AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 730 · 40 questions

AI tip (Name the idea, then answer): Learning from examples → AI; fixed rules → a normal program. Order: training data → model → prediction; good data is many, varied and correctly labelled. Generative AI makes new things and can hallucinate; one-sided data causes bias.

1. 1.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) a prediction (c) the model
2. 2.🔍 Which is the odd one out? (a) wrong labels (b) examples of every type (c) testing on training data (d) copies of one photo
3. 3.True or false: an AI chatbot writing a story about a lost kite is AI recommending something. (a) False (b) True
4. 4.Which of these is a prediction? (a) old emails marked spam (b) 500 labelled leaf photos (c) typing a new spoken word (d) what training builds
5. 5.True or false: “flagging a new email” is training data. (a) False (b) True
6. 6.Which of these is AI that sorts, spots or predicts (not generative)? (a) writing a new poem (b) sorting photos by face (c) making up a story (d) drawing a new picture
7. 7.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) slower to switch on (d) change colour
8. 8.🔍 Which is the odd one out? (a) sorting photos by face (b) writing quiz questions (c) unlocking with a face (d) spotting spam
9. 9.🧐 Fact or myth: “AI can make up facts”? (a) a fact (b) a myth
10. 10.🔍 Which is the odd one out? (a) sorting photos by face (b) spotting spam (c) drawing a new picture (d) reading number plates
11. 11.True or false: an AI chatbot writing a story about a lost kite is AI predicting something. (a) False (b) True
12. 12.⏩ In machine learning, which comes first: what training builds or 500 labelled leaf photos? (a) what training builds (b) 500 labelled leaf photos
13. 13.True or false: “flagging a new email” is the model. (a) True (b) False
14. 14.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
15. 15.🌍 An AI chatbot says the Earth has two moons. Your textbook says one. What now? (a) trust the textbook (b) share it online (c) stop studying (d) trust the AI
16. 16.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
17. 17.📱 What is the AI doing here: a book app suggesting a story like your last one? (a) generating (b) recommending (c) recognising (d) predicting
18. 18.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
19. 19.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) the right answer tag (d) a sticker on a laptop
20. 20.🔮 A trained model sees a new photo and says “mango”. This is… (a) a prediction (b) a label error (c) a password (d) training data
21. 21.True or false: “labelled fruit photos” is training data. (a) True (b) False
22. 22.True or false: “500 labelled leaf photos” is the model. (a) True (b) False
23. 23.📱 What is the AI doing here: an image generator drawing a robot from a sentence? (a) recognising (b) predicting (c) recommending (d) generating
24. 24.✨ Generative or not: predicting rain? (a) sorts or predicts (b) makes something new
25. 25.🧐 Fact or myth: “AI can be wrong”? (a) a fact (b) a myth
26. 26.True or false: “AI knows everything” is a myth about AI. (a) False (b) True
27. 27.🤖 What makes an AI system different from a normal program? (a) it uses electricity (b) it learns from examples (c) it is always right (d) it has a screen
28. 28.✨ Generative or not: spotting a sick leaf? (a) makes something new (b) sorts or predicts
29. 29.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
30. 30.🔍 Which is the odd one out? (a) a torch (b) a calculator (c) face unlock on a phone (d) a digital watch
31. 31.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) bias from data (d) a broken screen
32. 32.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
33. 33.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
34. 34.Which of these is the model? (a) typing a new spoken word (b) flagging a new email (c) naming a new leaf (d) the trained program
35. 35.Which of these describes how a normal program works? (a) spots patterns in data (b) gives a likely answer (c) learns from examples (d) does exactly as coded
36. 36.🤖 AI or a normal program: which one “learns from examples”? (a) a normal program (b) AI
37. 37.🛠️ Which does this job need: sorting names in A to Z order? (a) a simple rule is enough (b) needs AI (learning)
38. 38.✨ Is everything a generative AI makes true? (a) no, it can be wrong (b) yes, always (c) only on Mondays
39. 39.⏩ In machine learning, which comes first: flagging a new email or what training builds? (a) flagging a new email (b) what training builds
40. 40.🔍 Which is the odd one out? (a) AI can make up facts (b) AI has real feelings (c) AI knows everything (d) AI always checks facts

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