ORIGINAL RESEARCH article
Front. Psychol.
Sec. Educational Psychology
This article is part of the Research TopicMultidimensional Responses to AI-Driven Transformation in Educational Contexts: Theoretical Frameworks, Tool Development, and Practical ExplorationView all 7 articles
Does English Proficiency Matter? Testing Its Moderating Role in the TAM for AI-Enhanced MOOC Adoption in Vocational Education
Provisionally accepted
- 1The Open University of Sichuan, Chengdu, China
- 2Shanghai Ocean University, Shanghai, China
- 3Sichuan Vocational College of Health and Rehabilitation, Zigong, China
- 4Sichuan Winshare Vocational college, Chengdu, China
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This study investigates whether English proficiency moderates core Technology Acceptance Model (TAM) pathways in the context of AI-enhanced English MOOCs for vocational students. Drawing on an extended TAM that links Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Behavioral Intention (BI), and Perceived Learning Outcomes (PLO), we surveyed 516 learners from a provincial AI-powered MOOC. Confirmatory factor analysis confirmed strong measurement properties (all factor loadings > 0.74, AVE > 0.57, CR > 0.80). Structural analysis revealed robust direct effects: PEOU → PU (β = 0.756), PU →BI (β = 0.696), and BI →PLO (β = 0.814). Hierarchical regression showed no significant moderating by English proficiency on any TAM path, though a small positive direct effect on BI was observed (β = 0.064, p = .042). Results suggest that well-designed AI personalization can mitigate language-related barriers, allowing core TAM mechanisms to operate consistently across proficiency levels. The findings highlight the potential of adaptive AI tools to foster equitable engagement in vocational language learning. Future research should employ multi-item or objective proficiency measures and incorporate actual usage data to further validate these insights.
Keywords: AI-enhanced MOOCs, English proficiency, moderation analysis, Technology acceptance model, Vocational Education
Received: 20 Dec 2025; Accepted: 29 Jan 2026.
Copyright: © 2026 Hou, Liu, Shen, Zhang, Liu and Wu. 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: Xianhe Liu
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