04/21/2026
Critical AI literacy is not the same as AI literacy.
We spend a lot of energy teaching students how to use AI tools, how to write better prompts, how to evaluate outputs for accuracy, how to integrate AI into their workflows. That's AI literacy, and it's necessary. But critical AI literacy goes further than functional skills and safe use.
Critical AI literacy asks who built the tool, whose data trained it, who profits from its outputs, and whose voices got left out of the training set entirely. It asks students to think about power, not just productivity.
Roe, et al. and (2024) define it as the ability to critically engage with AI systems by understanding their "technical foundations, societal implications, and embedded power structures while recognising their limitations, biases, and broader social, environmental, and economic impacts."
Maha Bali (2023) breaks "critical" into three layers: critical thinking, critical pedagogy, and critique of harms.
I put together a short guide that lays all of this out for teachers. It includes a side-by-side table of AI literacy definitions next to critical AI literacy definitions so you can see where the two overlap and where they split.
The guide also includes an 8-dimension comparison framework and 24 classroom questions across six domains: output evaluation, bias awareness, thinking ownership, system understanding, ethical awareness, and strategic use.
The questions are designed to work across grade levels and subject areas, whenever students interact with AI.
Teaching students to prompt well without teaching them to question what they're prompting is training them to be consumers, not thinkers.
Link in the first comment!
