<![CDATA[The purpose of the study. This systematic review examines how artificial intelligence (AI) is applied to personalized learning, assessment, and performance analytics in physical education (PE) across K–12 and higher-education settings, with the aim of synthesizing empirical evidence, identifying patterns of implementation, and proposing evidence-based directions for future research and practice. Materials and methods. A systematic review was conducted following the PRISMA 2020 guidelines. Seven electronic databases (Web of Science, Scopus, EBSCOhost, PubMed, ACM Digital Library, Taylor & Francis Online, and Wiley Online Library) were searched from January 2014 to December 2025, using a reproducible Boolean search string centered on "artificial intelligence," "machine learning," "physical education," and related terms. Inclusion criteria covered empirical studies (experimental, quasi-experimental, case studies, and mixed-methods) that reported AI applications in PE focusing on personalized instruction, automated assessment, or performance analytics. Two reviewers independently screened titles, abstracts, and full texts; extracted data; and appraised quality using the Mixed-Methods Appraisal Tool (MMAT). Results. A total of 87 studies (from an initial pool of 2,945 records) met all inclusion criteria and were synthesized narratively. AI-based systems most commonly supported: (a) personalized learning through adaptive exercise plans and intelligent tutoring systems; (b) assessment via motion analysis and automated feedback mechanisms; and (c) performance analytics through wearable-driven dashboards and learning-analytics platforms. Overall, AI-enhanced PE was associated with improved student engagement, more accurate and objective assessment, and tailored motor-skill development. However, persistent concerns included data privacy vulnerabilities, algorithmic bias, and insufficient frameworks for teacher–AI collaboration. Conclusions. AI holds substantial potential to transform PE into a more personalized, data-informed, and student-centered discipline, particularly in large-class and inclusive settings. Future research should prioritize longitudinal designs, standardized outcome measures, and robust ethical frameworks to ensure equitable and sustainable integration of AI in PE contexts.]]>
