Why it works
Generative AI predicts likely text rather than retrieving verified facts, which is why it can be fluent and confidently wrong at once. Understanding this, along with bias, privacy, and the limits of AI detectors, lets teachers use these tools where they genuinely help and avoid where they do harm.
The research: ISTE guidance on AI in education; work on AI literacy for educators.
The run-of-show
Choose your slot. The agenda, timings, and length update to match.
The core activity: build your mental model and a use-or-avoid list
Teachers leave with a plain-language model of AI and a short list of where to use it and where not to.
- Capture, in plain language, how generative AI works: it predicts likely text, not facts.
- List two or three things AI is genuinely good at for your work, and two or three where it is risky.
- Note the privacy and bias cautions that apply in your setting.
- Decide one low-risk way you will try it.
Fluent is not the same as correctAI's biggest trap is that wrong answers sound as confident as right ones. A teacher who knows this checks the output; a student who does not, does not.
Facilitator notes
- Give teachers a plain-language model, not jargon.
- Let them try a tool and deliberately catch an error.
- Name privacy and bias plainly, and connect to school policy.
Do not oversell or dismiss itThe two unhelpful reactions are hype and refusal. Aim for informed judgment: real uses, real limits, real cautions.
Adapt it
- <b>Elementary:</b> focus on teacher use and media literacy for students.
- <b>Secondary:</b> student AI literacy becomes central.
- <b>All settings:</b> anchor in privacy and in verifying outputs.
Participant handout
One page for every teacher. Print it, or save it as a PDF.
K12 Academics · Professional Learning
AI Literacy: model it, use it, check it
<b>A working understanding:</b> how AI works, what it is good and bad at, and the cautions that apply.
- How AI works, in my own words:
- Two or three things it is good at for me:
- Two or three where it is risky:
- The privacy and bias cautions here:
- One low-risk way I will try it:
Make it stick
Literacy before tools:
- Build AI literacy before adopting AI tools.
- Check outputs, always.
- Bring one good use and one caught error to the next PLC.
Common questions
Do I need to be technical to understand AI?
No. You need a working mental model: these tools generate likely-sounding text, they do not know what is true, and they reflect their training data. That is enough to decide well.
Why does AI sometimes make things up?
Because it predicts plausible text rather than looking up facts. Confident, fluent, and wrong is a normal failure mode, which is why human checking matters.
Isn't AI literacy just for computer science teachers?
No. Every teacher and student now makes decisions involving AI, so AI literacy is a general capability, not a specialist topic.