AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 641 · 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.✅ Is this a real check of an AI answer: match it to the textbook? (a) not a real check (b) a real check
2. 2.🧐 Fact or myth: “confident means correct”? (a) a fact (b) a myth
3. 3.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
4. 4.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) its screen size (c) its colour (d) the weather
5. 5.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
6. 6.⏩ In machine learning, which comes first: what training builds or recorded spoken words? (a) what training builds (b) recorded spoken words
7. 7.🔮 A trained model sees a new photo and says “mango”. This is… (a) a prediction (b) a password (c) training data (d) a label error
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
9. 9.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
10. 10.🔍 Which is the odd one out? (a) AI answers need checking (b) AI copies data patterns (c) AI can make up facts (d) AI always checks facts
11. 11.🔍 Which is the odd one out? (a) making up a story (b) drafting a letter (c) creating a cartoon (d) spotting spam
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.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
14. 14.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
15. 15.True or false: “AI knows everything” is a myth about AI. (a) True (b) False
16. 16.Which of these helps reduce bias? (a) fix wrong labels (b) test on one group only (c) skip testing (d) ignore complaints
17. 17.✨ Generative or not: drawing a new picture? (a) sorts or predicts (b) makes something new
18. 18.Which of these is bad for training a model? (a) correct labels (b) new data for testing (c) blurry, unclear photos (d) many varied examples
19. 19.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
20. 20.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
21. 21.🔍 Which is the odd one out? (a) AI is never wrong (b) AI has real feelings (c) AI can mix up numbers (d) AI always checks facts
22. 22.🧐 Fact or myth: “AI copies data patterns”? (a) a myth (b) a fact
23. 23.Which of these describes how AI works? (a) follows fixed rules (b) one set rule per case (c) does exactly as coded (d) improves with more data
24. 24.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the price (c) a sticker on a laptop (d) the font size
25. 25.True or false: “never learns from data” describes how AI works. (a) False (b) True
26. 26.Which of these is a fact about AI? (a) AI knows everything (b) AI has real feelings (c) AI is never wrong (d) AI answers need checking
27. 27.📱 What is the AI doing here: an AI chatbot writing a poem about rain? (a) recognising (b) generating (c) recommending (d) predicting
28. 28.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
29. 29.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) the model (b) training data (c) a prediction
30. 30.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it may get it wrong (c) it will shut down (d) it will turn yellow
31. 31.Which of these adds to bias? (a) balance the data (b) test on many groups (c) ignore complaints (d) fix wrong labels
32. 32.🛠️ What helps make an AI system fairer? (a) one group only (b) more varied data (c) ignoring mistakes (d) fewer tests
33. 33.🔍 Which is the odd one out? (a) AI knows everything (b) AI always checks facts (c) confident means correct (d) AI answers need checking
34. 34.Which of these is generative AI (it makes something new)? (a) turning speech to text (b) composing a new tune (c) reading number plates (d) predicting rain
35. 35.🎨 An image generator makes a picture from… (a) your password (b) the weather (c) a text description (d) a phone number
36. 36.🔍 Which is the odd one out? (a) checking labels twice (b) examples of every type (c) testing on training data (d) clear, sharp examples
37. 37.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
38. 38.True or false: “the learned patterns” is a prediction. (a) False (b) True
39. 39.🔍 Which is the odd one out? (a) confident means correct (b) long answers are true (c) AI knows everything (d) AI copies data patterns
40. 40.Which of these is not AI? (a) face unlock on a phone (b) photos grouped by face (c) a calculator (d) a spam filter

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