Article RETRACTED due to malpractice AI-Powered Assessment in Physical Education: Rethinking Student Engagement through Social Sciences Perspectives

Resumo

This paper has explored the impact of AI assessment on Physical Education (PE) through the social science perspective to determine the technology’s impact on student engagement, equity, and inclusion. The primary objective was to understand the value of algorithmic tools, wearable technology, and personalized feedback systems in tracking participation rates using assessments that extend beyond traditional performance-based evaluations. Many of these aspects were addressed through a secondary research methodology, which encapsulated a range of peer-reviewed literature and empirical research spanning computer science, psychology, and education. Such an approach facilitated the incorporation of credible evidence, which included engagement detection accuracies of 81–92% and self-efficacy increases of more than 22%, thereby enabling the formulation of a coherent analytical framework. The results indicated that AI motion analytics accomplished more than performance assessments by uncovering hidden patterns of participation, and that adaptive feedback systems enhanced motivation and skill acquisition through machine learning and virtual reality (VR) corrections. Wearable technology captured more simplified measures of engagement, collaboration, and well-being, illustrating the cross-dimensional aspects of participation in PE. Other, more advanced algorithmic assessments further reframed equity, although there were still trust and privacy issues, which amplified the need for explainable AI and automated bias detection and removal. This study demonstrated that AI-powered assessment captures more dimensions of engagement and applies more equity and transparency in evaluation practices, although success in these areas hinges on the delicate balance between technological bias and student trust.