1 · Your capstone: a real project, done right
Welcome to your capstone — the big project where you put everything together. In F3 you learned what AI is (a pattern-learner, not magic, not alive), and across Y1–Y3 you practiced making with it carefully. Now you stop learning about AI and start doing something real with it: a science-fair project that uses AI honestly, from your first question all the way to standing up and presenting it.
Here's the big idea that shapes this whole course:
A science fair isn't about a flashy gadget. It's about asking a clear question, testing it fairly, and telling the truth about what you found — even when the answer surprises you.
That's exactly how real scientists work, and it's how real science fairs are judged. The Society for Science runs one of the biggest student science fairs in the world (the Regeneron International Science and Engineering Fair, or ISEF), and they expect students to "hold themselves to the highest ethical standards" — honesty and integrity come first, before any cool result.
What you'll build. By the end of this course you'll have a complete, fair-ready project:
| Piece | What it is |
|---|---|
| A question | Something you genuinely wonder, that AI can help test |
| Background research | What's already known — with real sources you cite |
| A hypothesis | Your testable, written-down guess |
| A plan | Steps, materials, and your data-ethics rules |
| The AI part | A model you train, or AI-assisted analysis you run carefully |
| Honest results | What happened, including the misses |
| A poster + talk | How you'll present and cite it |
The tool you'll mostly use is Google Teachable Machine — a free website where you can train a real AI model (to recognize images, sounds, or poses) right in your browser, with no coding. You give it examples, it finds the pattern — exactly the machine learning you learned about in F3. A grown-up should help you set it up and stay nearby.
Grown-up + safety note: Do this capstone with a parent, teacher, or mentor. They'll help you choose safe tools, get any permissions you need, and check your sources. You bring the curiosity; they help with the guardrails.
Think about it. In one sentence, why does "telling the truth about what you found" matter more than "getting the answer you hoped for"?
Sources
- Society for Science. Regeneron International Science and Engineering Fair (ISEF) — student research and scientific integrity. https://www.societyforscience.org/isef/
- Google. Teachable Machine — train image, sound, and pose models in your browser, no coding. https://teachablemachine.withgoogle.com/