AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 697 · 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.🤖 AI or a normal program: which one “does exactly as coded”? (a) a normal program (b) AI
2. 2.🤖 AI or a normal program: which one “makes a best guess”? (a) a normal program (b) AI
3. 3.True or false: “AI has real feelings” is a myth about AI. (a) False (b) True
4. 4.🤖 AI or a normal program: which one “one set rule per case”? (a) a normal program (b) AI
5. 5.True or false: “naming a new leaf” is the model. (a) False (b) True
6. 6.🤖 AI or a normal program: which one “learns from examples”? (a) AI (b) a normal program
7. 7.🔍 Which is the odd one out? (a) ask different people (b) add more of the same (c) copy old unfair choices (d) use one group only
8. 8.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
9. 9.Which of these helps reduce bias? (a) use one group only (b) test on one group only (c) ignore complaints (d) add varied examples
10. 10.🧮 Is a basic calculator an AI system? (a) yes, it can think (b) no, it follows rules (c) yes, it is smart (d) only at night
11. 11.True or false: an AI tool making a brand-new tune is AI predicting something. (a) True (b) False
12. 12.True or false: “improves with more data” describes how AI works. (a) False (b) True
13. 13.🤖 Does an AI system think and feel like a person? (a) yes, just like us (b) only on weekends (c) yes, it gets sad (d) no, it finds patterns
14. 14.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
15. 15.🔍 Which is the odd one out? (a) skip testing (b) balance the data (c) fix wrong labels (d) ask different people
16. 16.🏠 AI or not AI: a microwave timer? (a) not AI (b) uses AI
17. 17.🏠 AI or not AI: a calculator? (a) uses AI (b) not AI
18. 18.✨ Generative or not: spotting a sick leaf? (a) sorts or predicts (b) makes something new
19. 19.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) few child voices in data (c) it was a rainy day (d) children speak too fast
20. 20.🛠️ Does this reduce bias or add to it: balance the data? (a) reduces bias (b) adds to bias
21. 21.🔍 Which is the odd one out? (a) AI can make up facts (b) AI can be wrong (c) AI always checks facts (d) AI learns from data
22. 22.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
23. 23.🔍 Which is the odd one out? (a) sorting photos by face (b) writing a new poem (c) creating a cartoon (d) writing quiz questions
24. 24.🔍 Which is the odd one out? (a) a calculator (b) a voice assistant (c) a microwave timer (d) a torch
25. 25.🌍 An AI chatbot says the Earth has two moons. Your textbook says one. What now? (a) trust the textbook (b) stop studying (c) share it online (d) trust the AI
26. 26.✨ Generative or not: unlocking with a face? (a) makes something new (b) sorts or predicts
27. 27.📱 What is the AI doing here: a car camera reading a speed sign? (a) recommending (b) recognising (c) generating (d) predicting
28. 28.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
29. 29.✅ Is this a real check of an AI answer: redo the maths yourself? (a) not a real check (b) a real check
30. 30.🛠️ Which does this job need: spotting spam emails by their patterns? (a) a simple rule is enough (b) needs AI (learning)
31. 31.True or false: “ask different people” helps reduce bias. (a) True (b) False
32. 32.🔍 Which is the odd one out? (a) only one kind of example (b) correct labels (c) clear, sharp examples (d) examples of every type
33. 33.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) photos in many lights (d) testing on training data
34. 34.🔁 What is the right order? (a) data, prediction, model (b) prediction, data, model (c) data, model, prediction (d) model, prediction, data
35. 35.Which of these is bad for training a model? (a) clear, sharp examples (b) photos in many lights (c) checking labels twice (d) testing on training data
36. 36.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
37. 37.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) True (b) False
38. 38.Which of these is a fact about AI? (a) AI is never wrong (b) AI knows everything (c) AI always checks facts (d) AI can make up facts
39. 39.🤔 An AI answer sounds very confident. Does that mean it is right? (a) no, check it anyway (b) yes, if it is long (c) yes, always
40. 40.Which of these is bad for training a model? (a) checking labels twice (b) very few examples (c) correct labels (d) photos in many lights

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