AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 450 · 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.True or false: “find a trusted source” is NOT a real way to check an AI answer. (a) True (b) False
2. 2.Which of these describes how AI works? (a) same steps every time (b) makes a best guess (c) one set rule per case (d) does exactly as coded
3. 3.Which of these describes how AI works? (a) spots patterns in data (b) follows fixed rules (c) one set rule per case (d) uses rules people wrote
4. 4.True or false: “AI can mix up numbers” is a myth about AI. (a) True (b) False
5. 5.True or false: “five quiz questions on fractions” asks for an image. (a) False (b) True
6. 6.🤖 AI or a normal program: which one “makes a best guess”? (a) AI (b) a normal program
7. 7.🛠️ Which does this job need: sorting names in A to Z order? (a) a simple rule is enough (b) needs AI (learning)
8. 8.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) very few examples (d) photos in many lights
9. 9.✅ Is this a real check of an AI answer: find a trusted source? (a) a real check (b) not a real check
10. 10.🔍 Which is the odd one out? (a) AI copies data patterns (b) AI learns from data (c) confident means correct (d) AI can be wrong
11. 11.⏩ In machine learning, which comes first: the learned patterns or old emails marked spam? (a) the learned patterns (b) old emails marked spam
12. 12.True or false: “follows fixed rules” describes how a normal program works. (a) True (b) False
13. 13.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
14. 14.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) only one kind of example (d) checking labels twice
15. 15.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
16. 16.Which of these is AI that sorts, spots or predicts (not generative)? (a) making up a story (b) writing quiz questions (c) creating a cartoon (d) turning speech to text
17. 17.🤖 AI or a normal program: which one “improves with more data”? (a) AI (b) a normal program
18. 18.True or false: “500 labelled leaf photos” is training data. (a) True (b) False
19. 19.Which of these describes how a normal program works? (a) handles unseen examples (b) learns from examples (c) makes a best guess (d) never learns from data
20. 20.Which of these is a fact about AI? (a) long answers are true (b) AI always checks facts (c) AI is never wrong (d) AI can make up facts
21. 21.🔍 Which is the odd one out? (a) spotting a sick leaf (b) drafting a letter (c) writing a new poem (d) making up a story
22. 22.🤖 What makes an AI system different from a normal program? (a) it has a screen (b) it is always right (c) it uses electricity (d) it learns from examples
23. 23.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
24. 24.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
25. 25.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
26. 26.Which of these is bad for training a model? (a) examples of every type (b) checking labels twice (c) clear, sharp examples (d) labels added at random
27. 27.🔦 Is a torch with an on/off switch AI? (a) no (b) only if it is bright (c) yes
28. 28.🎨 This prompt asks a generative AI for: a story about a brave ant. What kind of output is that? (a) music or sound (b) an image (c) text
29. 29.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) one-sided data (b) balanced data
30. 30.Which of these is a myth about AI? (a) AI copies data patterns (b) long answers are true (c) AI answers need checking (d) AI learns from data
31. 31.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam
32. 32.True or false: adding up the marks on a report card needs AI that learns from examples. (a) False (b) True
33. 33.Which of these is generative AI (it makes something new)? (a) predicting rain (b) unlocking with a face (c) spotting spam (d) drawing a new picture
34. 34.🛠️ Which does this job need: recognising a friend’s face in photos? (a) a simple rule is enough (b) needs AI (learning)
35. 35.🧐 Fact or myth: “AI can mix up numbers”? (a) a fact (b) a myth
36. 36.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the font size (d) the right answer tag
37. 37.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) training data (c) a prediction
38. 38.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
39. 39.Which of these helps reduce bias? (a) ask different people (b) add more of the same (c) skip testing (d) use one group only
40. 40.📦 What happens during training? (a) it takes photos (b) it charges up (c) it learns patterns (d) it prints labels

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