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Over the past few months, I’ve noticed something consistent in conversations about AI and education across schools, policy discussions, and product spaces.
Everyone agrees AI literacy matters but very few people agree on what it actually means. That’s because we’ve been using the same phrase to describe very different things. Below is a distinction that has helped me make sense of the confusion.
Three types of AI literacy
Most conversations about AI literacy collapse these into one even though they’re not the same. Treating them as interchangeable only creates real gaps in learning.
1. Functional AI literacy (most visible)
Knowing what AI tools exist
Understanding basic concepts like data, models, and prompts
Being able to use systems effectively
This literacy answers the question: “How does this work?”
2. Critical AI literacy (less visible, most important)
Understanding how systems are trained
Recognizing bias, limitations, and tradeoffs
Questioning outputs rather than accepting them
This literacy answers the question: “What is this system actually doing?”
3. Human AI literacy (least discussed, hardest to teach)
Judgment about when to use AI and when not to
Awareness of how AI shapes thinking and behavior
A sense of agency rather than dependence
This literacy answers the question: “What role should this play in my thinking and decisions?”
Why this distinction matters
When schools focus only on functional literacy, students learn speed without judgment. When critical literacy is added, students learn skepticism. But without human literacy, students never fully learn how to responsibly situate AI within their own thinking. AI literacy isn’t one skill, but a progression, and confusing the categories makes it harder to design learning experiences that actually help students grow and aptly prepare them for an AI-shaped future.
A question I’m sitting with
If students leave school knowing how to use AI, but not how to judge it, question it, or place boundaries around it, what have we actually prepared them for?
That question is shaping how I think about FutureSkills, and I’ll continue exploring it here.
If you found this distinction useful, I’d love to know where it resonated or where it raised questions. Those responses will help guide what I explore next.