The Scale of Artificial Intelligence Literacy for all (SAIL4ALL): A Tool for Assessing Knowledge on Artificial Intelligence in All Adult Populations and Settings
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This study provides evidence of the psychometric quality of a new artificial intelligence (AI) literacy scale for comprehensive assessment of the concept across adult populations, regardless of the setting in which it is applied: the SAIL4ALL. It contains 56 items distributed across four themes [(1) What is AI? (a: Recognizing AI, Understanding Intelligence and Interdisciplinarity; b: General vs. Narrow); (2) What can AI do?; (3) How does AI work?; and (4) How should AI be used?], which can be used in combination or independently. Moreover, the scale is validated in two different response formats (true/false and 5-point Likert scale), each of which is applied depending on the context. The version with a true/false format is ideal for when AI literacy levels need to be assessed quickly because of its simplicity and ease of interpretation. Conversely, the 5-point Likert scale yields more nuanced responses based on the degree of confidence, providing richer insights into the respondents’ perceptions of their AI literacy. SAIL4ALL has demonstrated positive evidence of psychometric quality, and serves as a valuable tool for determining both actual and perceived knowledge of AI, thus guiding educational, organizational, and institutional AI literacy initiatives.
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SAIL4ALL is currently the only available performance-based scale targeting the general population.
“SAIL4ALL is currently the only available performance-based scale targeting general population.”
From A systematic review of AI literacy scales (2024) · cites this paper as [34]
The Introduction describes SAIL4ALL as a factual-knowledge scale intended for non-expert adults regardless of setting. However, §2.2, “Previous Measurements of AI Literacy” (page unavailable), says *most* existing scales assess perceived rather than actual knowledge and identifies Weber et al. (2023) as an exception. The paper therefore supports the scale’s performance-based, general-audience purpose, but not the claim that it is the only available scale of that kind. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMost SAIL4ALL subscales showed good evidence for internal consistency.
“Lastly, most SAIL4ALL34 subscales showed good evidence for structural validity and internal consistency, however, the psychometric properties of “What can AI do?” subscale are questionable.”
From A systematic review of AI literacy scales (2024) · cites this paper as [34]
Sections 4.3.2–4.3.3 report acceptable internal consistency for “What Can AI Do?” with the five-point response scale and adequate estimates for “How Does AI Work?” in both samples. But §4.3.4 says reliability was inadequate for the true/false “How Should AI Be Used?” scale, and §5.2.2 notes low reliability in two true/false scales. The broad claim is supported for several scales, but it obscures these response-format limitations. (LLM, conf=0.80)
Experimental — accuracy not yet measuredThe psychometric properties of the "What can AI do?" subscale of SAIL4ALL are questionable.
“Lastly, most SAIL4ALL34 subscales showed good evidence for structural validity and internal consistency, however, the psychometric properties of “What can AI do?” subscale are questionable.”
From A systematic review of AI literacy scales (2024) · cites this paper as [34]
Most SAIL4ALL subscales showed good evidence for structural validity.
“Lastly, most SAIL4ALL34 subscales showed good evidence for structural validity and internal consistency, however, the psychometric properties of “What can AI do?” subscale are questionable.”
From A systematic review of AI literacy scales (2024) · cites this paper as [34]
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‹[…] deswegen ist GPT nicht immer so gut […]›
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- Authors
- Soto-Sanfiel, M. T., Angulo-Brunet, A., & Lutz, C.
- Affiliations
- National University of Singapore (SG)
- Universitat Oberta de Catalunya (ES)
- BI Norwegian Business School (NO)
- Publisher
- Center for Open Science
- Published
- 2024-04-30
- OpenAlex
- W4396517756
- Cited by
- 20
- References
- 53
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SocArXiv (OSF Preprints) (submittedVersion, cc-by-nd)
- Topics
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- AI literacy, AI literacy assessment, psychometric validation, adults, AI knowledge, AI understanding, artificial intelligence use, AI literacy education
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