With the rapid advancement of AI in the education sector, Interactive AI Learning Environments (IAILE) have emerged as vital tools for facilitating personalized learning and enhancing learning outcomes. Trust in IAILE has become a key prerequisite for users’ continued engagement and adoption. Grounded in the Stereotype Content Model, this study develops a path model incorporating active and passive interaction to investigate how interaction experiences shape users’ social perceptions and trust, with user experience further examined as a moderating factor. Using PLS-SEM, data from 483 valid responses were analyzed. Results reveal that both active and passive interaction perceptions significantly enhance users’ social perception of system competence and warmth, which in turn positively affect trust in AI systems. Furthermore, novice users tend to rely more on perceived competence to build initial trust, whereas experienced users emphasize perceived warmth to maintain long-term trust. These findings underscore the importance of targeted interaction design and differentiated user experience strategies in cultivating trust, offering both theoretical insight and practical guidance for the development, design, and management of AI-based interactive learning systems.

With advances in artificial intelligence and computer vision, pose recognition-based feedback systems are increasingly being introduced into dance classes to support movement understanding and error correction. However, how learners interpret and adopt such feedback during actual classroom use, and how this process shapes their intention to continue using the system, remains insufficiently understood. This study develops and validates a three-layer “System–Psychological–Experience” model, with learning experience positioned as the key mechanism linking antecedent factors to usage intention. A sequential mixed-methods design was employed. In Phase 1, interviews were conducted with 20 dance majors to identify factors relevant to classroom integration. In Phase 2, structural equation modeling was performed using 398 valid survey responses. In Phase 3, explanatory interviews with five students and five teachers were conducted to further interpret the underlying mechanisms. The results showed that learning experience was the strongest direct predictor of usage intention (β = 0.409, p < 0.001). At the system layer, perceived usefulness and perceived ease of use contributed to usage intention mainly by strengthening learning experience. At the psychological layer, system trust and perceived value showed the same indirect pattern through learning experience. Teaching context also played an important role. Teaching compatibility directly enhanced learning experience, while teacher influence both strengthened learning experience and indirectly contributed to usage intention by increasing system trust and perceived value. Overall, the findings suggest that learners’ intention to continue using pose recognition-based feedback systems is closely associated with their overall learning experience in teacher-mediated classroom practice, and the study offers implications for classroom-oriented design.