AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 660 · 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 makes an AI system different from a normal program? (a) it learns from examples (b) it has a screen (c) it uses electricity (d) it is always right
2. 2.💬 What do we call the request you type to a generative AI? (a) a password (b) a prompt (c) a label (d) a battery
3. 3.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) the model (c) training data
4. 4.⏩ In machine learning, which comes first: recorded spoken words or naming a new fruit photo? (a) recorded spoken words (b) naming a new fruit photo
5. 5.True or false: suggesting the next word as you type only needs a simple fixed rule. (a) False (b) True
6. 6.🔍 Which is the odd one out? (a) drafting a letter (b) drawing a new picture (c) turning speech to text (d) creating a cartoon
7. 7.Which of these is a prediction? (a) recorded spoken words (b) the trained program (c) old emails marked spam (d) naming a new leaf
8. 8.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
9. 9.🔍 Which is the odd one out? (a) AI knows everything (b) AI learns from data (c) AI can mix up numbers (d) AI answers need checking
10. 10.True or false: an image generator drawing a robot from a sentence is AI generating something new. (a) True (b) False
11. 11.🧐 Fact or myth: “AI has real feelings”? (a) a fact (b) a myth
12. 12.🧐 Fact or myth: “AI can make up facts”? (a) a myth (b) a fact
13. 13.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
14. 14.Which of these adds to bias? (a) add varied examples (b) ask different people (c) test on many groups (d) copy old unfair choices
15. 15.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
16. 16.🔍 Which is the odd one out? (a) suggesting a video (b) composing a new tune (c) reading number plates (d) spotting spam
17. 17.✍️ How does an AI chatbot write its replies? (a) asks a person (b) copies one website (c) uses magic (d) predicts likely words
18. 18.🧐 Fact or myth: “AI learns from data”? (a) a fact (b) a myth
19. 19.Which of these is AI that sorts, spots or predicts (not generative)? (a) predicting rain (b) drafting a letter (c) writing a new poem (d) composing a new tune
20. 20.🏠 AI or not AI: a doorbell? (a) uses AI (b) not AI
21. 21.🏠 AI or not AI: a light switch? (a) uses AI (b) not AI
22. 22.🤖 Does an AI system think and feel like a person? (a) yes, just like us (b) no, it finds patterns (c) only on weekends (d) yes, it gets sad
23. 23.🤖 AI or a normal program: which one “one set rule per case”? (a) AI (b) a normal program
24. 24.🏠 AI or not AI: a torch? (a) uses AI (b) not AI
25. 25.🛠️ Which does this job need: reading messy handwriting? (a) needs AI (learning) (b) a simple rule is enough
26. 26.True or false: sorting names in A to Z order needs AI that learns from examples. (a) True (b) False
27. 27.Which of these uses AI? (a) a torch (b) a microwave timer (c) a doorbell (d) a voice assistant
28. 28.📱 What is the AI doing here: a weather app guessing tomorrow’s rain? (a) recognising (b) predicting (c) generating (d) recommending
29. 29.📱 What is the AI doing here: an online shop showing “you may also like”? (a) generating (b) recognising (c) recommending (d) predicting
30. 30.🧮 Is a basic calculator an AI system? (a) only at night (b) no, it follows rules (c) yes, it can think (d) yes, it is smart
31. 31.🛠️ Which does this job need: finding 10% of a price? (a) a simple rule is enough (b) needs AI (learning)
32. 32.True or false: showing the time on a clock needs AI that learns from examples. (a) False (b) True
33. 33.Which of these is training data? (a) flagging a new email (b) naming a new fruit photo (c) old emails marked spam (d) tomorrow’s rain guess
34. 34.🔍 Which is the odd one out? (a) testing on training data (b) many varied examples (c) very few examples (d) only one kind of example
35. 35.🔍 Which is the odd one out? (a) AI can mix up numbers (b) AI is never wrong (c) AI can make up facts (d) AI copies data patterns
36. 36.True or false: “it is long and detailed” is NOT a real way to check an AI answer. (a) True (b) False
37. 37.🔍 Which is the odd one out? (a) test on many groups (b) add varied examples (c) use one group only (d) ask different people
38. 38.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a prediction (c) a password (d) training data
39. 39.Which of these is good for training a model? (a) blurry, unclear photos (b) clear, sharp examples (c) very few examples (d) copies of one photo
40. 40.⏩ In machine learning, which comes first: what training builds or flagging a new email? (a) what training builds (b) flagging a new email

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