AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 388 · 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.✨ Is everything a generative AI makes true? (a) yes, always (b) no, it can be wrong (c) only on Mondays
2. 2.True or false: “old emails marked spam” is the model. (a) False (b) True
3. 3.True or false: a shop guessing how many umbrellas will sell is AI recognising something. (a) True (b) False
4. 4.True or false: “the trained program” is a prediction. (a) True (b) False
5. 5.🤖 AI or a normal program: which one “spots patterns in data”? (a) a normal program (b) AI
6. 6.Which of these is a fact about AI? (a) AI is never wrong (b) long answers are true (c) AI learns from data (d) confident means correct
7. 7.🎨 This prompt asks a generative AI for: a picture of a castle made of mangoes. What kind of output is that? (a) music or sound (b) an image (c) text
8. 8.Which of these is training data? (a) flagging a new email (b) what training builds (c) recorded spoken words (d) the learned patterns
9. 9.🧐 Fact or myth: “AI knows everything”? (a) a fact (b) a myth
10. 10.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) blurry, unclear photos (d) many varied examples
11. 11.Which of these is AI that sorts, spots or predicts (not generative)? (a) making up a story (b) creating a cartoon (c) reading number plates (d) writing a new poem
12. 12.🔍 Which is the odd one out? (a) photos in many lights (b) checking labels twice (c) only one kind of example (d) examples of every type
13. 13.🧪 Why test a model on NEW examples? (a) to delete its data (b) to see if it works (c) to change its name (d) to make it slower
14. 14.Which of these describes how a normal program works? (a) follows fixed rules (b) handles unseen examples (c) spots patterns in data (d) gives a likely answer
15. 15.True or false: spotting spam emails by their patterns only needs a simple fixed rule. (a) True (b) False
16. 16.True or false: “uses rules people wrote” describes how AI works. (a) False (b) True
17. 17.⏩ In machine learning, which comes first: labelled fruit photos or what training builds? (a) labelled fruit photos (b) what training builds
18. 18.⏩ In machine learning, which comes first: 500 labelled leaf photos or the trained program? (a) 500 labelled leaf photos (b) the trained program
19. 19.Which of these is the model? (a) 500 labelled leaf photos (b) typing a new spoken word (c) old emails marked spam (d) the learned patterns
20. 20.True or false: “one set rule per case” describes how AI works. (a) False (b) True
21. 21.Which of these describes how a normal program works? (a) improves with more data (b) learns from examples (c) spots patterns in data (d) uses rules people wrote
22. 22.True or false: “past weather records” is training data. (a) True (b) False
23. 23.Which of these is bad for training a model? (a) photos in many lights (b) checking labels twice (c) many varied examples (d) only one kind of example
24. 24.True or false: “find a trusted source” is NOT a real way to check an AI answer. (a) False (b) True
25. 25.True or false: switching on a street light at 6 pm only needs a simple fixed rule. (a) True (b) False
26. 26.🔍 Which is the odd one out? (a) ignore complaints (b) ask different people (c) test on many groups (d) fix wrong labels
27. 27.🔁 Training data, the model or a prediction: “the trained program”? (a) training data (b) the model (c) a prediction
28. 28.✅ Is this a real check of an AI answer: see if the book exists? (a) a real check (b) not a real check
29. 29.True or false: “AI can make up facts” is a fact about AI. (a) False (b) True
30. 30.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
31. 31.Which of these helps reduce bias? (a) copy old unfair choices (b) test on one group only (c) use one group only (d) ask different people
32. 32.✨ Generative or not: spotting a sick leaf? (a) makes something new (b) sorts or predicts
33. 33.🏷️ 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
34. 34.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) testing on training data (d) photos in many lights
35. 35.Which of these helps reduce bias? (a) ignore complaints (b) add more of the same (c) add varied examples (d) copy old unfair choices
36. 36.🛠️ Which does this job need: telling a cat photo from a dog photo? (a) needs AI (learning) (b) a simple rule is enough
37. 37.✨ Generative or not: inventing a recipe? (a) makes something new (b) sorts or predicts
38. 38.🎨 An image generator makes a picture from… (a) a text description (b) the weather (c) your password (d) a phone number
39. 39.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) a sticker on a laptop (d) the right answer tag
40. 40.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) a list of rules only (c) many labelled photos (d) one single photo

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