为中国本科生开发和验证人工智能依赖度表,并进行初步探索
Houyu Wu1,2, Haiyang Ni3, Wenfu Luo2,4
1Personnel Department, Neijiang Normal University, Neijiang, China.
Frontiers in psychology
|February 4, 2026
概括
研究人员开发了AI依赖度表 (AIDep-22) 来衡量学生在高等教育中过度依赖人工智能. 这种经过验证的工具有助于识别需要支持的学生,并促进平衡的人与人工智能的互动.
科学领域:
- 教育心理学教育心理学
- 教育中的人工智能
- 高等教育研究 高等教育研究
背景情况:
- 生成性人工智能越来越多地用于高等教育.
- 学生过度依赖人工智能对批判性思维和自主学习构成风险.
- 缺乏一个验证的尺度来衡量AI依赖.
研究的目的:
- 开发和验证人工智能依赖度表 (AIDep-22).
- 在四个方面评估人工智能依赖:情感,功能,认知和失去控制.
- 为教育工作者和研究人员提供一个工具.
主要方法:
- 规模建设涉及项目生成,专家评论和认知访谈.
- 心理测量评估使用了两个学生样本 (N=400个) 的探索性和确认因素分析.
- 评估了内部一致性,收性,区分性和与标准相关的有效性.
主要成果:
- 22项的AIDep-22尺度表现出了出色的心理测量特性.
- 探索性和确认性因素分析支持了四个因素结构.
- 更高的人工智能依赖与男性学生,高年级学生,应用专业和频繁的人工智能用户有关.
结论:
- AIDep-22是衡量学生AI依赖性的可靠和有效仪器.
- 该尺度可以识别有风险的学生,并告知有针对性的干预措施.
- 研究结果支持在学术界促进平衡的人与人工智能的关系.
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