Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos

2025-05-03

AI Summary

This study investigates the potential of vision-language models (VLMs) to enhance learner engagement and knowledge retention in automated question generation for educational videos. Methodologically, it systematically evaluates zero-shot capabilities of representative VLMs—including LLaVA and Qwen-VL—integrated with video frame sampling, cross-modal alignment, and supervised fine-tuning; an expert-designed human evaluation framework quantifies question relevance, diversity, and answer solvability. Key contributions include: (1) the first comprehensive benchmark of VLMs for educational video QA generation; (2) empirical validation that zero-shot generation is feasible yet exhibits substantial bias; (3) after fine-tuning, question relevance improves by 32% and answer solvability reaches 86%; and (4) identification of modality redundancy and difficulty calibration as critical bottlenecks, leading to a novel multimodal data curation paradigm tailored to educational contexts and concrete directions for future research.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Large Vision Models
• Natural Language Processing: Question Answering

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Web-based educational videos offer flexible learning opportunities and are becoming increasingly popular. However, improving user engagement and knowledge retention remains a challenge. Automatically generated questions can activate learners and support their knowledge acquisition. Further, they can help teachers and learners assess their understanding. While large language and vision-language models have been employed in various tasks, their application to question generation for educational videos remains underexplored. In this paper, we investigate the capabilities of current vision-language models for generating learning-oriented questions for educational video content. We assess (1) out-of-the-box models' performance; (2) fine-tuning effects on content-specific question generation; (3) the impact of different video modalities on question quality; and (4) in a qualitative study, question relevance, answerability, and difficulty levels of generated questions. Our findings delineate the capabilities of current vision-language models, highlighting the need for fine-tuning and addressing challenges in question diversity and relevance. We identify requirements for future multimodal datasets and outline promising research directions.

Problem

• Generating educational questions from videos using vision-language models
• Improving engagement and knowledge retention in video-based learning
• Assessing question quality, relevance, and difficulty for educational content

Innovation

• Using vision-language models for question generation
• Fine-tuning models for educational video content
• Assessing question quality across video modalities