Deliberate practice in the age of artificial intelligence: how AI-assisted practice supports self-regulated learning and expert performance development in conservatory musicians

Introduction Deliberate practice is central to expert performance development, yet the conditions under which practice produces sustained gains remain contested. AI-assisted music practice applications provide adaptive feedback, intelligent scheduling, automated error detection, and performance analytics, potentially supporting deliberate practice. This study examined whether engagement with AI-assisted practice applications is longitudinally associated with expert performance development among conservatory musicians through self-regulated learning (SRL). Methods A three-wave longitudinal study was conducted over a 16-week academic semester with a target sample of 600 conservatory musicians. Of these, 512 completed all three waves (85.3%). Temporal associations among AI-assisted practice engagement, SRL, and instructor-rated musical performance were examined. Results Significant longitudinal associations supported the proposed pathway. Greater engagement with AI-assisted practice applications was associated with subsequent SRL, which was in turn associated with later instructor-rated musical performance. These findings are consistent with a mediating role of SRL in the association between AI-assisted practice engagement and expert performance development. Discussion AI-assisted practice applications may support expert performance development by fostering self-regulatory processes conducive to deliberate practice. However, because the study was observational rather than randomized and involved heterogeneous AI applications rather than a standardized intervention, the findings should be interpreted as longitudinal associations rather than causal effects. AI-assisted practice applications are therefore best conceptualized as pedagogical supports that facilitate conditions for deliberate practice rather than as deliberate practice itself.