From access to mastery: Integrating AI in blended learning for equitable, inclusive, and accessible music theory educations

Although music education is considered a fundamental right for all, disparities in access remain widespread. Learners often face unequal opportunities shaped by their family backgrounds and prior experiences. This study explored the potential of AI integration in blended learning to promote inclusive and accessible music theory education. By utilizing AI-driven feedback in blended learning (AF-BL), students benefit from tailored learning experiences that promote equal opportunities for growth and reflection. A total of 43 students from a public university in China participated in a 4-week music theory course. They were divided into two groups: an experimental group (N = 22) utilizing the AF-BL method, and a control group (N = 21) following the conventional blended learning (C-BL) method. The results demonstrated that the AF-BL method significantly improved learners' music theory learning outcome and perceptions, compared to the C-BL method. Interviews with participants further highlighted the inclusivity and accessibility of the AF-BL approach, noting its ability to cater to diverse learning needs and provide equal learning opportunities for all students. The findings highlight the potential of AI in creating equitable and inclusive educational experiences, suggesting promising directions for future research and practical applications in music theory education.