Building AI Literacy for Staff and Students

Literacy is the pillar that turns AI from magic into a machine with known limits. A machine with known limits can be supervised. This module gives you the core ideas staff and students both need, and the order to teach them in.

What a language model actually does

Here is the one sentence, and it is worth teaching verbatim: a language model predicts the most likely next piece of text, based on patterns in the data it was trained on. It does not look anything up, it does not know facts, and it has no sense of true or false. Once a staff room holds that sentence, half the confusion clears. The tool is not lying when it invents a citation. It is doing exactly what it was built to do, which is produce plausible text.

The three limits every user must hold

• Confident errors. The model will state a wrong answer in the same fluent, assured tone it uses for a right one. Confidence is not a signal of accuracy.
• Invented sources. Ask for references and it will often produce citations that look perfect and do not exist. Every source must be checked against the real thing.
• Inherited bias. The model reflects the data it learned from, biases included. It is not neutral, and treating it as neutral is its own kind of error.

Teach the adults first

Sequence matters. Teachers cannot supervise what they have never tried, so staff literacy comes before student literacy, always. Run two or three short, hands-on sessions where staff use the tools on their own real tasks: planning a lesson, marking a sample, building a quiz. Confidence comes from use, not from a briefing slide.

Sources for this module