在高等教育中探索人工智能工具的采用:来自PLS-SEM模型的证据整合了多式联络素养,自我效能和大学支持
Zixuan Zhao1,2, Qi An1,2, Jiaqi Liu3
1College of Tourism and Landscape Architecture, Guilin University of Technology, Guilin, China.
Frontiers in psychology
|September 24, 2025
概括
人工智能 (AI) 工具的感知实用性强烈预测学生采用. 多模式素养,自我效能和大学支持提高了AI的实用性和易用性,促进了AI在高等教育中的整合.
科学领域:
- 教育技术的教育技术
- 人与计算机的交互
- 教育中的人工智能
背景情况:
- 人工智能 (AI) 工具在高等教育中的整合正在迅速增加.
- 了解影响学生采用人工智能技术的因素对于有效实施至关重要.
- 现有的模型,如技术接受模型 (TAM),为检查技术采用提供了一个框架.
研究的目的:
- 调查多式联络素养,自我效能和大学支持对学生对人工智能工具的态度和采用意图的影响.
- 通过结合个人和机构因素来扩展技术接受模式.
- 确定大学生接受人工智能工具的关键预测因素.
主要方法:
- 对498名大学生进行了定量研究.
- 调查数据收集用于测量多式联络素养,自我效能,大学支持,感知有用性,感知易用性,态度和采用人工智能工具的意图.
- 部分最小平方结构方程建模 (PLS-SEM 4.0) 和SPSS 29用于数据分析.
主要成果:
- 感知到的有用性是对人工智能工具的态度和使用它们的意图的最重要的预测因素.
- 多模式素养,自我效能和大学支持积极和显著地影响了感知到的有用性和感知到的易用性.
- 所有先前的变量都对技术接受模型的核心结构产生了重大影响.
结论:
- 这些发现突出了感知到的有用性在推动学生采用人工智能的关键作用.
- 个人因素 (多模式素养,自我效能) 和机构支持显著提高AI工具的感知价值和可用性.
- 将个人和机构层面纳入TAM框架为促进在高等教育环境中有效采用人工智能工具提供了有价值的见解.
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