Deep Learning as Artificial Intelligence Technology in Physical Education: A Literature Review Based on Bloom's Taxonomy - Rubby Syihabuddin; Indra Safari; Dewi Susilawati (Universitas Negeri Semarang, 2026)
Technological advancements necessitate a shift in education, especially in physical education, which sometimes remains confined to gross motor skills without enhancing cognitive processes. This study seeks to analyze and assess the role of Deep Learning (DL) technology within Artificial Intelligence (AI) in enhancing the quality of physical education instruction based on Bloom's Taxonomy framework. The method used is a Systematic Literature Review (SLR) following the PRISMA standards. Data were collected through the Google Scholar, Scopus, and Elsevier databases using the Publish or Perish tool, which produced 10 selected articles to be synthesized after undergoing a rigorous selection process. The study's results indicate that the incorporation of Deep Learning can elevate learning from simple imitation of movement to profound analytical comprehension. The use of algorithms such as ResNet-101 and 3D pose estimation enables precise biomechanical analysis and objective performance evaluation that supports higher-order thinking skills (HOTS). In conclusion, this technology acts as a catalyst in achieving physical literacy and student learning independence. The integration of AI with student-centered pedagogical strategies is the key to the future of holistic physical education.