There is a growing consensus that AI literacy should be part of every student’s education. But there is very little consensus on what AI literacy actually means — and the default assumption is dangerously wrong.
The default assumption is that AI literacy means learning to code, or learning how machine learning algorithms work, or learning to use generative AI tools. None of these, alone or together, constitute AI literacy.
What AI Literacy Actually Requires
AI literacy is the capacity to understand, evaluate, and make informed decisions about AI systems — as a citizen, a professional, and a leader. It encompasses:
- Understanding how AI systems make decisions and where they fail
- Recognising bias in datasets, algorithms, and outputs
- Evaluating the social, economic, and political implications of AI deployment
- Making governance decisions about AI adoption in professional contexts
This is not computer science. It is critical thinking applied to the most consequential technology of our era.
The Curriculum Gap
Most education systems are responding to the AI moment by adding coding modules or approving the use of ChatGPT in assignments. Neither approach constitutes a serious response to the governance, ethical, and strategic challenges that AI presents.
What is needed is a cross-disciplinary approach that embeds AI literacy across subjects — not as a technical skill, but as a critical competency.