相关实验视频
一个两步结构方程建模和可解释的机器学习框架,以了解大学生采用生成AI:平衡内在动机和感知风险
Daixing Zeng1, Xiaoqin Xu1, Tianxing Zhu2
1Department of Statistics, Chengdu University of Information Technology, Chengdu, China.
Frontiers in psychology
|February 16, 2026
概括
大学的学生大学生.
科学领域:
- 教育中的人工智能
- 高等教育技术采用 高等教育技术采用
- 人与计算机的交互
背景情况:
- 大学学生采用生成性AI (GenAI) 的数量正在迅速增加.
- 了解采用驱动因素和障碍对于在高等教育中负责任地整合人工智能至关重要.
- 像传统的结构方程建模 (SEM) 等现有方法在识别所有相关因素方面存在局限性.
研究的目的:
- 引入和验证一个新的两步SEM-XML框架来分析GenAI采用.
- 调查影响学生GenAI使用的感知好处和风险的相互作用.
- 确定推动和阻碍大学生采用GenAI的关键因素.
主要方法:
- 对中国西南部880名大学生进行了横截面调查.
- 采用了一种新的两步SEM-XML框架,集成SEM用于假设测试和XML用于数据驱动因素识别.
- 该框架混合了技术接受模型,计划行为理论,感知风险和知识态度实践模型的构造.
主要成果:
- 感知到的有用性和个人利益与GenAI采用有积极的相关性.
- 关于伦理,准确性和学术诚信的感知风险显著抑制了GenAI的使用.
- 内部的态度和外部的压力相互作用,塑造了学生们对GenAI的接受.
结论:
- 开发的SEM-XML框架有效地分析了GenAI的采用因素.
- 教育机构,政策制定者和开发人员可以利用这些见解进行战略AI整合.
- 解决感知到的风险对于促进学术界负责任和有益的GenAI使用至关重要.
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