Equipment Needed
- Windows PC with browser (to show your Teachable Machine model)
- Your completed product from Lesson 5
Key Vocabulary
Starter Activity (5 minutes)
Quick fire: Without looking back, write down as many key words from this unit as you can remember in 2 minutes.
Target words: machine learning, model, class/label, training data, testing data, accuracy, prediction, bias, fairness, overfitting, confidence, balanced dataset. Award points for each correct keyword.
Main Tasks
Part A: Showcase Your Product (20 minutes)
Presentation Guide
Present your Lesson 5 product to your partner or the class. Cover these points:
- Product name and what problem it solves
- Who is the target user?
- Demo: Show your model working on Teachable Machine
- Accuracy: How well does it work? Show your results.
- Improvements: What would you change if you had more time?
After presenting, get feedback from your partner or the class:
Organisation options:
- Pairs: Students present to a partner, then swap. Each gives 2 stars and a wish.
- Gallery walk: Students set up their model on screen, class walks around and tries each one.
- Whole class: Select 3–4 volunteers to present to everyone. Use a timer (3 mins each).
Part B: AI Mythbusters (15 minutes)
For each statement, decide: is it TRUE or FALSE? Write your answer and explain why.
| Statement | True or False? | Why? |
|---|---|---|
| "AI can think and feel like a human." | ||
| "AI is always fair and unbiased." | ||
| "AI learns from data — if the data is bad, the AI is bad." | ||
| "AI will replace all human jobs." | ||
| "AI needs humans to create and check it." |
Answers:
- FALSE — AI doesn't have feelings, consciousness, or understanding. It follows patterns in data.
- FALSE — AI can be biased if trained on biased data (as we saw in Lesson 2).
- TRUE — This is the key lesson from the unit. "Garbage in, garbage out."
- FALSE (mostly) — AI will change many jobs but not replace all of them. Jobs requiring creativity, empathy, and complex judgement are harder to automate.
- TRUE — Humans create AI, choose the training data, and need to check it works properly and fairly.
Part C: Unit Reflection
Looking Back at the Unit
Common Mistakes & Quick Fixes
- Presentation too short: Make sure you cover all 5 points in the presentation guide above.
- Can't remember model results: Check your Lesson 5 page — your answers are saved!
- Mythbusters too easy: Discuss in pairs first, then as a class. Encourage debate!
Challenge / Stretch Task
Write a short paragraph (4–5 sentences) explaining to a Year 6 student what machine learning is and why understanding bias matters. Use simple language they would understand.
Exit Ticket
Expected answers:
- Examples: medical diagnosis, sorting recycling, helping blind people navigate, detecting wildfires.
- Examples: bias/discrimination, privacy concerns, job losses, deepfakes, over-reliance on technology.
- Because AI can be biased, make mistakes, or be used in harmful ways. Humans need to make sure AI is fair, accurate, and used responsibly.
- Self-assessment — look for students who can justify their rating.