Session 3 — Teach the Computer

Duration: 60 min · Format: live online · Ages: 8–11

Session goal: by the end, students can train their own AI to tell two things apart, test it and read its confidence, and explain why more examples make it smarter.

Before class — prep (5 min)

• Open Teachable Machine in a tab — you'll demo it and screen-share.
• Have the diagram below ready to share on screen (collect → train → predict).
• Check that students have a computer with a webcam and a grown-up nearby for the first go.
• Decide the two poses you'll model in your demo (e.g. thumbs up and thumbs down).

Agenda

0:00 · Hook (5 min)

Ask the class to recall the learning recipe from Session 1: Examples → Learn → Guess.

• Take a few answers, then reveal the twist: today they stop using AI and start making it.
• Tell them they'll do all three steps themselves with a free tool called Teachable Machine — they are the teacher now.

0:05 · Teach — How you train an AI (13 min)

Explain: you teach an AI the same way you'd teach a puppy — with lots of examples and clear labels.

Share this diagram:

Walk through the three steps out loud:

1. Collect examples — show the AI many pictures of each class (for example, "thumbs up" and "thumbs down").
2. Train — press one button. The AI studies the examples and finds the patterns.
3. Predict — show something new. The AI guesses and tells you how sure it is with a bar (like "thumbs up: 92%").

Key point to land — confidence: the bar shows how sure the AI is. A short bar means it's not sure. More and clearer examples make the bars taller and the guesses better.

⚠ Watch for this: students think more examples of one class is enough. Each class needs plenty of varied examples, or the model just learns the background instead of the pose.

Ask: "If your model keeps guessing wrong, what's the easiest fix?" (Answer: add more and clearer examples for each class.)

0:18 · Activity — Train your first AI (30 min)

Demo first, then have students follow along on their own devices (a grown-up nearby for the first go).

1. Open Teachable Machine → click Get Started → Image Project → Standard image model.
2. Class 1: name it (e.g. Thumbs up). Hold the pose and click Hold to Record to capture 20–30 pictures.
3. Class 2: name it (e.g. Thumbs down) and record 20–30 pictures.
4. Click Train Model. Wait a few seconds.
5. Test it live: move in front of the camera and watch the confidence bars change.

Then have students try to trick it: tilt the hand, change the background, stand further away. When does it get confused? That tells them what to fix.

• Circulate/watch for: students who recorded all their examples from one angle or with a busy background — nudge them to re-record with more variety and re-train.
• Watch for: classes with very different numbers of pictures — encourage a roughly even amount for each.

0:48 · Check for understanding (7 min)

Ask these aloud or drop them in the chat. Answer key (for you):

1. What are the three steps to make an AI? → Collect examples → Train → Predict.
2. What does "confidence" mean? → How sure the AI is about its guess, shown as a percentage or a bar.
3. Your model keeps getting it wrong. What's the easiest fix? → Add more and clearer examples for each class (different angles, good lighting).

0:55 · Wrap-up + homework (5 min)

• Ask one student to explain in their own words: why do more examples make the AI smarter?
• Homework: add a 3rd class to the model (like Nothing — an empty hand), re-train, and see if it works better. Write down one time it failed and an idea to fix it. Bring it to Session 4.

Teaching notes

• Correct this misconception: "the AI is thinking / recognising like me." It's matching new pictures against the patterns in the examples it was given — nothing more.
• Fast finishers (extension): go multi-class — make a model with 3 or 4 classes (e.g. fist, open hand, peace sign) and notice how confidence spreads out. Then try first code: open Google Colab → New notebook → type this in a cell and press run:

python print("Hello! I am training my first AI.") for i in range(1, 4): print("Example number", i) That's real Python — the language AI is built with. Challenge: write one sentence on what their classifier could be useful for in real life. - Low-tech fallback: if some devices have no webcam, screen-share your own model and have those students direct your recording ("more from the side!"), then run the trick-it test together.

Vocabulary

Resources

• Teachable Machine — the tool used in this session.
• Teachable Machine — official tutorials — short how-to guides.
• Machine Learning for Kids — train models and use them in Scratch (great for extension).
• Google Colab — run first Python for the extension task.

For grown-ups: in Teachable Machine's basic mode no images leave the computer — a safe first project.

Next session

Session 4 — Your First AI Project: students plan, build, test, and present their very own classifier.