As artificial intelligence technologies rapidly spread across music learning platforms, the focus of user experience has shifted from evaluating individual functions to making comprehensive judgments about the overall quality and sustainability of the learning process. However, existing research has mainly concentrated on functional configurations, learning motivation, and emotional stimulation, while systematic analyses of the structure and priority of user needs throughout the learning process remain insufficient. To address this gap, this study focuses on music learning platforms that integrate artistic learning experience with AI-based intelligent technologies and establishes a user-need identification and weighting evaluation framework grounded in real usage contexts. Semi-structured interviews were conducted to collect learners’ actual usage experiences, and systematic coding was employed to derive a user-need structure model consisting of five core categories and fourteen subcategories. The Analytic Hierarchy Process (AHP) and the CRITIC method were then applied to calculate subjective weights based on expert judgment and objective weights based on user evaluation data, which were subsequently integrated to obtain comprehensive importance values. The results reveal clear differences in the relative importance of user needs, showing that users evaluate platform value primarily from the perspective of the stability and sustainability of the learning process rather than learning motivation or emotional experience. In particular, the tolerability of practice load and the extent to which feedback can be translated into executable error-correction actions emerged as key factors. Based on these findings, this study proposes the concept of process-tolerant learning experience to explain the structure of music learning experience under AI intervention. This concept suggests that the stability of learning experience is mainly determined by the tolerability of practice intensity and the clarity of operational guidance, while emotional factors and individual differences play complementary moderating roles.
ZUI CHEN (Tue,) studied this question.