The longitudinal relationships among AI-assisted music classroom engagement, flow experience, and musical emotional intelligence: a parallel-process latent growth modeling study

Background Although artificial intelligence (AI) is increasingly integrated into music education, limited research has examined how students’ classroom engagement, flow experience, and emotional intelligence develop together over time. This study investigated the longitudinal relationships among these three psychological constructs in AI-assisted music classrooms, with particular attention to their developmental trajectories and the indirect association involving changes in flow experience. Methods A five-wave longitudinal survey was conducted among 850 undergraduate music students from four Chinese universities, with 718 participants retained at the final measurement. AI-assisted music classroom engagement, flow experience, and self-perceived emotional intelligence in musical learning contexts were assessed at each wave. Longitudinal measurement invariance was examined, and parallel-process latent growth modeling was employed to investigate developmental trajectories and associations among growth factors. Indirect effects were evaluated using 5,000 bootstrap resamples. Results Classroom engagement (slope = 0.044), flow experience (slope = 0.051), and emotional intelligence (slope = 0.046) exhibited significant positive growth over time (all p < 0.001), with significant individual differences in growth rates. The growth rate of classroom engagement was positively associated with that of flow experience ( r = 0.39), while the growth rate of flow experience was positively associated with that of emotional intelligence ( r = 0.40). Furthermore, a significant indirect association between engagement growth and emotional intelligence growth was observed through flow growth (indirect effect = 0.120, 95% bootstrap CI [0.064, 0.205]). This association remained significant in additional robustness analyses. Conclusion The findings reveal interconnected developmental trajectories of classroom engagement, flow experience, and self-perceived emotional intelligence in AI-assisted music learning contexts. Growth in flow experience emerged as a significant statistical link between changes in classroom engagement and emotional intelligence. These findings extend research on the longitudinal associations among engagement, flow, and emotional functioning in technology-supported music education, while highlighting the need for further research to establish the temporal and causal mechanisms underlying these relationships.