Flow, Psychological Needs, and Behavioral Intention in AI ‐Assisted Learning: Evidence From Music Education

ABSTRACT This study investigates how flow experience influences students' intention to continue using AI‐assisted learning tools, with a focus on the mediating role of three basic psychological needs (autonomy, competence, and relatedness) as conceptualized by Self‐Determination Theory (SDT). Drawing on data from 941 university‐level music students who had experience using AI‐supported music learning platforms, the study employed structural equation modeling (SEM) to test a hypothesized motivation pathway. Results revealed that flow did not directly predict behavioral intention, but its effects were transmitted indirectly through autonomy and relatedness. Competence, however, did not significantly mediate the relationship. These findings suggest that learners' engagement with AI systems is sustained not merely by momentary immersion but by the satisfaction of deeper psychological needs. The study extends motivational theories in the field of AI‐supported education and offers practical guidance for designing psychologically supportive intelligent learning systems.