Session 4 — Your First AI Project

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

Session goal: by the end, students can plan, build, and test their own image classifier that solves a real problem, and present it with a one-page summary.

Before class — prep (5 min)

• Open Teachable Machine in a tab — students will build here and you'll demo.
• Have the diagram below ready to share on screen (the four project steps).
• Have a few example project ideas ready to suggest (recycle helper, ripe-fruit checker, toy sorter, tidy-desk checker).
• Ask students to have paper and a pencil for their one-page summary, plus a webcam-ready device.

Agenda

0:00 · Hook (5 min)

Ask the class: "You've learned what AI is, spotted patterns, and trained a model. Today you put it all together — what real thing would you want an AI to sort for you?"

• Take a few answers (chat or unmute).
• Reveal the mission: today each student builds a real AI project they can show someone.

0:05 · Teach — Your mission and the four steps (10 min)

Explain: the mission is to build an AI that sorts pictures into groups to help with a real task, then show it to someone.

Share this diagram:

Walk through the four steps out loud:

1. Collect — capture at least 20–30 pictures per class, with different angles, backgrounds, and lighting.
2. Train — click Train Model and wait a few seconds.
3. Test — show it new things; watch the confidence bars; find one it gets right and one it gets wrong.
4. Show — make a one-page summary and present it.

Help students pick an idea. Offer these and have each student write theirs down:

• Recycle helper: paper vs plastic
• Fruit checker: ripe vs not ripe
• My toys sorter: cars vs animals
• Tidy desk: tidy vs messy

⚠ Watch for over-ambition: students often pick groups that look almost identical or too many classes at once. Steer them to two clearly different groups for a first project.

Ask each student to finish the sentence: "My AI will sort __ into _ and ___."

0:15 · Activity — Build it: collect, train, test (28 min)

Have students open Teachable Machine → Image Project, make one class per group, and work through the steps. Demo each step on your shared screen first.

• Collect: aim for 20–30 pictures per class using different angles, backgrounds, and lighting — variety makes it smart.
• Train: click Train Model and wait a few seconds.
• Test: show it new things, watch the confidence bars, and find 1 thing it gets right and 1 thing it gets wrong. Add more examples of the tricky ones and re-train — is it better?

Use this checklist to track each student as you circulate:

• [ ] Chose an idea and wrote it down
• [ ] Collected 20+ examples for each class
• [ ] Trained the model
• [ ] Tested it on new things
• [ ] Improved a tricky class and re-trained

Circulate/watch for: models trained on one background only (they learn the background, not the object); classes with very uneven numbers of pictures; students who skip re-training after adding examples.

0:43 · Check for understanding (7 min)

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

1. What are the four project steps? → Collect → Train → Test → Show.
2. Your model gets a new picture wrong — what do you do? → Add more, varied examples of that tricky class and re-train.
3. Why use different angles, backgrounds, and lighting when collecting? → Variety makes the model smarter so it recognises the object, not just one background.

0:50 · Wrap-up + present + homework (10 min)

• Have students make a tiny one-page summary (draw or type): the project name, what it does (one sentence), the groups it sorts into, and one cool thing and one hard thing they learned.
• Invite 2–3 volunteers to present their project for 1 minute on camera.
• Homework: finish the one-page summary and present the project to family or the class. Well done — that completes Unit 1: students learned what AI is, how it spots patterns, and built and presented their own classifier.

Teaching notes

• Correct this misconception: "a bigger score means the AI really understands." Accuracy just means how often it matched the patterns in the examples — it doesn't understand the objects.
• Fast finishers (extension): collect a real dataset — instead of the webcam, take real photos (e.g. 30 of paper, 30 of plastic) and upload them. Be honest about accuracy: test 10 new items, count how many it gets right (e.g. 8/10 = 80%), and write the score down. Then write a mini project brief (3 lines): the problem, the data used, the result (accuracy), and one idea to improve it.
• Low-tech fallback: if some devices have no webcam, have those students plan on paper (idea, two groups, the clues that separate them) and pair with a webcam student to build one model together.

Vocabulary

Resources

• Teachable Machine — build and test the model.
• Teachable Machine — save & share — how to keep the project.
• Machine Learning for Kids — turn a model into a game (extension).

Next session

Unit 2 — Data & Problem-Solving: students become data explorers — collecting information, reading charts, and telling stories with data.