College Music Teachers' Perceptions of AI in Music Creating, Learning, and Teaching

Introduction

Artificial Intelligence in Music Education (AI-ME) is defined as "any computer-based learning system which has some degree of autonomous decision-making with respect to some aspect of its interaction with its users" (Holland, 1995). This definition includes AI tools in three broad categories:

• Technology for music teaching and learning

• Music analysis software

• Music production and composition software

A goal of this research is to clearly define the types of Al tools available to music faculty.

Al tools, such as machine learning algorithms, virtual reality simulations, and automated music composition software, have demonstrated the potential to revolutionize the way music is taught and learned (Holland, 2000; Wise, et al., 2011). However, there is limited empirical research on how college music educators perceive and engage with these technologies.

The purpose of this research is to understand what Al tools are currently being used by music faculty, how they impact student learning, and what do those faculty believe are the important pedagogical elements that should be included in teacher training programs.

Methods

Computer based tools for music teaching, learning, creation, and analysis have been in development and examined by music educators since the 1970’s (Roads, 1980). New applications are released regularly and the need for a categorization of these tools was the first task of this research. Initial results from a Systematic Review of the literature (Higgins & Thomas, 2024) indicates 9 types of AI-ME in 3 categories.

A survey asking music instructors about their perceptions and use of AI, their access to training on AI and other technology, and university affiliation was distributed to a pilot group (N=28) to evaluate the content validity. A revised survey is currently under review by the author’s affiliated IRB. An email containing a Qualtrics survey link will be distributed to all USG part- and full-time music faculty and administration.

Results from three distribution rounds will be analyzed to compare results between and within USG institutions, leading to further refinement of the AI-ME categorization review and used to develop training for college music instructors.

AI-ME Technology Categories

A systematic review (Higgins & Thomas, 2024) examines extant literature for common features, analyses, results, and/or discussion elements.

Similar to content analyses that have been published periodically, examining basic features and methodologies (Yarbrough, 1984, 2002), studies employing quantitative methodologies (Schmidt & Zdzinski, 1993), popular music pedagogy (Mantie, 2013), research paradigm shifts (Jorgensen & Ward-Steinman, 2015), JRME publication decisions (Sims et al., 2016), and research interests and trends (Stambaugh & Dyson, 2016), a systematic review of music education literature includes a greater variety of literature sources, advanced modeling methods, and participation by multiple researchers from disparate specializations.

Preliminary analysis of the literature from music education focused academic journals (N=14) indicates seven major categories defined by the intended use and realized learning outcomes of computer-based technology for music education. Further refinement of the search terms to include artificial intelligence, machine learning, language model(ing), generative models, and highlighted 3 categories representing 9 types of AI-ME.

• Teaching and Learning

• Music tutoring and learning

• Virtual music teachers

• VR music trainers

• Music Analysis

• Theoretical analysis and research

• Music genre analysis and recommendations

• Voice and music recognition

• Music Production and Composition

• Music production assistance

• AI generated music composition

• AI enhanced instruments

These categories were then used to create a survey tool that includes current examples of AI-ME that college music instructors can test and evaluate on their own. Results from the survey will aid in the development of training modules for college music faculty.

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