An Exploration of the Use of and the Attitudes Toward Artificial Intelligence in Music Education Settings

Abstract

The purpose of this study was to determine what artificial intelligence (AI) technologies are being used in music education settings and to examine factors that influence the attitudes of teachers and university students toward the use of AI. I administered the Use of AI in Music Education Settings questionnaire to teachers and university students associated with the National Association for Music Education (N  = 213). I found that many music educators and university music education students reported never using generative AI (52.2%) or traditional AI (44.6%). Results of a confirmatory factor analysis demonstrate a good fit for the constructs of performance expectancies, effort expectancies, social influences, and facilitating conditions to load onto the second-order factor of attitude with significant factor loadings of observed variables. Additionally, results from an analysis of variance revealed that attitudes toward AI use appear conflicted among participants, with statistically significant differences among the groups. Finally, I found moderate, positive, statistically significant correlations between one's attitude toward AI and actual uses of generative AI (r  = .466, p < .001), traditional AI (r  = .412, p < .001), and AI in general (r  = .526, p < .001).