AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 287 · 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.💬 What do we call the request you type to a generative AI? (a) a password (b) a label (c) a prompt (d) a battery
2. 2.📱 What is the AI doing here: a weather app guessing tomorrow’s rain? (a) generating (b) predicting (c) recommending (d) recognising
3. 3.Which of these is a prediction? (a) recorded spoken words (b) old emails marked spam (c) 500 labelled leaf photos (d) naming a new fruit photo
4. 4.📱 What is the AI doing here: a map app guessing how long a trip will take? (a) recommending (b) recognising (c) predicting (d) generating
5. 5.Which of these describes how a normal program works? (a) spots patterns in data (b) improves with more data (c) gives a likely answer (d) uses rules people wrote
6. 6.True or false: “balance the data” helps reduce bias. (a) True (b) False
7. 7.📚 An AI chatbot names a book that does not exist. This is called… (a) a backup (b) a password (c) a summary (d) a hallucination
8. 8.True or false: “does exactly as coded” describes how a normal program works. (a) True (b) False
9. 9.🧐 Fact or myth: “AI answers need checking”? (a) a fact (b) a myth
10. 10.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
11. 11.🔍 Which is the odd one out? (a) a map predicting traffic (b) a voice assistant (c) a fan regulator (d) an image generator
12. 12.🔍 Which is the odd one out? (a) examples of every type (b) testing on training data (c) only one kind of example (d) wrong labels
13. 13.🛠️ Which does this job need: suggesting the next word as you type? (a) a simple rule is enough (b) needs AI (learning)
14. 14.📚 Usually, more good examples make a model… (a) change colour (b) better at its task (c) slower to switch on (d) forget everything
15. 15.Which of these is not AI? (a) an image generator (b) a map predicting traffic (c) face unlock on a phone (d) a fan regulator
16. 16.🔍 Which is the odd one out? (a) drafting a letter (b) making up a story (c) writing a new poem (d) spotting spam
17. 17.True or false: “labelled fruit photos” is a prediction. (a) False (b) True
18. 18.True or false: “AI knows everything” is a fact about AI. (a) True (b) False
19. 19.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
20. 20.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a list of rules only (c) a song about dogs (d) many labelled photos
21. 21.🛠️ Which does this job need: recognising a friend’s face in photos? (a) a simple rule is enough (b) needs AI (learning)
22. 22.🏠 AI or not AI: photos grouped by face? (a) uses AI (b) not AI
23. 23.🔢 An AI chatbot gives a sum’s answer. What is the smart thing to do? (a) copy it straight away (b) ask it to hurry (c) check it on paper (d) never do maths
24. 24.True or false: a car camera reading a speed sign is AI recognising something. (a) True (b) False
25. 25.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) ignore complaints (d) balance the data
26. 26.True or false: “naming a new fruit photo” is training data. (a) True (b) False
27. 27.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) training data (b) a prediction (c) the model
28. 28.🛠️ Which does this job need: finding 10% of a price? (a) a simple rule is enough (b) needs AI (learning)
29. 29.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
30. 30.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
31. 31.✅ Is this a real check of an AI answer: find a trusted source? (a) a real check (b) not a real check
32. 32.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
33. 33.🤖 AI or a normal program: which one “handles unseen examples”? (a) a normal program (b) AI
34. 34.🏠 AI or not AI: a map predicting traffic? (a) not AI (b) uses AI
35. 35.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
36. 36.🔍 Which is the odd one out? (a) balance the data (b) check who is missing (c) test on many groups (d) test on one group only
37. 37.🔍 Which is the odd one out? (a) ignore complaints (b) fix wrong labels (c) test on many groups (d) balance the data
38. 38.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) training data (b) the model (c) a prediction
39. 39.True or false: “add more of the same” adds to bias. (a) False (b) True
40. 40.Which of these is bad for training a model? (a) examples of every type (b) new data for testing (c) very few examples (d) many varied examples

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