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探索职前音乐教师对生成式人工智能的接受度：一种偏最小二乘结构方程模型-人工神经网络方法

Exploring pre-service music teachers' acceptance of generative artificial intelligence: a PLS-SEM-ANN approach.

作者信息

He Sirui, Ren Yuhong

机构信息

Communication University of China, Beijing, China.

Hebei Normal University, Shijiazhuang, Hebei Province, China.

出版信息

Front Psychol. 2025 Jun 27;16:1571279. doi: 10.3389/fpsyg.2025.1571279. eCollection 2025.

DOI:10.3389/fpsyg.2025.1571279
PMID:40657578
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12247848/
Abstract

INTRODUCTION

Based on the extended Unified Theory of Acceptance and Use of Technology Model 2 (UTAUT2), explores the intention to accept Generative Artificial Intelligence (Generative AI) technology in teaching and its influencing factors among pre-service music teachers in higher education.

METHOD

Quantitative research.

RESULTS

The results indicate that Perceived Risk, Social Influence, and Habit significantly influence Behavioral Intention, while Behavioral Intention and Perceived Risk are key predictors of actual use behavior. Sensitivity analysis further confirms the central role of Behavioral Intention and the inhibitory effect of Perceived Risk.

DISCUSSION

The findings provide theoretical and practical guidance for promoting the application of generative AI in music education.

摘要

引言

基于扩展的技术接受与使用统一理论模型2（UTAUT2），探讨高等教育中准音乐教师对生成式人工智能（生成式AI）技术在教学中的接受意愿及其影响因素。

方法

定量研究。

结果

结果表明，感知风险、社会影响和习惯显著影响行为意愿，而行为意愿和感知风险是实际使用行为的关键预测因素。敏感性分析进一步证实了行为意愿的核心作用和感知风险的抑制作用。

讨论

研究结果为促进生成式AI在音乐教育中的应用提供了理论和实践指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eef2/12247848/edf5ae547012/fpsyg-16-1571279-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eef2/12247848/eb3f4abc71ee/fpsyg-16-1571279-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eef2/12247848/ba80e9942442/fpsyg-16-1571279-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eef2/12247848/c885c3624c60/fpsyg-16-1571279-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eef2/12247848/edf5ae547012/fpsyg-16-1571279-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eef2/12247848/eb3f4abc71ee/fpsyg-16-1571279-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eef2/12247848/ba80e9942442/fpsyg-16-1571279-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eef2/12247848/c885c3624c60/fpsyg-16-1571279-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eef2/12247848/edf5ae547012/fpsyg-16-1571279-g0004.jpg

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本文引用的文献

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Pre-Service Teachers' GenAI Anxiety, Technology Self-Efficacy, and TPACK: Their Structural Relations with Behavioral Intention to Design GenAI-Assisted Teaching.职前教师的生成式人工智能焦虑、技术自我效能感与技术教学法内容知识：它们与设计生成式人工智能辅助教学行为意图的结构关系。
Behav Sci (Basel). 2024 Apr 29;14(5):373. doi: 10.3390/bs14050373.
2
Chinese Pre-service Music Teachers' Perceptions of Augmented Reality-Assisted Musical Instrument Learning.中国职前音乐教师对增强现实辅助乐器学习的认知
Front Psychol. 2021 Feb 4;12:609028. doi: 10.3389/fpsyg.2021.609028. eCollection 2021.
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Construct validity: advances in theory and methodology.
结构效度：理论与方法的进展
Annu Rev Clin Psychol. 2009;5:1-25. doi: 10.1146/annurev.clinpsy.032408.153639.