预服务教师的GenAI焦虑,技术自我效率和TPACK:它们与行为意图的结构关系,以设计GenAI辅助教学
Kai Wang1, Qianqian Ruan2, Xiaoxuan Zhang3
1Center for Teacher Education Research, Beijing Normal University, Beijing 100091, China.
Behavioral sciences (Basel, Switzerland)
|May 24, 2024
概括
服务前教师的教师.
科学领域:
- 教育技术的教育技术.
- 教育中的人工智能
- 教师培训 培训教师培训
背景情况:
- 生成型人工智能 (GenAI) 为K-12教育带来了重大机遇和挑战.
- 了解预服务教师的观点对于将GenAI工具有效整合到教学实践中至关重要.
- 技术接受和使用的统一理论 (UTAUT) 为研究技术采用提供了一个框架.
研究的目的:
- 探索影响预服务教师设计GenAI辅助教学的行为意图的因素.
- 研究技术教学内容知识 (TPACK),基因人工智能焦虑,技术自我效能,社会影响,绩效预期,努力预期和促进条件的作用.
- 在教师培训中采用GenAI的背景下应用UTAUT模型.
主要方法:
- 对606名服务前教师进行了一项定量研究.
- 用统计方法分析数据,以确定行为意图的预测因素.
- 扩展了UTAUT模型,包括GenAI采用的相关变量.
主要成果:
- 基因人工智能焦虑,社会影响和绩效预期是预服务教师打算设计基因人工智能辅助教学的重要预测因素.
- 预期的努力和促进条件没有显示出与行为意图的统计学意义上的关联.
- 在提供的摘要中,TPACK和技术自我有效性并没有被明确地列为重要的预测因素,但它们是研究模型的一部分.
结论:
- 预服务教师使用GenAI的意图受到他们的焦虑程度,对社会影响的看法和预期的绩效效益的影响.
- 促进GenAI采用的努力应该解决教师的焦虑,并强调这些工具的实际好处.
- 进一步的研究可以探索TPACK和自我有效性的细微差别,在GenAI的教育背景下.
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