Determinants of Preservice Music Teachers' Intention to Integrate AI-Based Accompaniment Tools into Classroom Teaching: An Extended Technology Acceptance Model

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Authors: Liu Chang
Cited by: 0
References: 57

Abstract

Abstract Generative artificial intelligence has revolutionized how musicians approach the enduring obstacle of obtaining real-time piano accompaniment during instruction. While numerous AI-powered solutions now exist for this purpose, scholarly understanding of what motivates prospective educators to embrace such innovations remains insufficient. The current research synthesizes two established theoretical perspectives—the Technology Acceptance Model alongside Technological Pedagogical Content Knowledge—to investigate variables affecting music education majors' willingness to incorporate intelligent accompaniment applications. Questionnaire responses were gathered from 150 undergraduates pursuing teacher certification in music at a Chinese provincial institution. Statistical procedures including descriptive analysis, internal consistency evaluation, structural validation, bivariate correlation assessment, and sequential multiple regression were performed through IBM SPSS 26.0 and AMOS 24.0 software packages. Findings indicate that TPACK (β = .447, p < .001), attitudinal orientation toward adoption (β = .417, p < .001), and usefulness beliefs (β = .127, p < .05) serve as meaningful predictors of implementation intentions. Ease of operation showed no direct influence but exhibited mediated pathways via usefulness perceptions and attitudinal dispositions. The combined framework captured 84.4% of variance in adoption intentions (R² = .844). Bias assessment confirmed that single-respondent methodology does not compromise result integrity. These discoveries suggest that preparing future music instructors demands attention beyond operational training toward cultivating pedagogical expertise for AI utilization, strengthening both applied value recognition and technology-pedagogy-content understanding. Ramifications for music educator preparation throughout Asia-Pacific territories receive consideration.

Fields of Study & Subjects

Diverse Music Education Insights Technology Adoption and User Behaviour Artificial Intelligence in Education

Publication details

Journal / source
Research Square
Publication date
3 March 2026
Language
EN
Document type
Preprint