关于产生性人工智能在中学后教育中的使用意图影响因素的研究:基于AIDUA扩展模型的经验分析
1School of Journalism and New Media, Xi'an Jiaotong University, Xi'an, China.
Frontiers in psychology
|October 3, 2025
概括
高等教育中生成人工智能 (AIGC) 的接受取决于感知质量和安全,而不仅仅是社会影响. 了解用户评估是有效的AI采用策略的关键.
科学领域:
- 人工智能的人工智能
- 高等教育 高等教育
- 认知心理学 认知心理学
背景情况:
- 传统模型不足以解决在高等教育中采用生成人工智能 (AIGC) 的心理和伦理障碍.
- 需要一个新的综合认知行为框架来理解AIGC接受的复杂性.
- 现有的研究往往忽视了人工智能采用的认知,行为和社会伦理因素的微妙相互作用.
研究的目的:
- 开发和经验测试AIGC接受高等教育的综合认知行为框架.
- 调查社会伦理 (伦理风险,可解释性) 和技术性能 (生成质量,上下文意识) 维度对用户接受度的影响.
- 检查核心技术信仰和情感的调解作用,以及人口统计学变量的调节作用.
主要方法:
- 使用结构方程建模 (SEM) 和多组分析.
- 调查数据来自中国15个不同机构的462名大学生.
- 在认知评估理论中的扩展的AIDUA模型指导了框架开发.
主要成果:
- 感知能力和安全是AIGC认知评估的主要驱动因素.
- 技术性能 (产品质量) 和社会伦理 (可解释性,伦理风险) 维度显著预测性能预期.
- 伦理风险感知对绩效预期产生负面影响,并增加感知认知负担 (努力预期).
结论:
- AIGC验收是一个多阶段的评估过程,受实用性,伦理和用户体验的影响.
- 个人和文化背景变量显著适度AIGC采用途径,反驳一个普遍的学生用户.
- 促进AIGC采用需要平衡技术进步与透明的缓解道德和认知方面的担忧.
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