AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 842 · 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.Which of these describes how a normal program works? (a) never learns from data (b) handles unseen examples (c) makes a best guess (d) spots patterns in data
2. 2.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) its colour (d) the weather
3. 3.🧐 Fact or myth: “confident means correct”? (a) a myth (b) a fact
4. 4.🔍 Which is the odd one out? (a) ignore complaints (b) add more of the same (c) use one group only (d) test on many groups
5. 5.🏠 AI or not AI: a microwave timer? (a) not AI (b) uses AI
6. 6.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) one single photo (c) a song about dogs (d) many labelled photos
7. 7.Which of these describes how AI works? (a) improves with more data (b) does exactly as coded (c) follows fixed rules (d) same steps every time
8. 8.🏠 AI or not AI: a calculator? (a) uses AI (b) not AI
9. 9.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
10. 10.True or false: “uses rules people wrote” describes how AI works. (a) False (b) True
11. 11.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) ignore complaints (d) test on many groups
12. 12.✅ Is this a real check of an AI answer: ask your teacher? (a) not a real check (b) a real check
13. 13.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
14. 14.🤖 Does an AI system think and feel like a person? (a) only on weekends (b) no, it finds patterns (c) yes, just like us (d) yes, it gets sad
15. 15.Which of these is a myth about AI? (a) AI can be wrong (b) AI answers need checking (c) AI has real feelings (d) AI can make up facts
16. 16.True or false: recognising a friend’s face in photos needs AI that learns from examples. (a) False (b) True
17. 17.Which of these is not AI? (a) photos grouped by face (b) a doorbell (c) an image generator (d) an app translating signs
18. 18.🔍 Which is the odd one out? (a) AI has real feelings (b) AI learns from data (c) AI always checks facts (d) AI is never wrong
19. 19.🤖 AI or a normal program: which one “makes a best guess”? (a) AI (b) a normal program
20. 20.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
21. 21.Which of these adds to bias? (a) check who is missing (b) add varied examples (c) skip testing (d) fix wrong labels
22. 22.🏠 AI or not AI: a digital watch? (a) not AI (b) uses AI
23. 23.Which of these uses AI? (a) photos grouped by face (b) a fan regulator (c) a calculator (d) a microwave timer
24. 24.🎨 This prompt asks a generative AI for: a logo idea for the eco club. What kind of output is that? (a) music or sound (b) an image (c) text
25. 25.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) balanced data (b) one-sided data
26. 26.🔍 Which is the odd one out? (a) copy old unfair choices (b) use one group only (c) ask different people (d) add more of the same
27. 27.🔍 Which is the odd one out? (a) add more of the same (b) use one group only (c) copy old unfair choices (d) check who is missing
28. 28.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) a prediction (c) training data
29. 29.🤖 AI or a normal program: which one “gives a likely answer”? (a) a normal program (b) AI
30. 30.True or false: “examples of every type” is good for training a model. (a) True (b) False
31. 31.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
32. 32.Which of these is a prediction? (a) typing a new spoken word (b) what training builds (c) old emails marked spam (d) past weather records
33. 33.Which of these is a prediction? (a) recorded spoken words (b) the trained program (c) old emails marked spam (d) naming a new fruit photo
34. 34.🛠️ What helps make an AI system fairer? (a) more varied data (b) ignoring mistakes (c) one group only (d) fewer tests
35. 35.🧐 Fact or myth: “AI knows everything”? (a) a fact (b) a myth
36. 36.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) very few examples (d) new data for testing
37. 37.True or false: “spots patterns in data” describes how a normal program works. (a) True (b) False
38. 38.✍️ How does an AI chatbot write its replies? (a) uses magic (b) copies one website (c) asks a person (d) predicts likely words
39. 39.📱 What is the AI doing here: turning your spoken words into typed text? (a) recognising (b) generating (c) recommending (d) predicting
40. 40.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) training data (c) a prediction

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