对人工智能识字量表进行系统审查
1Department of Educational Sciences, Faculty of Arts, Masaryk University, Brno, Czech Republic. tomas.lintner@mail.muni.cz.
NPJ science of learning
|August 6, 2024
概括
高质量的人工智能 (AI) 识字评估工具是必不可少的. 这次审查发现,大多数人工智能识字量表缺乏强有力的验证,强调需要更好的仪器开发和报告人工智能识字研究.
科学领域:
- 教育技术的教育技术
- 心理测量 心理测量 心理测量
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 融入社会既带来了机遇,也带来了挑战.
- 人工智能素养对于有效导航这些发展至关重要.
- 需要高质量的工具来评估和促进人工智能素养.
研究的目的:
- 系统地审查和评估现有的AI识字量表的质量.
- 引导研究人员选择适当的工具来评估人工智能素养.
- 识别当前AI识字测量工具的验证中存在的差距.
主要方法:
- 对人工智能识字量表的系统文献综述.
- 使用COSMIN (基于共识的健康测量仪器选择标准) 工具进行质量评估.
- 在不同人群中识别经过验证的尺度.
主要成果:
- 在22项研究中确定了16个AI识字量表,针对一般人群,学生和教师.
- 尺度通常显示出良好的结构有效性和内部一致性.
- 在测试内容有效性,可靠性,构造有效性,响应性,跨文化有效性和测量错误方面存在重大差距.
- 很少有研究报告了可解释性指标或提供原始数据.
- 大多数尺度是自我报告的 (13),而不是基于绩效的 (3).
结论:
- 虽然一些人工智能识字量表显示出足够的结构有效性和内部一致性,但许多缺乏全面的心理测量评估.
- 需要进一步的研究来开发和验证强大的AI识字工具,特别是在跨文化有效性和测量误差等领域.
- 改进报告标准,包括可解释性和数据可用性,对于推进人工智能识字研究和实践至关重要.
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