AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 37 · 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 does this job need: recognising a bird from its song? (a) a simple rule is enough (b) needs AI (learning)
2. 2.Which of these is a myth about AI? (a) AI answers need checking (b) AI can be wrong (c) AI can make up facts (d) confident means correct
3. 3.🔍 Which is the odd one out? (a) blurry, unclear photos (b) only one kind of example (c) very few examples (d) new data for testing
4. 4.🔍 Which is the odd one out? (a) AI has real feelings (b) AI is never wrong (c) AI answers need checking (d) AI always checks facts
5. 5.True or false: “add varied examples” helps reduce bias. (a) True (b) False
6. 6.✨ Is everything a generative AI makes true? (a) yes, always (b) no, it can be wrong (c) only on Mondays
7. 7.🧪 Why test a model on NEW examples? (a) to make it slower (b) to delete its data (c) to change its name (d) to see if it works
8. 8.Which of these uses AI? (a) video suggestions (b) a doorbell (c) a microwave timer (d) a fan regulator
9. 9.Which of these is bad for training a model? (a) new data for testing (b) correct labels (c) many varied examples (d) blurry, unclear photos
10. 10.✅ Is this a real check of an AI answer: it sounds very sure? (a) not a real check (b) a real check
11. 11.✨ Generative or not: writing a new poem? (a) sorts or predicts (b) makes something new
12. 12.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) the model (c) a prediction
13. 13.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
14. 14.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
15. 15.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) a prediction (b) the model (c) training data
16. 16.⏩ In machine learning, which comes first: what training builds or naming a new leaf? (a) what training builds (b) naming a new leaf
17. 17.Which of these describes how AI works? (a) never learns from data (b) does exactly as coded (c) follows fixed rules (d) learns from examples
18. 18.🔁 What is the right order? (a) prediction, data, model (b) data, model, prediction (c) data, prediction, model (d) model, prediction, data
19. 19.True or false: “new data for testing” is good for training a model. (a) False (b) True
20. 20.✨ Generative or not: composing a new tune? (a) makes something new (b) sorts or predicts
21. 21.🤖 AI or a normal program: which one “does exactly as coded”? (a) AI (b) a normal program
22. 22.Which of these is a prediction? (a) recorded spoken words (b) what training builds (c) typing a new spoken word (d) old emails marked spam
23. 23.Which of these is a prediction? (a) past weather records (b) recorded spoken words (c) the trained program (d) flagging a new email
24. 24.🤖 AI or a normal program: which one “gives a likely answer”? (a) a normal program (b) AI
25. 25.True or false: “copies of one photo” is good for training a model. (a) False (b) True
26. 26.Which of these is a myth about AI? (a) AI can make up facts (b) AI is never wrong (c) AI can be wrong (d) AI learns from data
27. 27.🔍 Which is the odd one out? (a) inventing a recipe (b) drawing a new picture (c) writing a new poem (d) unlocking with a face
28. 28.True or false: “AI has real feelings” is a myth about AI. (a) True (b) False
29. 29.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
30. 30.✨ Generative or not: unlocking with a face? (a) makes something new (b) sorts or predicts
31. 31.True or false: “confident means correct” is a myth about AI. (a) False (b) True
32. 32.🔍 Which is the odd one out? (a) new data for testing (b) clear, sharp examples (c) very few examples (d) photos in many lights
33. 33.Which of these is bad for training a model? (a) checking labels twice (b) many varied examples (c) examples of every type (d) labels added at random
34. 34.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
35. 35.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
36. 36.Which of these is training data? (a) what training builds (b) past weather records (c) the trained program (d) flagging a new email
37. 37.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
38. 38.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it may get it wrong (d) it will turn yellow
39. 39.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
40. 40.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) ignore complaints (d) balance the data

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