How YouTube Frames ChatGPT Use in Education: An Epistemic Network Analysis with Supporting Multimodal Metadata

2026-07-09

AI Summary

This study investigates how educational YouTube videos construct discourses around ChatGPT and shape viewers’ cognition and dissemination dynamics. Following PRISMA guidelines, the authors analyzed 52 videos by integrating multimodal metadata—including transcripts, titles, thumbnails, and comments—with Epistemic Network Analysis, identifying three distinct discourse frames surrounding ChatGPT. The findings reveal that output-oriented content, while widely disseminated and comparable in influence to skill-oriented content, frequently elicits viewer concerns about superficial learning and cognitive offloading. This work pioneers the integration of multimodal metadata analysis with epistemic network methods to uncover structural tensions in AI-related educational content within informal learning environments, offering a novel perspective on how generative AI is represented and perceived in public education contexts.

Abstract

We examine educational YouTube videos through multimodal metadata, such as transcripts, titles, thumbnails, and viewer comments, to investigate how ChatGPT is framed across creator groups and how those framings relate to audience response and platform reach. Little is known about how large language models are presented to learners in informal, creator-driven public discourse. Following PRISMA, we selected 52 videos for analysis. We identified three structurally distinct discourse groups: (G1) videos that positioned ChatGPT as a conceptual scaffold for thinking, (G2) videos oriented toward retrieval practice and skill-building, and (G3) videos that framed ChatGPT as a tool for output generation. Epistemic Network Analysis revealed statistically significant group differences with large effect sizes. Multimodal metadata consistently reflected these distinctions across transcript discourse, titles, and thumbnails. Viewers of learning-oriented content described ChatGPT as a thinking partner or tutor, whereas viewers of output-oriented content raised concerns about over-reliance, surface-level learning, and cognitive offloading. G3 achieved comparable platform reach to G2, yet with substantially weaker learning-oriented framing. This may suggest that output-oriented content competes for visibility despite lower pedagogical depth. These findings reveal a structural tension in self-directed AI learning: content that prioritizes quick outputs reaches far more learners than content that promotes deep engagement. This gap raises critical questions about whose vision of AI literacy scales and what learners are actually left with.

Problem

• ChatGPT
• educational framing
• YouTube
• AI literacy
• informal learning

Innovation

• Epistemic Network Analysis
• multimodal metadata
• discourse framing
• AI literacy
• informal learning