AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 295 · 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: “recorded spoken words”? (a) training data (b) the model (c) a prediction
2. 2.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) children speak too fast (c) the phone was new (d) few child voices in data
3. 3.✅ Is this a real check of an AI answer: it sounds very sure? (a) a real check (b) not a real check
4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
5. 5.🛠️ Which does this job need: switching on a street light at 6 pm? (a) a simple rule is enough (b) needs AI (learning)
6. 6.True or false: turning spoken words into typed text only needs a simple fixed rule. (a) False (b) True
7. 7.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) a prediction (c) the model
8. 8.🤖 Who builds and trains AI systems? (a) nobody at all (b) people (c) the weather (d) the Moon
9. 9.Which of these describes how AI works? (a) does exactly as coded (b) gives a likely answer (c) uses rules people wrote (d) follows fixed rules
10. 10.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) fix wrong labels (d) ignore complaints
11. 11.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
12. 12.✅ Is this a real check of an AI answer: it is long and detailed? (a) a real check (b) not a real check
13. 13.🔍 Which is the odd one out? (a) AI can mix up numbers (b) AI learns from data (c) AI answers need checking (d) AI is never wrong
14. 14.Which of these is generative AI (it makes something new)? (a) suggesting a video (b) predicting rain (c) sorting photos by face (d) writing a new poem
15. 15.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
16. 16.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) training data (c) a prediction
17. 17.Which of these is generative AI (it makes something new)? (a) predicting rain (b) spotting a sick leaf (c) reading number plates (d) creating a cartoon
18. 18.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
19. 19.🤖 AI or a normal program: which one “makes a best guess”? (a) a normal program (b) AI
20. 20.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a broken screen (b) bias from data (c) a strong battery (d) a fair model
21. 21.True or false: “a story about a brave ant” asks for music or sound. (a) True (b) False
22. 22.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) examples of every type (d) blurry, unclear photos
23. 23.True or false: “a drum beat for a dance” asks for music or sound. (a) True (b) False
24. 24.Which of these describes how AI works? (a) does exactly as coded (b) never learns from data (c) one set rule per case (d) improves with more data
25. 25.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
26. 26.True or false: “typing a new spoken word” is a prediction. (a) True (b) False
27. 27.Which of these is a prediction? (a) naming a new fruit photo (b) 500 labelled leaf photos (c) old emails marked spam (d) recorded spoken words
28. 28.True or false: “testing on training data” is good for training a model. (a) False (b) True
29. 29.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
30. 30.🔁 What is the right order? (a) model, prediction, data (b) data, prediction, model (c) data, model, prediction (d) prediction, data, model
31. 31.🏠 AI or not AI: a digital watch? (a) not AI (b) uses AI
32. 32.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
33. 33.Which of these is training data? (a) naming a new fruit photo (b) old emails marked spam (c) naming a new leaf (d) the trained program
34. 34.Which of these helps reduce bias? (a) fix wrong labels (b) ignore complaints (c) add more of the same (d) copy old unfair choices
35. 35.🏠 AI or not AI: video suggestions? (a) not AI (b) uses AI
36. 36.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) a prediction (b) the model (c) training data
37. 37.🔍 Which is the odd one out? (a) AI knows everything (b) confident means correct (c) AI can make up facts (d) AI always checks facts
38. 38.🔍 Which is the odd one out? (a) AI learns from data (b) AI always checks facts (c) AI is never wrong (d) long answers are true
39. 39.✅ Is this a real check of an AI answer: asking the same AI again? (a) not a real check (b) a real check
40. 40.True or false: “labelled fruit photos” is the model. (a) False (b) True

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