AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 704 · 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.🏷️ 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
2. 2.Which of these is not AI? (a) face unlock on a phone (b) a digital watch (c) video suggestions (d) photos grouped by face
3. 3.✨ Generative or not: suggesting a video? (a) makes something new (b) sorts or predicts
4. 4.Which of these is good for training a model? (a) very few examples (b) copies of one photo (c) wrong labels (d) many varied examples
5. 5.🔍 Which is the odd one out? (a) drafting a letter (b) writing quiz questions (c) spotting a sick leaf (d) drawing a new picture
6. 6.🔍 Which is the odd one out? (a) test on many groups (b) check who is missing (c) add varied examples (d) copy old unfair choices
7. 7.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will shut down (d) it will be perfect
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
9. 9.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
10. 10.🛠️ Does this reduce bias or add to it: add varied examples? (a) reduces bias (b) adds to bias
11. 11.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
12. 12.True or false: a car camera reading a speed sign is AI generating something new. (a) False (b) True
13. 13.True or false: photos of people from only one age group is one-sided data (likely to make a biased model). (a) True (b) False
14. 14.🧐 Fact or myth: “AI has real feelings”? (a) a myth (b) a fact
15. 15.✅ Is this a real check of an AI answer: ask your teacher? (a) not a real check (b) a real check
16. 16.Which of these is a prediction? (a) past weather records (b) 500 labelled leaf photos (c) flagging a new email (d) the learned patterns
17. 17.True or false: “old emails marked spam” is a prediction. (a) False (b) True
18. 18.🏠 AI or not AI: a calculator? (a) not AI (b) uses AI
19. 19.🔍 Which is the odd one out? (a) a printed timetable (b) a voice assistant (c) a stopwatch (d) a torch
20. 20.True or false: “a poster of a rainforest” asks for an image. (a) True (b) False
21. 21.True or false: “follows fixed rules” describes how AI works. (a) True (b) False
22. 22.🔍 Which is the odd one out? (a) video suggestions (b) a doorbell (c) an image generator (d) a map predicting traffic
23. 23.True or false: “skip testing” helps reduce bias. (a) True (b) False
24. 24.🔁 What is the right order? (a) data, model, prediction (b) data, prediction, model (c) model, prediction, data (d) prediction, data, model
25. 25.True or false: turning spoken words into typed text only needs a simple fixed rule. (a) True (b) False
26. 26.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
27. 27.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
28. 28.True or false: “one set rule per case” describes how AI works. (a) False (b) True
29. 29.🔍 Which is the odd one out? (a) checking labels twice (b) correct labels (c) many varied examples (d) labels added at random
30. 30.📅 An AI gives a date for a history event. How can you check? (a) look in your textbook (b) pick a lucky number (c) ask it to repeat (d) guess
31. 31.🏷️ 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
32. 32.Which of these is a myth about AI? (a) AI copies data patterns (b) AI learns from data (c) AI can make up facts (d) AI is never wrong
33. 33.Which of these describes how a normal program works? (a) handles unseen examples (b) same steps every time (c) learns from examples (d) spots patterns in data
34. 34.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) labels added at random (d) clear, sharp examples
35. 35.Which of these is not AI? (a) a printed timetable (b) video suggestions (c) photos grouped by face (d) face unlock on a phone
36. 36.🧐 Fact or myth: “AI can mix up numbers”? (a) a fact (b) a myth
37. 37.True or false: “recorded spoken words” is training data. (a) True (b) False
38. 38.Which of these is generative AI (it makes something new)? (a) reading number plates (b) spotting a sick leaf (c) writing a new poem (d) predicting rain
39. 39.Which of these is training data? (a) recorded spoken words (b) naming a new leaf (c) tomorrow’s rain guess (d) the learned patterns
40. 40.🤖 AI or a normal program: which one “makes a best guess”? (a) a normal program (b) AI

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