International Journal of Science, Engineering and TechnologyAuthors: Liang Qing, Chng Lay Kee
Abstract: This study investigates the influence of AI-driven personalized learning systems on student engagement within music education settings at higher education institutions in China. Employing a mixed-methods approach, the research utilized structured questionnaires for quantitative analysis and semi-structured interviews for qualitative insights. Findings indicate that AI-driven personalized learning systems significantly enhance student engagement by offering adaptive content, real-time feedback, and individualized learning pathways. These systems foster motivation, self-efficacy, and a sense of ownership in the learning process. However, challenges related to equitable access, data privacy, and the need for teacher training were identified. The study concludes that while AI-driven personalized learning systems hold transformative potential for music education, their integration must be accompanied by robust ethical guidelines and inclusive strategies to maximize benefits for all students.
DOI: https://doi.org/10.5281/zenodo.15798044
