An Analysis of Artificial Intelligence Systems on the Teaching of Music Improvisation, Composition, and Instrumental Pedagogy

Artificial intelligence (AI) has experienced a significant boom in the first half of the 2020s and has significant implications for the fields of music composition, music production, and music education. AI Large language models (LLMs) such as ChatGPT are frequently used sources for students on a wide range of academic subjects, and can provide in-depth, well-informed and eloquently written explanations in a matter of seconds. Music generative AI programs such as Suno and UDIO can almost instantaneously compose and produce musical recordings from word-generated prompts from users. Noise cancelling AI software such as Moises can create isolated instrumental tracks from musical recordings, which gives listeners the opportunity to hear instrumental parts with greater clarity and detail. This dissertation aims to explore the opportunities and risks that AI presents to collegiate music education. With theoretical foundations in Howard Gardner’s theory of multiple intelligences and Malcolm Knowles’s writings on adult education, the paper utilizes a qualitative research method to develop a practical framework for integrating artificial intelligence into the teaching of music composition, music theory, music improvisation, and music pedagogy. This researcher will cultivate this educational framework through research of previously published literature on artificial intelligence and interviews with educators in music education and music technology.