Development of the “Scale for the assessment of non-experts’ AI literacy” – An exploratory factor analysis

探索性因素分析 读写能力 比例(比率) 因子(编程语言) 心理学 计算机科学 数据科学 心理测量学 地理 教育学 发展心理学 地图学 程序设计语言
作者
Matthias Carl Laupichler,Alexandra Aster,Nicolas Haverkamp,Tobias Raupach
出处
期刊:Computers in human behavior reports [Elsevier BV]
卷期号:12: 100338-100338 被引量:199
标识
DOI:10.1016/j.chbr.2023.100338
摘要

Artificial Intelligence competencies will become increasingly important in the near future. Therefore, it is essential that the AI literacy of individuals can be assessed in a valid and reliable way. This study presents the development of the “Scale for the assessment of non-experts' AI literacy” (SNAIL). An existing AI literacy item set was distributed as an online questionnaire to a heterogeneous group of non-experts (i.e., individuals without a formal AI or computer science education). Based on the data collected, an exploratory factor analysis was conducted to investigate the underlying latent factor structure. The results indicated that a three-factor model had the best model fit. The individual factors reflected AI competencies in the areas of “Technical Understanding”, “Critical Appraisal”, and “Practical Application”. In addition, eight items from the original questionnaire were deleted based on high intercorrelations and low communalities to reduce the length of the questionnaire. The final SNAIL-questionnaire consists of 31 items that can be used to assess the AI literacy of individual non-experts or specific groups and is also designed to enable the evaluation of AI literacy courses’ teaching effectiveness.
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