The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Development of the “Scale for the assessment of non-experts’ AI literacy” – An exploratory factor analysis
Open accessAbstract
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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In the citing paper
“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)
In the citing paper
“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)
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“GSE-6AI30, MAIRS-MC43, and SNAIL45 have been validated specifically for medical students, which makes them the instruments of choice if medical students are to be assessed.”
From A systematic review of AI literacy scales (2024) · cites this paper as [45]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“AI literacy encompasses not only technical knowledge but also the ability to critically evaluate the ethical implications and societal impacts of AI systems (Carolus et al. 2023;Laupichler et al. 2023;Long and Magerko 2020;Ng et al. 2024;Touretzky et al. 2019;Wang et al. 2023).”
From Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study (2025) · cites this paper as Laupichler et al. 2023
In the citing paper
“The validity of the self-report measurement instruments for AI literacy [11,12,14,15] has only been tested regarding their factorial structure.”
From Meta AI literacy scale: Further validation and development of a short version (2024) · cites this paper as [12]
In the citing paper
“Existing scales, such as those developed by Laupichler et al. [3,4], provide robust measures of general AI literacy, but they lack domain-specific items relevant to nursing practice and were validated in Western educational contexts.”
From Development and psychometric validation of the Thai AI Literacy Scale for Nursing Students in Thailand: a methodological study (2026) · cites this paper as [3]
In the citing paper
“studies and scales have been criticized for primarily focusing on individuals’ basic knowledge of AI and skills in their applications”
From Assessment of AI ethical reflection: the development and validation of the AI ethical reflection scale (AIERS) for university students (2025) · cites this paper as Laupichler et al., 2023
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Metadata
- Authors
- Laupichler, M. C., Aster, A., Haverkamp, N., & Raupach, T.
- Affiliations
- University Hospital Bonn (DE)
- University of Bonn (DE)
- Corresponding author
- Laupichler, Matthias Carl
- Venue
- Computers in Human Behavior Reports
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 12
- Pages
- 100338
- ISSN
- 2451-9588 (ISSN-L)
- 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
- gold
- doi.org
- 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
- Social Sciences › Education › Digital literacy in education
- Social Sciences › Education › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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