Motivational, Affective, and Self-Regulatory Correlates of Perceived Performance During AI-Assisted Perceptual–Motor Tasks

This study examined how psychological factors relate to perceived perceptual-motor performance in AI-assisted learning among Chinese junior college students. A total of 910 students from five provinces completed established questionnaires measuring intrinsic motivation, learning anxiety, peer support, self-regulation, perceived competence, and curiosity while engaging in AI-supported perceptual-motor tasks. Correlation, hierarchical regression, and structural equation modeling analyses revealed that intrinsic motivation, peer support, and self-regulation were positively associated with perceived competence and curiosity, whereas learning anxiety showed negative associations. Self-regulation emerged as the strongest predictor across models. The findings suggest that performance in AI-assisted environments is shaped by the coordinated influence of motivational, affective, social, and regulatory processes rather than technological features alone.