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Instrumental music teachers' perceptions and acceptance of Al integration in teaching: a mixed-methods study based on
1Moscow State Normal University, Moscow, Russia.
Introduction:
The application of Artificial Intelligence (AI) is fast becoming widespread in educational environments, especially within the field of music education. However, instrumental music teachers, who are highly dependent on their physical instructions and aesthetic judgement, have never undergone an analysis of how they perceive and embrace AI technologies. This study is based on an expanded version of Acceptance and Use of Technology (UTAUT2), which includes two additional variables that relate to the context: Perceived Threat to Teaching Artistry (PTTA) and Perceived Irreplaceability of Embodied Teaching (PIET).
Methods:
The method employed by this study was an explanatory sequential mixed methods approach, wherein the first phase involved the use of Partial Least Squares Structural Equation Modeling (PLS-SEM) on survey data gathered from 352 in-service instrumental music teachers in China. In the qualitative phase, reflexive thematic analysis was conducted on semi-structured interviews with 17 instrumental music teachers.
Results:
Results show that performance expectancy, habit, hedonic motivation, effort expectancy, and social influence significantly and positively predict behavioral intention, while facilitating conditions and price value show no significant effect. Both PTTA and PIET significantly and negatively affect behavioral intention, with PIET's negative effect being more pronounced among experienced teachers and string/wind instrument teachers. The qualitative phase yielded four core themes, revealing a conditional acceptance pattern: teachers acknowledge AI's supplementary value in basic skill training but firmly maintain their irreplaceable role in aesthetic judgment, individualized expressive guidance, and embodied interaction.
Discussion:
This study extends UTAUT2's applicability to the instrumental music teaching context, where artistry and embodiment are central, providing empirical evidence for designing and promoting AI teaching tools.
