AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 558 · 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.⏩ In machine learning, which comes first: tomorrow’s rain guess or the trained program? (a) tomorrow’s rain guess (b) the trained program
2. 2.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) print pages (d) learn from data
3. 3.Which of these is generative AI (it makes something new)? (a) turning speech to text (b) making up a story (c) sorting photos by face (d) suggesting a video
4. 4.True or false: “it uses big words” is NOT a real way to check an AI answer. (a) True (b) False
5. 5.🧐 Fact or myth: “AI copies data patterns”? (a) a myth (b) a fact
6. 6.🔍 Which is the odd one out? (a) AI can make up facts (b) AI can be wrong (c) AI has real feelings (d) AI copies data patterns
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
8. 8.Which of these helps reduce bias? (a) test on many groups (b) ignore complaints (c) use one group only (d) copy old unfair choices
9. 9.🌍 An AI chatbot says the Earth has two moons. Your textbook says one. What now? (a) trust the AI (b) share it online (c) stop studying (d) trust the textbook
10. 10.🧐 Fact or myth: “AI has real feelings”? (a) a fact (b) a myth
11. 11.Which of these is good for training a model? (a) labels added at random (b) correct labels (c) testing on training data (d) very few examples
12. 12.True or false: “labelled fruit photos” is training data. (a) False (b) True
13. 13.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) a prediction (b) training data (c) the model
14. 14.🤖 AI or a normal program: which one “does exactly as coded”? (a) a normal program (b) AI
15. 15.Which of these is the model? (a) flagging a new email (b) naming a new fruit photo (c) 500 labelled leaf photos (d) what training builds
16. 16.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) labels added at random (d) checking labels twice
17. 17.🔍 Which is the odd one out? (a) examples of every type (b) new data for testing (c) many varied examples (d) blurry, unclear photos
18. 18.Which of these helps reduce bias? (a) test on one group only (b) check who is missing (c) skip testing (d) copy old unfair choices
19. 19.🔍 Which is the odd one out? (a) AI can make up facts (b) AI always checks facts (c) AI has real feelings (d) AI is never wrong
20. 20.🔍 Which is the odd one out? (a) photos in many lights (b) new data for testing (c) correct labels (d) copies of one photo
21. 21.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
22. 22.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
23. 23.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
24. 24.✅ Is this a real check of an AI answer: it has a neat layout? (a) a real check (b) not a real check
25. 25.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
26. 26.🎨 This prompt asks a generative AI for: a summary of chapter 3. What kind of output is that? (a) an image (b) text (c) music or sound
27. 27.💬 What do we call the request you type to a generative AI? (a) a password (b) a label (c) a battery (d) a prompt
28. 28.✨ Is everything a generative AI makes true? (a) no, it can be wrong (b) only on Mondays (c) yes, always
29. 29.Which of these is a fact about AI? (a) AI is never wrong (b) confident means correct (c) AI can mix up numbers (d) AI knows everything
30. 30.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) one-sided data (b) balanced data
31. 31.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
32. 32.🔍 Which is the odd one out? (a) use one group only (b) fix wrong labels (c) test on many groups (d) check who is missing
33. 33.🛠️ Which does this job need: spotting spam emails by their patterns? (a) a simple rule is enough (b) needs AI (learning)
34. 34.🧐 Fact or myth: “AI always checks facts”? (a) a myth (b) a fact
35. 35.Which of these adds to bias? (a) check who is missing (b) add varied examples (c) balance the data (d) test on one group only
36. 36.⏩ In machine learning, which comes first: naming a new fruit photo or labelled fruit photos? (a) naming a new fruit photo (b) labelled fruit photos
37. 37.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
38. 38.📱 What is the AI doing here: a weather app guessing tomorrow’s rain? (a) predicting (b) recommending (c) generating (d) recognising
39. 39.Which of these describes how AI works? (a) one set rule per case (b) improves with more data (c) same steps every time (d) uses rules people wrote
40. 40.True or false: “AI always checks facts” is a myth about AI. (a) True (b) False

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