A3 How Machines Learn

1. Starter (5 min). Write rules for telling a cat from a dog. Where do they break?
2. Teach (10 min). A rule-based program checks fixed rules; a learning model finds patterns in many examples.
3. Activity (22 min). Sort mystery cards using made-up rules, then using examples, and compare which works better when something new appears.
4. Reflect (8 min). Which approach would you choose for spam email? Why?

Do-it-yourself version for children

1. Starter (5 min). A student who memorises answers versus one who understands: how can we tell?
2. Teach (10 min). Split the data: a training set to learn from, a test set to check.
3. Activity (22 min). Split a card deck into training and test sets, train a partner, then test only on the unseen cards.
4. Reflect (8 min). What would go wrong if we tested on the same cards we trained on?

The “Teach the machine” activity on the Try page uses exactly this idea: the machine is tested on the creatures you did not teach it.

Do-it-yourself version for children

1. Recap (5 min). Training and test data.
2. Demo (8 min). Choose a question (for example, “is this a drawing of a happy or a sad face?”) and gather examples from the class.
3. Build (25 min). Train the classifier with at least 20 examples per class, hold back 5 for testing, and record the results.
4. Reflect (7 min). Which examples confused it?

Do-it-yourself version for children

1. Starter (5 min). If 8 out of 10 test items are right, what is the accuracy? Is 80% good enough for a medical test?
2. Teach (10 min). Accuracy as a fraction; two kinds of mistake (a false alarm and a miss).
3. Activity (22 min). Record a tally of right, false alarm and miss for the classifier from Lesson 3, then improve the training set and retest.
4. Reflect (8 min). Which mistake is worse for a smoke alarm, a false alarm or a miss?

Do-it-yourself version for children

1. Starter (5 min). Two pictures, one real and one computer-made. Can you tell?
2. Teach (10 min). Computers can make convincing pictures, text and voices. Check the source, look for the original, compare with other sources, and ask a trusted adult.
3. Activity (22 min). Evidence detectives: examine pre-checked examples with a checklist (who made it, why, where else is it, does it feel too good or strange to be true?).
4. Reflect (8 min). Three rules for checking before you share.

Teachers choose the examples in advance so children do not meet harmful or distressing content.

Do-it-yourself version for children

1. Plan (8 min). A one-page template: what it does, how it was trained, how I tested it, where it fails.
2. Build (25 min). Complete the project and the one-pager.
3. Share (10 min). Peer review: “One thing it does well, one way it could fail.” Also ask: who checks it, and who is responsible if it is wrong?
4. Reflect (7 min). Badge and a class “wise AI user” charter.

Do-it-yourself version for children

1. Hook (7 min). Tell two short stories: AI that helps farms use less water, and AI that helps find people after a flood. Ask: who benefits, and who could be missed?
2. Teach (8 min). AI is used for good in the environment, health and aid. Good use also needs rules: someone is responsible for it, it must treat people fairly, and it should respect the values and needs of the community it serves.
3. Plan (22 min). In groups, plan an AI helper for one cause (saving water, finding lost pets, helping after a flood). On a planning sheet write: the data it learns from, one way it could be unfair or wrong, who checks it and whose values it should respect.
4. Share (8 min). Each group presents in 1 minute. The class asks: who is responsible if it goes wrong?

Tool check: use the Ask Wondi activity, then look the same fact up in a book. Which was quicker? Which was easier to check? Discuss when each tool is best.

Do-it-yourself version for children