A common mistake in rolling out AI literacy across a college is using the same module for every department. What matters to a computer science student is often irrelevant to a commerce or arts student — and vice versa.
Start from the department's actual tools
Commerce students should learn AI in the context of financial analysis and market research. Arts students should learn it in the context of research and creative work. Generic "how AI works" content doesn't stick without that anchor.
Skip the technical internals
Non-technical departments don't need to understand transformer architecture. They need to understand capabilities, limitations, and responsible use — the practical layer, not the engineering layer.
Include department-specific ethical questions
The ethical questions in journalism (attribution, authenticity) are different from the ethical questions in finance (bias in AI-assisted decisions). Generic AI ethics content misses both.
Bring in practitioners, not just materials
A guest session from someone actually using AI in that field — a working analyst, a working writer — lands harder than any slide deck, because students see it applied by someone in the career they're aiming for.