
Session 1 · DESIGN: Improve
Teach a machine
- 10 minHow AI learns: it looks at many labelled examples and finds patterns. Show how Spyfish counts from volunteers train DOC's fish AI.
- 20 minTrain Teachable Machine to tell two objects apart (harakeke leaf and kawakawa leaf, or can and bottle). Use at least 30 photos of each.
- 15 minBreak it on purpose: train a second model with photos from only one background or one angle. Test both. Which is fooled, and why?
- 5 minRecord: what did the training data leave out, and who could that be unfair to if this were a real app?
Session 2 · DESIGN: Improve
Is AI right for this job?
- 15 minYour teacher asks an AI chatbot three questions about your topic on the projector. Fact-check each answer against a trusted source. Was it right, partly right or made up?
- 20 minAI suitability card: for your context, score an AI tool on accuracy, fairness and context. Should it count birds, fish or people? What must a person still do?
- 10 minMisinformation check: look at a fake-looking post about your topic. Use the three questions: who made it, what is the evidence, what do other sources say?
- 5 minImprove your data story plan using what you learned.
Getting started
Train Teachable Machine with only two classes and a plain background first.
Stretch
Play Code.org AI for Oceans and explain how the AI's training changes when you choose which examples to show it.
Checkpoint
A tested model with a written note on its blind spot, and a draft AI suitability card.
Activities for this week
- Teach a machine, then fool it: How does an AI learn from examples, and what happens when the examples are unfair?
- AI or not? The suitability card game: When should we use AI for a job, and when should a person do it?
Useful this week
- Artificial intelligence ↗ A clear introduction to how AI and machine learning work, with New Zealand examples.
- Teachable Machine ↗ Train your own image model in the browser and see how training data shapes the result.
- AI for Oceans ↗ Train an AI to sort fish from rubbish and discover how bias creeps in.