Why we chose AI fluency over AI literacy

When I taught digital literacy many years ago, the focus was less on the mechanics of using digital tools and more on how to engage critically with the information those tools produced.

A web search did require knowing how to formulate an effective query. More importantly, however, it was about finding relevant information, evaluating its credibility, assessing the quality of the evidence, and understanding where that information came from.

As AI becomes increasingly embedded in our workflows, I've noticed a shift in terminology. AI literacy is often used to describe the ability to use AI tools effectively, while AI fluency increasingly refers to the ability to interrogate their outputs: questioning assumptions, testing reasoning, identifying gaps, and evaluating the strength of evidence.

The distinction between literacy and fluency is, in this case, largely semantic. However, it did influence how we approached the design of one of our latest courses. We chose the term 〰️AI fluency〰️ because the course is not primarily about tool usage. Rather, it is about developing the capacity to critically evaluate the increasingly polished and confident-looking outputs that AI systems generate.