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Cited by 271Digital literacy in educationArtificial Intelligence in EducationLibrary Science and Information Literacy

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

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Laupichler, Matthias Carl, Aster, Alexandra, Haverkamp, Nicolas, Raupach, Tobias·2023·Computers in Human Behavior Reports
DOI 10.1016/j.chbr.2023.100338
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Abstract

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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  • Laupichler et al. (2023) aimed to address both technical and ethical dimensions in a balanced way in their scale focusing on artificial intelligence literacy.

    “Laupichler et al. (2023), on the other hand, aimed to address both technical and ethical dimensions in a balanced way in their scale focusing on artificial intelligence literacy.”

    From Development of the Artificial Intelligence Perception and Attitude Scale (AIPAS) (2025) · cites this paper as Laupichler et al. (2023)

  • The AIPAS examines attitudes towards the use of AI tools as well as ethical perceptions, whereas Laupichler et al.'s (2023) study addresses both technical and ethical dimensions of AI literacy.

    “While Laupichler et al.'s (2023) study addresses both technical and ethical dimensions of AI literacy, the AIPAS examines attitudes towards the use of AI tools as well as ethical perceptions.”

    From Development of the Artificial Intelligence Perception and Attitude Scale (AIPAS) (2025) · cites this paper as Laupichler et al.'s (2023)

  • The items generated in the Delphi study were presented to a sample of more than 400 participants and an exploratory factor analysis found three AI-literacy factors: technical understanding, critical appraisal, and practical application.

    “The items generated in the Delphi study were presented to a sample of more than 400 participants and, subsequently, analyzed through an exploratory factor analysis that found three AI-literacy factors: technical understanding, critical appraisal, and practical application [27].”

    From Evaluating AI Courses: A Valid and Reliable Instrument for Assessing Artificial-Intelligence Learning through Comparative Self-Assessment (2023) · cites this paper as [27]

  • The SNAIL was developed by Laupichler et al. [27], validated on a sufficiently large sample, and its items can be adapted particularly well for course evaluation.

    “In this study, the 'scale for the assessment of non-experts' AI literacy' (SNAIL), which was developed by Laupichler et al. [27], was used, because it was validated on a sufficiently large sample and the items can be adapted particularly well for course evaluation.”

    From Evaluating AI Courses: A Valid and Reliable Instrument for Assessing Artificial-Intelligence Learning through Comparative Self-Assessment (2023) · cites this paper as [27]

  • SNAIL was constructed on an elaborate Delphi study.

    “SNAIL45 was constructed on an elaborate Delphi study63, it has been revalidated in another three studies49–51 including one with comparative self-assessment gains49, it is one of the few scales with evidence of reliability and responsiveness, and it demonstrated good structural validity and internal consistency, which makes it a promising instrument.”

    From A systematic review of AI literacy scales (2024) · cites this paper as [45]

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  1. Tabachnik, 2019, Vol. 7

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Authors
Laupichler, M. C., Aster, A., Haverkamp, N., & Raupach, T.
Affiliations
  • University Hospital Bonn (DE)
  • University of Bonn (DE)
Corresponding author
Laupichler, Matthias Carl
ORCID
Venue
Computers in Human Behavior Reports
Publisher
Elsevier BV
Published
2023-12
Volume
12
Pages
100338
ISSN
2451-9588 (ISSN-L)
DOI
10.1016/j.chbr.2023.100338
OpenAlex
W4387082112
Semantic Scholar
a79d835c8af0fd54cddaf501cc28db21a298cb47
Cited by
269
References
49
Citations by year
2026: 135 · 2025: 96 · 2024: 35 · 2023: 3
FWCI
63.15
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers in Human Behavior Reports (publishedVersion, cc-by)
  • bonndoc (University of Bonn) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Rheinische Friedrich-Wilhelms-Universität Bonn
Topics
  • Physical Sciences › Computer Science › Information Systems › Digital literacy in education
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Library and Information Sciences › Library Science and Information Literacy
Keywords
AI literacy, non-experts, AI literacy assessment, questionnaire development, factor structure, technical understanding, critical appraisal, practical application, teaching effectiveness evaluation

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