
AI summaryⓘ
The authors tested large language models (LLMs) to find helpful parts in recorded programming lectures that answer student questions. Instead of creating new text, the LLMs only pointed to existing teacher-approved video clips to avoid mistakes or confusion. They found that two proprietary LLMs matched human experts well in choosing relevant video segments. When used by a large group of students, this system was popular for reviewing basic programming ideas. This suggests using LLMs to retrieve set teaching content can safely support beginner programmers.
large language modelsintroductory programmingvideo retrievalgenerative hallucinationpedagogical risksknowledge retrievalproprietary modelsopen-weight modelsuser engagementnovice computing education
Authors
Owen Tang, Alexandra Vassar, Jake Renzella
Abstract
This study evaluates the effectiveness of utilising large language models (LLMs) to retrieve targeted segments from delivered video recordings to answer student questions in introductory programming environments. By restricting AI to identifying existing, educator-verified media rather than generating open-ended text, this approach aims to mitigate common pedagogical risks such as generative hallucinations and cognitive bypassing. We benchmarked three distinct models, two proprietary (Gemini 3.1 Pro and GPT 5.4 Pro) and one open-weight (Qwen3.5 397B), against a human lecturer's manual video selections. An automated judging framework subsequently assessed the outputs for relevance, sufficiency, redundancy, and the presence of extraneous material. While the AI-retrieved timestamps rarely shared exact overlaps with the human baseline, the proprietary models achieved near-parity with the expert in delivering sufficient and highly relevant answers. Furthermore, a pilot deployment of this retrieval system in a large C programming cohort (n~=900) demonstrated strong user engagement, with students primarily utilising the tool to review foundational concepts. By leveraging AI to retrieve established lecture material, this approach shows potential for a reliable, high-fidelity pathway for safely integrating LLMs into novice computing courses.