AI-Powered Assessment of Motor Development: Using Platforms Like KineticAI to Analyze Fundamental Movement Skills in Children.

This study evaluates the accuracy, reliability, and applicability of KineticAI, an artificial intelligence (AI)-based platform for assessing fundamental movement skills (FMS) in children aged 7 to 8, comparing its performance to traditional human evaluations using the Test of Gross Motor Development (TGMD-3). Involving 200 participants from urban and suburban schools, KineticAI demonstrated high reliability (Intraclass Correlation Coefficient of 0.94) and strong validity (correlation with TGMD-3 scores of 0.92), with low mean absolute errors across motor tasks such as running, jumping, hopping, and balancing. The AI system classified motor competence into proficient, developing, and emerging categories, revealing disparities by gender and school location, with urban boys scoring highest and suburban girls lowest, highlighting inequities in access to physical activity resources. The findings suggest that KineticAI offers a scalable, objective, and efficient alternative to conventional motor skill assessments suitable for educational, clinical, and sports settings, while noting the need for further refinement in evaluating complex movements and enhancing cultural adaptability.