In short
A knowledge test about AI for any adult, with right and wrong answers. It has four parts you can use separately. Use it to find out what staff know, not what they think they know.
For whoever runs the assessment: an HR or operations lead, an internal project team, or an outside consultant.
What it measures
What a person knows about AI, in four parts:
- what AI is;
- what AI can do;
- how AI works;
- how AI should be used.
Because the answers are right or wrong, the score does not depend on how confident the person feels. That matters: most AI-literacy questionnaires are self-ratings, and self-ratings and test results differ.
How to use it
- Pick the parts that match what staff need for their role. All 56 questions is a lot for a workplace.
- Keep it anonymous and low-stakes. A test that feels like an exam raises anxiety and drop-out.
- Report results by team, never by person.
- Set the result beside a self-rating. The gap shows who is overconfident.
- Test again after training.
See test AI knowledge instead of asking staff to rate their own.
What it does not measure
- Skill in a particular job. It tests general knowledge about AI, not whether an accountant or a planner can use it well.
- Attitudes or confidence.
- Up-to-date facts for long. Questions about what AI can do go out of date faster than questions about attitudes.
How it was tested
The authors tested it on three samples of adults in the United Kingdom. The four parts appeared as intended. The test also worked the same way for men and women and for people with different levels of education, so scores can be compared across those groups.
Licence and access
The journal version is open access under CC BY 4.0. When you translate true-or-false questions, check that each answer is still clearly right or wrong in the new language.
Alternatives
Other assessments with a similar job, and when to prefer each.
- What do staff know about AI, including generative AI? AI Competency Objective Scale (AICOS) Prefer it when you want questions on generative AI, or a shorter test. Its short version is still preliminary. Staff, each taking a test · A full and a short version. The number of questions is not established Free to use and adapt
- How do staff rate their own AI skills and their confidence to learn? Meta AI Literacy Scale (MAILS) Prefer it when you want a self-rating to set beside the test, including how confident people feel about learning AI. Staff, each rating their own skills · 34 questions, or 10 in the short version Free to use, ask before changing
Practices that use it
- Train staff on the tools you actually chose Show every user what your chosen AI tool is, where it goes wrong, and what they must check. Use your own tasks and your own rules, give people time to practise, and include temporary staff. Do first · Effort: medium (weeks, a few people)
- Test AI knowledge instead of asking staff to rate their own People judge their own skills poorly. To find out what your staff know about AI, use a short knowledge test or a second opinion, not only a self-rating. Do next · Effort: medium (weeks, a few people)
The details
- Testing
- Tested by its authors on three samples in the United Kingdom. The structure held, and the test worked the same way for men and women and across education levels. No independent team or other country has tested it yet.
- Looks at
- Person
- How the answers are collected
- A knowledge test. Answers are true or false, or on a five-point scale.
- Length
- 56 questions in 4 parts. Each part can be used on its own.
- Cost
- Free
- Licence
- CC BY 4.0You may translate and adapt the published version if you credit the authors. Use the 2025 journal version, not the earlier preprint, which had a stricter licence.
- Languages
- English
- By
- Soto-Sanfiel, Angulo-Brunet and Lutz, 2025
Sources
- Soto-Sanfiel, Angulo-Brunet and Lutz (2025). The scale of artificial intelligence literacy for all (SAIL4ALL): assessing knowledge of artificial intelligence in all adult populations. Humanities and Social Sciences Communications. doi:10.1057/s41599-025-05978-3
Related studies on this site
- A review of 16 questionnaires that measure AI knowledge Lintner (2024). A systematic review of AI literacy scales. npj Science of Learning, 9, 50.
- Self-rated digital skills versus test results in 629 students Luna, Ocaña, Ortiz-Colón and Rodríguez Moreno (2026). From Self-Perception to Reality in Digital Competence: Findings from the DigComp 2.2 Framework. The Electronic Journal of e-Learning, 24(3), 137–153.
- Knowing how AI works improves decisions about what to hand over Pinski, Adam and Benlian (2023). AI Knowledge: Improving AI Delegation through Human Enablement. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems.