Mitigating AI Risks in Computing Education via LLM-Driven Lecture Video Curation

A research study evaluates the application of Large Language Models (LLMs) for curating specific segments from educational video recordings to address student inquiries in introductory programming. This method is designed to mitigate AI-related pedagogical risks, such as generative hallucinations and cognitive bypassing, by restricting the AI's role to identifying existing, validated content rather than generating new text. The study benchmarked three LLMs against human curation, assessing outputs for relevance, sufficiency, and redundancy.

Why it matters

This development offers a potential strategy for integrating AI into educational or training contexts while managing inherent risks. By shifting AI's function from content generation to content curation from pre-validated sources, institutions can enhance learning support safely and efficiently. This could lead to improved educational outcomes and scalable training solutions without compromising accuracy or pedagogical integrity.

Key insights

• Large Language Models (LLMs) can be leveraged to retrieve targeted segments from pre-recorded educational videos in response to student questions.
• This approach aims to mitigate pedagogical risks associated with generative AI, including hallucinations and cognitive bypassing, by focusing on content curation.
• The methodology involves restricting AI to identifying educator-verified media rather than generating open-ended text responses.
• The study benchmarked two proprietary LLMs (Gemini 3.1 Pro, GPT 5.4 Pro) and one open-weight LLM (Qwen3.5 397B) against human-selected video segments.
• Evaluation criteria for LLM outputs included relevance, sufficiency, redundancy, and the presence of extraneous material.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.16131