Effects of Artificial Intelligence-Assisted Physical Education on Students’ Motivation, Engagement, and Physical Activity Levels: A Quasi-Experimental Study

Authors

  • Muhammad Javed Bajwa Lecturer, Department of Physical Education & Sports Sciences, Government College University, Lahore, Punjab, Pakistan, Email: javed.bajwa@gcu.edu.pk Corresponding author: Muhammad Javed Bajwa Author

DOI:

https://doi.org/10.63163/jpehss.v4i1.1724

Keywords:

Artificial intelligence; physical education; student motivation; student engagement; physical activity; self-determination theory; university students; quasi-experimental study

Abstract

Artificial intelligence (AI) technology is becoming common in educational settings, but its application in university physical education (PE) has not been studied enough. This research project explores the potential impacts of AI-enabled PE on students' motivation, engagement, and physical activity levels compared to traditional PE. Within the framework of Self-Determination Theory, an intervention based on integration of AI-powered learning guidance, feedback, reminder of activities, monitoring, and interactive support is proposed in teacher-directed PE. A quasi-experimental design with pretest and posttest assessments was designed with university students randomized into two conditions of AI-enabled and traditional PE during eight weeks of an intervention program. Motivation, engagement, and physical activity were measured before and after an intervention, and baseline-controlled comparisons of the two groups were established. The illustrative analysis revealed increased motivation, engagement, and physical activity in the AI group after an intervention relative to their baseline values. These trends are congruent with the expected ability of personalized guidance and feedback to support autonomy, competence, and relatedness and stimulate involvement in physical activity. However, the numerical findings presented in the current manuscript are illustrative due to the unavailability of the original individual-level dataset. Consequently, these should not be taken as empirically validated evidence. The study thus offers a theory-based approach towards studying AI-supported PE and underscores the need for teacher-mediated intervention, responsible use of data, and objectivity in assessing physical activity. There is need for future research that uses real student-level data, rigorous experimental design, long-term follow-up, and objective measurement of physical activity.

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Published

2026-03-18