Innovative Applications of Augmented Reality and Interactive Mobile Technologies in Music Education

Abstract

Against the backdrop of digital transformation in education, the integration of augmented reality (AR) and interactive mobile technologies offers new pathways for intelligent and personalized development in music education. While existing studies have explored the use of AR in musical learning environments, significant gaps remain in the dynamic capture of learner behavior data and its application in personalized adaptation. For instance, current AR-based music visualization systems often lack mechanisms for feedback on user behavior trajectories, and mobile learning platforms struggle to effectively integrate short- and long-term student behavior data in their recommendation modules. To address these limitations, this study proposes a music learning point-of-interest (POI) recommendation model based on spatiotemporal AR expansion and mobile interaction networks. The model operates through the synergistic functions of three components: a long-term behavior dependency module, a short-term behavior dependency module, and a balancing and integration module. Together, they enable precise content recommendation and context-aware learning path guidance. The findings of this study not only provide personalized learning support for music learners but also offer a technical framework and methodological reference for the development of personalized models in digital music education.