The aim of our research paper is to present and discuss a theoretically developed model for categorizing possible opportunities for applications of AI in music lessons from both the teachers' and the learners' perspectives. The model is intended to provide a comprehensive vision of how and to what degree the usage of AI will influence the preparation, conduction and evaluation of music lessons. The model serves as a framework for designing AI-related music-teacher trainings within the project DigiProSMK (Digitalisierungsbezogene und digital gestützte Professionalisierung von Sport-, Musik- und Kunstlehrkräften; cf. https://lernen.digital/verbuende/digiprosmk/) as part of the nationwide digitization strategy lernen:digital of the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung – BMBF).  

Our conceptual framework combines three existing concepts into a three-dimensional model. The first dimension consists of different fields of activity when dealing with music in class. In this respect, we follow the system of five fields, which was established by Dankmar Venus in 1969 and is still widely used in German music education. With regards to the teacher’s perspective, the second dimension divides the process of teaching into the three phases of preparation, conduction and evaluating regarding music lessons (cf. Heimann, Otto & Schulz, 1965). The last dimension consists of the tiers of the SAMR model by Ruben R. Puentedura (2006). This model describes four different tiers of technology integration (depending on the degree of transformation which is already gained).  

The interconnection of these three models finally results in a three-dimensional matrix with a total of 60 operational fields that provides ideas for a wide variety of different uses of AI in music lessons. At last, we collected examples for possible practical cases regarding the different fields within this matrix. The model itself and these operational scenarios will be presented and discussed in this research paper.   

This model provides both a framework for categorizing application scenarios of AI in music education and a basis for reflecting on these scenarios. Based on the three dimensions of the model, we discuss how the use of AI in music education needs to be considered regarding the professionalization of music teachers, subject-specific questions, and ethical dilemmas. This comprehensive approach can contribute to a balanced and thoughtful integration of AI in music education. 

• Digital environment and music Education

Benjamin Hecht is working at the Rostock University for Music and Drama in the field of digitaleducation. He focuses on the areas of videos in different scenarios of music education, AI in thecontext of music education and teacher training, using digital tools for asynchronous teachingmethods and digital music making.

Oliver Krämer is a Professor of Music Education at the University of Music and Theatre Rostock, where he leads the study programmes for music education in secondary schools. He holds degrees in composition, music education, and German studies, and earned his doctorate on the visualization of music. His research interests focus on the intersection of music and visual arts, the didactics of contemporary music, improvisation, and curriculum development. Currently, he is involved in a research consortium with ten universities (DigiProSMK), focusing on the professionalization of music teachers in the context of digitalization. In 2015, he organized the 23rd EAS Conference in Rostock, titled "Open Ears – Open Minds," which emphasized listening to and understanding music. From 2016 to 2017 and since 2022, he has co-led the EAS Student Forum together with Prof. Dr. Branka Rotar Pance from the University of Ljubljana, Slovenia.