| ADVANCING PHYSICAL EDUCATION THROUGH ARTIFICIAL INTELLIGENCE: EMERGING APPLICATIONS, PEDAGOGICAL IMPLICATIONS, AND RESEARCH GAPS |
| Paper ID : 1181-SPORTCONGRESS |
| Authors |
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Sara Bagheri1, Sepideh Poursadeghi *2 1Assistant Professor, Department of Physical Education, Farhangian University, P.O. Box 14665-889, Tehran, Iran 2Tehran District 2 Education Department |
| Abstract |
| Introduction: Physical education (PE) often faces challenges such as inadequate motor performance assessment, time constraints in providing personalized feedback, and limited digital resources. The integration of artificial intelligence (AI) holds significant potential to address these challenges by enhancing instructional quality, assessing learning performance, creating personalized learning experiences, and improving teacher-student interactions. Methods: The study employed a systematic literature review of peer-reviewed articles published from 2015 to 2025 to examine artificial intelligence (AI) applications in physical education (PE). Databases searched included SCIE, SSCI, PubMed, Medline, Scopus, and SportDiscus using keywords such as artificial intelligence, machine learning, deep learning, and physical education. The initial search identified 100 studies, which were screened through a two-stage process—title/abstract review and full-text assessment—based on predefined inclusion and exclusion criteria. Fifty eligible studies were analyzed. Data were extracted on study themes, methodologies, and outcomes, and synthesized qualitatively using thematic analysis to identify emerging trends, pedagogical implications, and research gaps. Iterative coding ensured comprehensive categorization and rigorous synthesis of AI-related advancements in PE. Results: Findings indicate that AI research in PE is in its early stages, with limited depth and scope, primarily focusing on motion recognition (e.g., deep learning-based algorithms), learning analytics, and performance evaluation. Conclusion: Significant research gaps exist, particularly in exploring pedagogical strategies, cognitive processes, and behavioral aspects of motor learning, which are critical for holistic student development (encompassing motor skills, motivation, and self-efficacy) but underrepresented. The review also highlights challenges in PE teacher training amid AI-driven educational transformations, such as gaps in digital literacy and adaptive pedagogy, and proposes essential competencies for future educators. This review provides evidence-based recommendations for policymakers and educators to address these gaps through targeted professional development frameworks focused on digital literacy, AI-integrated curriculum design, and adaptive teaching skills, advancing AI integration in physical education and sports. |
| Keywords |
| Artificial Intelligence, Deep Learning, Machine Learning, Physical Education, Digital Literacy |
| Status: Abstract Accepted (Poster Presentation) |
