ArticleFrontiers in psychology2026
Modeling music student teachers' behavioral intention of using artificial intelligence in China.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-driven psychological and cognitive decision processes in professional practice: a systematic review using music teachers as an instrumental case.Frontiers in psychology · 2026Pooled it
- Beyond output metrics: reframing AI-assisted vocal pedagogy through human learning and educational value.Frontiers in psychology · 2026Article
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Authors and funding
1 author.
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Abstract
Introduction: The integration of artificial intelligence (AI) into education is rapidly increasing worldwide and governments actively promote teachers' positive attitudes toward AI and its use in instructional practices. Although prior research has highlighted the potential of AI in music education, limited studies have examined the factors influencing pre-service music teachers' intentions to use AI in teaching. Methods: This study employed an online questionnaire based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. A total of 370 pre-service music teachers participated in the survey, and structural equation modeling was used to examine the determinants of their intentions to integrate AI into teaching. Results: The proposed UTAUT model explained 62.4% of the variance in pre-service music teachers' intentions to use AI. The results indicated that social influence, performance expectancy, and effort expectancy positively predicted intentions to use AI, whereas education policy and facilitating conditions had negative direct effects. AI usage habit showed no significant effect. Notably, education policy demonstrated positive indirect effects through effort expectancy and social influence, indicating a dual mechanism of policy influence. Discussion: The findings of this study provide insights into how individual, institutional, and policy-related factors jointly shape pre-service music teachers' intentions to adopt AI in education. This study then discussed implications for AI in music teacher training programs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.