L. Xiao, G. Chen, Y. Zhang et al. (2026) — arXiv preprint. Venue: arXiv:2607.08698.
Uses epistemic network analysis of multimodal YouTube metadata (transcripts, titles, thumbnails, comments) to show how different creator groups frame ChatGPT use in education, revealing divergent narratives around learning support versus academic-integrity risk. The work connects to broader debates about how generative-ai systems reshape student-experience and the conditions under which AI support scaffolds rather than undermines learning. It has direct implications for pedagogy-ai-mistakes and the risk of over-reliance when assistants absorb too much of the cognitive load. Findings also bear on ai-literacy and self-regulated-learning, and on how institutions should govern student-ai-interaction and academic-integrity. Practitioners in higher-ed and teachers can use the evidence to calibrate when to deploy llm-based help and how to pair it with feedback that preserves learning gains.
Related Pages
- ai-literacy — see this paper's treatment of the topic
- student-experience — see this paper's treatment of the topic
- academic-integrity — see this paper's treatment of the topic
- higher-ed — see this paper's treatment of the topic
- generative-ai — see this paper's treatment of the topic
- student-ai-interaction — see this paper's treatment of the topic