The dynamic relationships among AI-assisted formative assessment in music teaching, feedback literacy, and music learning engagement

Artificial intelligence (AI) tools are increasingly used to provide formative feedback in music education, yet the dynamic mechanisms linking AI-supported feedback to student engagement remain insufficiently understood. Drawing on feedback literacy theory and self-regulated learning, this study examined the reciprocal within-person relationships among engagement with AI-generated formative feedback, feedback literacy, and music-learning engagement across one semester. Three-wave panel data were collected from 420 university music-education students and analyzed using a random-intercept cross-lagged panel model to distinguish within-person fluctuations from stable between-person differences. A reliability-corrected model was also estimated to account for measurement error in the composite indicators. The results showed that increases in engagement with AI formative feedback predicted subsequent improvements in feedback literacy, which, in turn, predicted higher levels of music-learning engagement. Feedback literacy therefore emerged as a key mechanism through which AI-supported formative feedback contributed to students' learning engagement. Most reverse effects were not supported, although music-learning engagement predicted later engagement with AI feedback after measurement error was corrected. At the between-person level, epistemic trust in AI feedback accounted for a substantial proportion of the associations among the focal constructs. Overall, the findings indicate a predominantly forward but partially reciprocal dynamic and highlight the importance of feedback literacy, epistemic trust, and measurement-error correction in research on AI-supported music assessment.