The rapid advancements in artificial intelligence (AI) have transformed physical skill acquisition across various domains, including sports training, rehabilitation, and technical education. This study investigates the impact of cognitive load and feedback mechanisms in AI-based and traditional training methods for physical skill acquisition. It explores how these variables influence learning efficiency, motor skill retention, and learner engagement. Using a mixed-method research design, data were collected from 200 participants through pre- and post-training assessments, cognitive load measurements, and qualitative feedback surveys. Findings indicate that AI-based training reduces cognitive load through adaptive learning and real-time feedback while improving skill retention. However, traditional training methods provide enhanced personalized instruction and social motivation, which positively impact engagement and motivation. The study highlights the strengths and limitations of both approaches, offering valuable insights for optimizing physical training programs.

Keywords: Cognitive Load, Feedback Mechanisms, AI-Based Training, Traditional Training, Physical Skill Acquisition, Learning Efficiency.

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