AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 932 · 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.⌨️ Your phone keyboard suggests the next word. What is the AI doing? (a) drawing (b) sleeping (c) singing (d) predicting
2. 2.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) better at its task (d) slower to switch on
3. 3.⏩ In machine learning, which comes first: what training builds or 500 labelled leaf photos? (a) what training builds (b) 500 labelled leaf photos
4. 4.True or false: “naming a new fruit photo” is training data. (a) True (b) False
5. 5.🤖 AI or a normal program: which one “improves with more data”? (a) a normal program (b) AI
6. 6.Which of these is AI that sorts, spots or predicts (not generative)? (a) inventing a recipe (b) making up a story (c) drawing a new picture (d) suggesting a video
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
8. 8.Which of these helps reduce bias? (a) balance the data (b) add more of the same (c) copy old unfair choices (d) use one group only
9. 9.Which of these describes how AI works? (a) one set rule per case (b) spots patterns in data (c) does exactly as coded (d) uses rules people wrote
10. 10.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) new data for testing (d) very few examples
11. 11.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
12. 12.🏷️ 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
13. 13.🧪 Why test a model on NEW examples? (a) to change its name (b) to see if it works (c) to make it slower (d) to delete its data
14. 14.True or false: “AI can mix up numbers” is a fact about AI. (a) False (b) True
15. 15.✨ Generative or not: sorting photos by face? (a) sorts or predicts (b) makes something new
16. 16.📱 What is the AI doing here: a keyboard guessing your next word? (a) generating (b) predicting (c) recognising (d) recommending
17. 17.True or false: “AI can make up facts” is a myth about AI. (a) False (b) True
18. 18.Which of these is generative AI (it makes something new)? (a) spotting a sick leaf (b) suggesting a video (c) drawing a new picture (d) spotting spam
19. 19.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
20. 20.Which of these is generative AI (it makes something new)? (a) turning speech to text (b) spotting a sick leaf (c) making up a story (d) unlocking with a face
21. 21.Which of these uses AI? (a) a stopwatch (b) photos grouped by face (c) a kitchen weighing scale (d) a calculator
22. 22.🔁 What is the right order? (a) prediction, data, model (b) model, prediction, data (c) data, prediction, model (d) data, model, prediction
23. 23.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
24. 24.True or false: “tomorrow’s rain guess” is a prediction. (a) True (b) False
25. 25.🎨 This prompt asks a generative AI for: a cartoon of a dancing elephant. What kind of output is that? (a) music or sound (b) text (c) an image
26. 26.🧐 Fact or myth: “AI knows everything”? (a) a myth (b) a fact
27. 27.🤖 What does AI stand for? (a) app installer (b) actual information (c) artificial intelligence (d) automatic internet
28. 28.True or false: “500 labelled leaf photos” is the model. (a) True (b) False
29. 29.🔮 A trained model sees a new photo and says “mango”. This is… (a) a password (b) a label error (c) training data (d) a prediction
30. 30.🔍 Which is the odd one out? (a) clear, sharp examples (b) very few examples (c) wrong labels (d) only one kind of example
31. 31.Which of these is a myth about AI? (a) AI knows everything (b) AI can be wrong (c) AI can make up facts (d) AI can mix up numbers
32. 32.✨ Generative or not: turning speech to text? (a) sorts or predicts (b) makes something new
33. 33.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
34. 34.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
35. 35.True or false: “old emails marked spam” is training data. (a) True (b) False
36. 36.True or false: showing the time on a clock needs AI that learns from examples. (a) False (b) True
37. 37.Which of these is good for training a model? (a) very few examples (b) copies of one photo (c) labels added at random (d) checking labels twice
38. 38.🎨 This prompt asks a generative AI for: five quiz questions on fractions. What kind of output is that? (a) music or sound (b) text (c) an image
39. 39.True or false: “ask different people” adds to bias. (a) True (b) False
40. 40.🤖 AI or a normal program: which one “makes a best guess”? (a) a normal program (b) AI

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