Research on Human-centered Design in College Music Education to Improve Student Experience of Artificial Intelligence-based Information Systems

Abstract

The integration of Artificial Intelligence (AI) technology with music instruction necessitates a delicate balance between technical advancement and the maintenance of humanistic teaching. This study examined how human-centered design concepts were used to optimize the integration of AI while also investigating the effects of AI technology on college-level music instruction in China. It aimed to identify potential, difficulties and make recommendations for ethical AI deployment in this particular environment. Semi-structured interviews with 20 music students and professors from Chinese higher education institutions were conducted using a qualitative study design. To condense significant themes and subthemes from the data, open coding, axial coding, and selective coding were used. The study revealed complex interactions between AI and Chinese music instruction. Themes included "Enhanced Learning with AI", emphasizing AI's role in motivating and personalizing music education; "User-Centric Design", emphasizing the importance of intuitive interfaces and aesthetic appeal; "Collaboration and Peer Learning", demonstrating AI's facilitation of collaborative projects; "Technical Challenges and Ethical Concerns", addressing technical obstacles and ethical concerns; and "Educator Support and Curriculum Alignment", emphasizing the importance of educator support and curriculum alignment. This study adds knowledge about how AI can be successfully incorporated into Chinese music teaching. It informs best practices for the adoption of AI, ensuring that technology enhances the learning experience for students while preserving cultural nuances. The study improves the conversation about innovative pedagogy and responsible technology integration. Implications include the potential for AI to change music education, cultural preservation, and global viewpoints. However, drawbacks such as sample bias and the dynamic nature of AI technology necessitate more study and development of educational techniques that use AI. Personalization and multimodal methods used in college music instruction in the future, to help increase student involvement. The importance of ethical issues, long-term effect analyses, and user-centered design will call for interdisciplinary cooperation. The future of AI-enhanced music education will also be shaped by assuring accessibility, diversity, and active engagement in policy and regulation discussions.