ai-literacy

A designed program still has to be delivered, kept current as tools change, and documented well enough to show what you did. This lesson covers delivery formats and why attaching training to tool rollout beats annual campaigns, measurement that is useful rather than required, the records that constitute evidence, refresh triggers, and an honest account of what an AI literacy program cannot fix.

A single company-wide e-learning module satisfies nobody and teaches almost no one. This lesson turns the AI Act's own factors into a design method: inventory the AI systems actually in use, segment the population by what they do with them, and build a layered curriculum from a universal baseline through role-specific modules to high-risk operator training, with the content that belongs in each.

AI literacy has a legal definition in the EU AI Act, and the obligation attached to it changed in 2026. This lesson covers Article 3(56), the original Article 4 duty to ensure a sufficient level of literacy, how the Digital Omnibus reframed it as an obligation of effort rather than result, who is bound, which AI systems are in scope, and the adjacent duties that remain hard requirements.

Using AI honestly protects two things: your standing at school and, more importantly, your own learning. Learn how academic-integrity rules apply to AI, why cheating with AI mainly cheats yourself, how to avoid the dependency trap, why verifying AI matters for learning, and how to build honest habits that make AI a genuine advantage rather than a hidden weakness.

Students now have AI too, and that changes teaching. Learn why AI detectors are unreliable, how to design assessments that stay meaningful when AI is everywhere, how to teach AI literacy as a core skill, and how to handle student data privacy and equity, so AI becomes something you teach students to use well rather than a threat to police.