What is AI literacy?
AI literacy is the ability to understand, evaluate, use and govern AI systems appropriately and responsibly.
In more detail
AI literacy does not require evidence synthesists to become AI engineers. Instead, it means understanding what AI systems can and cannot do, how they are evaluated, what risks they introduce and how their outputs should be interpreted. AI literacy enables users to make informed decisions about adoption, oversight and reporting. As AI becomes increasingly embedded in evidence workflows, AI literacy is becoming a core professional competency.
Why it matters
Poor AI literacy increases the risk of overtrusting or misusing AI outputs.
Decision rule
Do not use an AI system if you cannot explain its purpose, limitations and risks.
Common misconceptions
“AI literacy means learning to code.”
“Only developers need AI literacy.”
At a glance
- Evidence strength
- Strong
- Stakeholders
- All stakeholders
- RAISE principle
- Responsible use requires informed users.
Related concepts
AI literacy is becoming a core skill for evidence professionals.