Artificial intelligence in education: effects on motor learning, motivation, and student engagement from an educational psychology perspective

Vollständiger Abstract

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The rapid expansion of artificial intelligence (AI) in education has renewed debates about its pedagogical value, particularly in relation to learner-centered approaches and motivational processes. While a growing body of research has examined AI-supported learning in cognitively oriented subjects, empirical evidence remains scarce in practice-based disciplines such as physical education (PE), especially within secondary school contexts in developing countries. This study investigated the effects of integrating AI-supported instructional tools into PE lessons on students' motor learning, intrinsic motivation, and engagement in Tunisian secondary schools. A quasi-experimental pre-test/post-test design involving two intact classes was employed because individual random assignment was not feasible in the school setting ( N = 56; age range 15–17 years, M = 16.4, SD = 0.9), including an experimental group receiving AI-supported instruction and a control group following traditional teaching methods over an 8-week intervention period. Motor learning was assessed using the TGMD-3, intrinsic motivation through the Intrinsic Motivation Inventory, and engagement via a multidimensional student engagement scale. Results revealed statistically significant and educationally meaningful improvements in motor skill acquisition, intrinsic motivation, and engagement among students exposed to AI-supported instruction compared with their peers in the control group. Effect sizes indicated strong practical relevance, suggesting that AI-supported visual feedback based on predefined movement indicators was interpreted and contextualized by the teacher to support motor skill acquisition. These findings suggest that AI-supported instruction may enhance motor learning, intrinsic motivation, and student engagement when integrated into teacher-mediated physical education. Given the quasi-experimental design involving intact classes, these findings should be interpreted as evidence of association rather than definitive causal effects. These findings contribute context-sensitive empirical evidence to the field of educational psychology by highlighting the potential of human-centered AI integration in secondary school physical education within a developing country context. The study underscores the importance of pedagogical mediation, learner autonomy, and ethical awareness in the design and implementation of AI-supported learning environments.