探索预服务音乐教师对生成人工智能的接受:一种PLS-SEM-ANN方法
1Communication University of China, Beijing, China.
Frontiers in psychology
|July 14, 2025
概括
预服务音乐教师音乐教师
科学领域:
- 教育技术的教育技术.
- 音乐教育中的人工智能
背景情况:
- 将生成人工智能 (生成AI) 整合到高等教育教学中正在迅速发展.
- 了解用户接受对于在音乐教师培训中有效采用技术至关重要.
研究的目的:
- 调查影响预服务音乐教师在教学实践中接受和使用生成人工智能的意图的因素.
- 将扩展的技术接受和使用模型2 (UTAUT2) 的统一理论应用于音乐教育中的生成人工智能.
主要方法:
- 使用了定量研究方法.
- 从高等教育机构的预服务音乐教师那里收集数据.
主要成果:
- 感知风险,社会影响和习惯是使用生成AI的行为意图的重要预测因素.
- 行为意图和感知风险是实际生成人工智能使用行为的关键决定因素.
- 敏感性分析验证了行为意图的中心作用和感知风险的负面影响.
结论:
- 该研究提供了对音乐教育中技术接受的理论见解.
- 提供了实用建议,以促进未来音乐教育工作者采用和有效利用生成人工智能.
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