ORIGINAL RESEARCH article
Front. Psychol.
Sec. Educational Psychology
This article is part of the Research TopicNew Directions of Research and Measurement in Cognitive Load TheoryView all 8 articles
Enhancing Deep Learning in AI-Enhanced Education: A Dual Mediation Model of Cognitive Load and Learning Motivation Through Interaction Quality
Provisionally accepted
- Ningbo Education College, Ningbo, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
This research develops and validates a dual mediation framework examining the pathways through which interaction quality in artificial intelligence educational systems is positively associated with deep learning outcomes via cognitive load reduction and motivational enhancement. Utilizing covariance-based structural equation modeling (CB-SEM), we analyzed survey data from 570 university teachers engaged with AI-powered learning platforms. Findings demonstrate that high-quality human-AI interaction significantly reduces cognitive burden, which in turn is positively related to learning motivation and shows pathways to deep learning approaches. Bootstrap procedures confirmed robust sequential mediation effects, with this pathway accounting for 53% of the total variance. The model achieved excellent fit indices and explained 31.5% of variance in deep learning outcomes. By synthesizing Cognitive Load Theory with Self-Determination Theory, this study contributes to educational technology scholarship by elucidating the psychological mechanisms linking interface design to learning depth. The empirical evidence provides actionable insights for developing AI educational systems that strategically minimize cognitive demands, foster motivational engagement, and support meaningful learning experiences.
Keywords: Cognitive Load Theory, Covariance-based structural equation modeling, Deep learning strategies, Intelligent educational systems, Interaction Quality, Learning motivation
Received: 16 Dec 2025; Accepted: 12 Feb 2026.
Copyright: © 2026 Dong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Li Dong
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.