通过扩展技术接受框架,模拟教师培训学生采用大型语言模型
Yulin Gong1, Chengshu Xu2,3, Suwen Luo4
1China Institute of Rural Education Development, Northeast Normal University, Changchun City, 130024, China.
Scientific reports
|September 1, 2025
概括
教师培训的学生
科学领域:
- 教育技术
- 教育中的人工智能
背景情况:
- 大型语言模型 (LLM) 越来越多地被整合到写作支持教育中.
- 对于教育的数字化转型来说, 了解教师培训学生对LLM的接受度至关重要.
研究的目的:
- 调查影响教师教育学生采用LLM的因素.
- 将技术接受模型 (TAM) 扩展到动机,风险,自我效能和经验等变量.
主要方法:
- 使用结构方程建模 (SEM) 来分析调查数据.
- 收集了552名中国四年级教师培训学生的数据.
主要成果:
- 主观规范强烈预测行为意图.
- 感知到的易用性对LLM的态度有积极的影响.
- 感知到的时间风险会对感知到的实用性产生负面影响;隐私风险会影响感知到的易用性.
- 使用经验增强学习动力,促进学习.
结论:
- 这些发现扩大了TAM在教育技术中的应用.
- 提供设计技术培训和优化LLM用户体验的见解.
- 为未来的教师提供数字素养培训的框架.
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