AI Literacy in the Canadian Curriculum: What’s Missing?

I’ve been looking at provincial curricula across Canada to see how AI literacy is being addressed, and honestly, there are significant gaps.

Most provincial curricula include digital literacy as a competency, but “digital literacy” as currently defined was written before generative AI became mainstream. Knowing how to use a search engine or evaluate a website is important, but it doesn’t prepare students for the world of AI-generated content, deepfakes, and automated decision-making.

What’s missing specifically:

Understanding how AI models work at a conceptual level. Students don’t need to understand transformer architecture, but they should understand that AI generates text by predicting probable next words, not by “understanding” or “knowing” things. This misconception leads to over-trust in AI outputs.

Critical evaluation of AI-generated content. Students need practice identifying when AI is wrong, biased, or fabricating information. Current curricula don’t address this systematically.

Ethics of AI use. Beyond “don’t cheat with ChatGPT,” students need to think about algorithmic bias, privacy implications, environmental costs of AI, and the societal impact of automation.

Practical AI skills. Like it or not, many jobs will require working alongside AI tools. Students who learn to use AI effectively, including knowing its limitations, will have a professional advantage.

The Canadian AI ecosystem. Canada has a strong AI research sector (Montreal, Toronto, Edmonton). Students should know about the AI industry in their own country.

Some individual teachers are incorporating AI literacy into their classes, but it’s ad hoc and depends on the teacher’s own knowledge and interest. We need systematic curriculum integration.

What AI literacy topics do you think are most urgent to add to the curriculum?