人工智能扫盲量表的系统回顾。

IF 3.6 1区 心理学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Tomáš Lintner
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引用次数: 0

摘要

随着人工智能的发展及其与社会的融合所带来的机遇和挑战,人工智能素养成为人们关注的焦点。利用高质量的人工智能素养工具对于了解和促进人工智能素养发展至关重要。本系统性综述使用 COSMIN 工具评估了人工智能素养量表的质量,旨在帮助研究人员选择人工智能素养评估工具。本综述确定了 22 项研究,验证了 16 个量表,针对不同人群,包括普通人群、高校学生、中学生和教师。总体而言,这些量表具有良好的结构效度和内部一致性。另一方面,只有少数量表经过了内容效度、信度、建构效度和响应度测试。没有一个量表经过跨文化有效性和测量误差测试。大多数研究没有报告任何可解释性指标,几乎没有一项研究提供原始数据。与 13 个自我报告量表相比,目前有 3 个基于表现的量表。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A systematic review of AI literacy scales.

A systematic review of AI literacy scales.

With the opportunities and challenges stemming from the artificial intelligence developments and its integration into society, AI literacy becomes a key concern. Utilizing quality AI literacy instruments is crucial for understanding and promoting AI literacy development. This systematic review assessed the quality of AI literacy scales using the COSMIN tool aiming to aid researchers in choosing instruments for AI literacy assessment. This review identified 22 studies validating 16 scales targeting various populations including general population, higher education students, secondary education students, and teachers. Overall, the scales demonstrated good structural validity and internal consistency. On the other hand, only a few have been tested for content validity, reliability, construct validity, and responsiveness. None of the scales have been tested for cross-cultural validity and measurement error. Most studies did not report any interpretability indicators and almost none had raw data available. There are 3 performance-based scale available, compared to 13 self-report scales.

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来源期刊
CiteScore
5.40
自引率
7.10%
发文量
29
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