AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 950 · 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.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) the model (c) training data
2. 2.🛠️ Which does this job need: sorting names in A to Z order? (a) a simple rule is enough (b) needs AI (learning)
3. 3.Which of these is good for training a model? (a) copies of one photo (b) labels added at random (c) only one kind of example (d) examples of every type
4. 4.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
5. 5.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
6. 6.⏩ In machine learning, which comes first: flagging a new email or labelled fruit photos? (a) flagging a new email (b) labelled fruit photos
7. 7.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
8. 8.🔍 Which is the odd one out? (a) copies of one photo (b) very few examples (c) many varied examples (d) wrong labels
9. 9.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
11. 11.🔍 Which is the odd one out? (a) AI answers need checking (b) AI can make up facts (c) AI learns from data (d) long answers are true
12. 12.⏩ In machine learning, which comes first: the trained program or naming a new leaf? (a) the trained program (b) naming a new leaf
13. 13.🔁 What is the right order? (a) prediction, data, model (b) model, prediction, data (c) data, model, prediction (d) data, prediction, model
14. 14.Which of these is not AI? (a) photos grouped by face (b) an app translating signs (c) video suggestions (d) a calculator
15. 15.Which of these is AI that sorts, spots or predicts (not generative)? (a) drawing a new picture (b) writing quiz questions (c) sorting photos by face (d) inventing a recipe
16. 16.💬 What do we call the request you type to a generative AI? (a) a battery (b) a password (c) a label (d) a prompt
17. 17.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
18. 18.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
19. 19.✨ Is everything a generative AI makes true? (a) yes, always (b) only on Mondays (c) no, it can be wrong
20. 20.⏩ In machine learning, which comes first: typing a new spoken word or old emails marked spam? (a) typing a new spoken word (b) old emails marked spam
21. 21.✨ Generative or not: reading number plates? (a) makes something new (b) sorts or predicts
22. 22.True or false: “it has a neat layout” is a real way to check an AI answer. (a) True (b) False
23. 23.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
24. 24.🏠 AI or not AI: a torch? (a) uses AI (b) not AI
25. 25.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
26. 26.🛠️ Does this reduce bias or add to it: use one group only? (a) reduces bias (b) adds to bias
27. 27.🔁 Training data, the model or a prediction: “flagging a new email”? (a) a prediction (b) the model (c) training data
28. 28.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) balanced data (b) one-sided data
29. 29.✨ Generative or not: creating a cartoon? (a) makes something new (b) sorts or predicts
30. 30.⏩ In machine learning, which comes first: old emails marked spam or the learned patterns? (a) old emails marked spam (b) the learned patterns
31. 31.True or false: switching on a street light at 6 pm only needs a simple fixed rule. (a) False (b) True
32. 32.Which of these helps reduce bias? (a) add more of the same (b) ignore complaints (c) test on one group only (d) balance the data
33. 33.True or false: “improves with more data” describes how AI works. (a) False (b) True
34. 34.🔍 Which is the odd one out? (a) examples of every type (b) clear, sharp examples (c) blurry, unclear photos (d) many varied examples
35. 35.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will shut down (c) it will turn yellow (d) it will be perfect
36. 36.True or false: “AI answers need checking” is a fact about AI. (a) False (b) True
37. 37.True or false: “handles unseen examples” describes how AI works. (a) False (b) True
38. 38.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
39. 39.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
40. 40.Which of these is AI that sorts, spots or predicts (not generative)? (a) drawing a new picture (b) inventing a recipe (c) writing quiz questions (d) unlocking with a face

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