Permanent record
RIR-2077 · Logged 13 July 2026
Assessing Artificial Intelligence Literacy: Development and Validation of the SAIL4ALL Psychometric Scale
The study introduces and validates the SAIL4ALL scale, a comprehensive tool for measuring AI literacy across diverse adult populations.
In plain words
How can we tell how well adults understand artificial intelligence? This study builds and checks a questionnaire called SAIL4ALL that measures this reliably across different groups of adults, so AI learning programmes can be judged.
Primary research question
How can AI literacy be reliably measured and assessed across diverse adult populations?
Knowledge gap
What remains worth asking
Standardized, psychometrically validated tools for measuring AI literacy across different demographics are currently lacking.
Potential contribution
Why it may matter
Provides a foundational instrument for evaluating the effectiveness of AI literacy initiatives in various sectors.
Academic placement
Academic fields and topic tags
Study frame: Adult population AI literacy assessment. · Method signals: Psychometric Validation, Confirmatory Factor Analysis
Possible study pathways
One question, different levels
Postgraduate diploma
Designing organizational AI literacy training programs.
Professional master’s / MBA
Strategic assessment of AI readiness and workforce skill gaps.
Doctoral
Exploring the socio-technical implications of AI literacy on societal equity.
- originality
- Moderate
- methodology
- Advanced
- Data access
- Moderate
- ethics
- Accessible
Quality and audit
91/100 quality
Safeguard-based audit was not recorded under the prior standard.
- High practical utility for policy makers.
- Robust psychometric methodology.
- Open-access scholarly source and DOI metadata verified
This older record can be checked against the current standard on request. It changes only if every academic gate and independent safeguard passes.
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Research Idea curation
- DOI and bibliographic metadata independently resolved
- Open-access status verified
- The research direction is transparently marked as AI-inferred
The public contributor code contains no name or account email.
APA 7 source
Soto-Sanfiel, M. T., Angulo-Brunet, A., & Lutz, C. (2025). The scale of artificial intelligence literacy for all (SAIL4ALL): assessing knowledge of artificial intelligence in all adult populations. Humanities and Social Sciences Communications, 12(1), Article 1618. https://doi.org/10.1057/s41599-025-05978-3
Paper abstract and discussion context; AI-inferred direction
This is an AI-inferred direction. The source authors did not state it as a recommendation.
Open source ↗