AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 536 · 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 of these is a myth about AI? (a) AI can mix up numbers (b) AI can be wrong (c) AI knows everything (d) AI answers need checking
2. 2.🏷️ 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
3. 3.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) False (b) True
4. 4.True or false: a car camera reading a speed sign is AI generating something new. (a) False (b) True
5. 5.🔢 An AI chatbot gives a sum’s answer. What is the smart thing to do? (a) copy it straight away (b) never do maths (c) ask it to hurry (d) check it on paper
6. 6.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) blurry, unclear photos (d) checking labels twice
7. 7.True or false: “naming a new fruit photo” is a prediction. (a) False (b) True
8. 8.True or false: “improves with more data” describes how AI works. (a) True (b) False
9. 9.🎨 This prompt asks a generative AI for: a summary of chapter 3. What kind of output is that? (a) music or sound (b) text (c) an image
10. 10.True or false: “AI can be wrong” is a myth about AI. (a) False (b) True
11. 11.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) training data (c) the model
12. 12.Which of these is bad for training a model? (a) clear, sharp examples (b) many varied examples (c) correct labels (d) testing on training data
13. 13.Which of these adds to bias? (a) balance the data (b) check who is missing (c) add more of the same (d) ask different people
14. 14.Which of these is a fact about AI? (a) long answers are true (b) AI copies data patterns (c) AI has real feelings (d) AI always checks facts
15. 15.True or false: photos of people of many ages and skin tones is balanced data (fairer for everyone). (a) True (b) False
16. 16.🔍 Which is the odd one out? (a) predicting rain (b) writing quiz questions (c) unlocking with a face (d) sorting photos by face
17. 17.True or false: “it sounds very sure” is NOT a real way to check an AI answer. (a) False (b) True
18. 18.Which of these is a fact about AI? (a) AI knows everything (b) AI can make up facts (c) AI has real feelings (d) AI is never wrong
19. 19.Which of these uses AI? (a) a kitchen weighing scale (b) a spam filter (c) a torch (d) a fan regulator
20. 20.🛠️ Which does this job need: spotting spam emails by their patterns? (a) needs AI (learning) (b) a simple rule is enough
21. 21.📱 What is the AI doing here: a car camera reading a speed sign? (a) generating (b) predicting (c) recommending (d) recognising
22. 22.True or false: sorting names in A to Z order needs AI that learns from examples. (a) True (b) False
23. 23.🧐 Fact or myth: “AI can be wrong”? (a) a myth (b) a fact
24. 24.✅ Is this a real check of an AI answer: find a trusted source? (a) not a real check (b) a real check
25. 25.🔍 Which is the odd one out? (a) correct labels (b) very few examples (c) photos in many lights (d) examples of every type
26. 26.🤖 AI or a normal program: which one “follows fixed rules”? (a) AI (b) a normal program
27. 27.Which of these is generative AI (it makes something new)? (a) predicting rain (b) inventing a recipe (c) suggesting a video (d) spotting spam
28. 28.Which of these is bad for training a model? (a) new data for testing (b) examples of every type (c) correct labels (d) labels added at random
29. 29.🔍 Which is the odd one out? (a) long answers are true (b) AI knows everything (c) AI can be wrong (d) AI has real feelings
30. 30.Which of these is training data? (a) recorded spoken words (b) naming a new leaf (c) what training builds (d) naming a new fruit photo
31. 31.🏷️ 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
32. 32.📺 A video app suggests what to watch next. What is the AI doing? (a) charging (b) printing (c) cooking (d) recommending
33. 33.Which of these is a myth about AI? (a) AI can be wrong (b) AI has real feelings (c) AI can mix up numbers (d) AI copies data patterns
34. 34.🤖 AI or a normal program: which one “improves with more data”? (a) a normal program (b) AI
35. 35.🔍 Which is the odd one out? (a) photos grouped by face (b) a light switch (c) a calculator (d) a printed timetable
36. 36.True or false: “new data for testing” is bad for training a model. (a) False (b) True
37. 37.🔮 A trained model sees a new photo and says “mango”. This is… (a) a password (b) training data (c) a label error (d) a prediction
38. 38.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) a prediction (c) training data
39. 39.🎨 This prompt asks a generative AI for: a picture of a castle made of mangoes. What kind of output is that? (a) text (b) music or sound (c) an image
40. 40.Which of these is not AI? (a) a voice assistant (b) voice typing (c) a torch (d) video suggestions

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