Artificial Intelligence in University Physical Education Pedagogy: A Curriculum Framework for Vietnam

Objectives. This review examines how artificial intelligence (AI) can support university physical education (PE) pedagogy and curriculum development while preserving motor learning, meaningful movement time, teacher judgment, safety, and equity, and proposes a context-sensitive framework for Vietnamese higher education. Materials and Methods. A structured narrative review and conceptual synthesis were conducted using peer-reviewed research and authoritative policy and guidance sources identified through PubMed and targeted scholarly searches of journal and publisher platforms. The search was updated on 23 September 2026. Priority was given to systematic and scoping reviews, recent empirical studies, and PE-focused sources addressing motor learning, movement analysis, feedback, assessment, wearables, teacher competence, ethics, and governance. Thirty-four sources were retained: 29 peer-reviewed journal articles and reviews and five policy or guidance documents. Coding and theory-driven consolidation were used to map recurring findings to framework elements, with greater interpretive weight given to reviews and authoritative guidance. Results. Five application domains were identified: curriculum needs analysis and learning design, personalized motor-learning support, movement recognition and feedback, multimodal movement and participation monitoring, and teacher and program support. Educational value depended on context-specific validity, teacher competence, data governance, equitable access, and preservation of active learning time. The proposed AI-PE framework links six components—learning outcomes, learner data, learning design, AI support, multi-source assessment, and curriculum improvement—under three safeguards: human decision-making, data safety and ethics, and equitable access. Conclusions. AI should function as a pedagogical and decision-support layer rather than an autonomous instructor or assessor. The framework places embodied learning, motor competence, physical activity, and teacher responsibility at the center and provides a basis for institutional pilots and empirical validation in Vietnamese universities.

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Publication Details

Journal
Physical Education Theory and Methodology
Published
2026-11-05
DOI
https://doi.org/10.17309/tmfv.2026.6.03
Primary Topic
Educational Technology and Pedagogy
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial Intelligence in University Physical Education Pedagogy: A Curriculum Framework for Vietnam

Nguyễn Đức Thành, Viet Quoc Mai Nguyen, Truong Ngoc Duong
Physical Education Theory and Methodology
Educational Technology and Pedagogy
article

Artificial Intelligence in University Physical Education Pedagogy: A Curriculum Framework for Vietnam

Nguyễn Đức Thành, Viet Quoc Mai Nguyen, Truong Ngoc Duong
article en

Abstract

Objectives. This review examines how artificial intelligence (AI) can support university physical education (PE) pedagogy and curriculum development while preserving motor learning, meaningful movement time, teacher judgment, safety, and equity, and proposes a context-sensitive framework for Vietnamese higher education. Materials and Methods. A structured narrative review and conceptual synthesis were conducted using peer-reviewed research and authoritative policy and guidance sources identified through PubMed and targeted scholarly searches of journal and publisher platforms. The search was updated on 23 September 2026. Priority was given to systematic and scoping reviews, recent empirical studies, and PE-focused sources addressing motor learning, movement analysis, feedback, assessment, wearables, teacher competence, ethics, and governance. Thirty-four sources were retained: 29 peer-reviewed journal articles and reviews and five policy or guidance documents. Coding and theory-driven consolidation were used to map recurring findings to framework elements, with greater interpretive weight given to reviews and authoritative guidance. Results. Five application domains were identified: curriculum needs analysis and learning design, personalized motor-learning support, movement recognition and feedback, multimodal movement and participation monitoring, and teacher and program support. Educational value depended on context-specific validity, teacher competence, data governance, equitable access, and preservation of active learning time. The proposed AI-PE framework links six components—learning outcomes, learner data, learning design, AI support, multi-source assessment, and curriculum improvement—under three safeguards: human decision-making, data safety and ethics, and equitable access. Conclusions. AI should function as a pedagogical and decision-support layer rather than an autonomous instructor or assessor. The framework places embodied learning, motor competence, physical activity, and teacher responsibility at the center and provides a basis for institutional pilots and empirical validation in Vietnamese universities.

Physical Education Theory and MethodologyVol. 26(6)
Openalex Percentile: Top 9%
Educational Technology and Pedagogy
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