AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 426 · 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: “write a poem about the monsoon” asks for music or sound. (a) False (b) True
2. 2.🛠️ Which does this job need: recognising a friend’s face in photos? (a) a simple rule is enough (b) needs AI (learning)
3. 3.⏩ In machine learning, which comes first: what training builds or tomorrow’s rain guess? (a) what training builds (b) tomorrow’s rain guess
4. 4.Which of these is not AI? (a) a spam filter (b) an app translating signs (c) photos grouped by face (d) a stopwatch
5. 5.🎨 This prompt asks a generative AI for: a story about a brave ant. What kind of output is that? (a) music or sound (b) text (c) an image
6. 6.🔍 Which is the odd one out? (a) test on many groups (b) ignore complaints (c) test on one group only (d) copy old unfair choices
7. 7.True or false: ringing the school bell at 9 am only needs a simple fixed rule. (a) False (b) True
8. 8.Which of these is a prediction? (a) what training builds (b) recorded spoken words (c) old emails marked spam (d) flagging a new email
9. 9.True or false: “fix wrong labels” adds to bias. (a) False (b) True
10. 10.⏩ In machine learning, which comes first: the trained program or tomorrow’s rain guess? (a) the trained program (b) tomorrow’s rain guess
11. 11.🏠 AI or not AI: a digital watch? (a) not AI (b) uses AI
12. 12.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
13. 13.⚖️ One-sided or balanced data: leaves from every season of the year? (a) one-sided data (b) balanced data
14. 14.True or false: “examples of every type” is bad for training a model. (a) True (b) False
15. 15.Which of these is bad for training a model? (a) blurry, unclear photos (b) many varied examples (c) clear, sharp examples (d) correct labels
16. 16.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
17. 17.🛠️ Which does this job need: spotting spam emails by their patterns? (a) needs AI (learning) (b) a simple rule is enough
18. 18.True or false: adding up the marks on a report card needs AI that learns from examples. (a) True (b) False
19. 19.🛠️ What helps make an AI system fairer? (a) fewer tests (b) one group only (c) ignoring mistakes (d) more varied data
20. 20.Which of these is training data? (a) naming a new fruit photo (b) recorded spoken words (c) the trained program (d) naming a new leaf
21. 21.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
22. 22.True or false: “typing a new spoken word” is training data. (a) True (b) False
23. 23.🔍 Which is the odd one out? (a) a calculator (b) a kitchen weighing scale (c) photos grouped by face (d) a light switch
24. 24.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
25. 25.🔍 Which is the odd one out? (a) AI knows everything (b) AI has real feelings (c) AI is never wrong (d) AI can be wrong
26. 26.⏩ In machine learning, which comes first: labelled fruit photos or typing a new spoken word? (a) labelled fruit photos (b) typing a new spoken word
27. 27.Which of these describes how a normal program works? (a) learns from examples (b) spots patterns in data (c) does exactly as coded (d) makes a best guess
28. 28.True or false: “AI answers need checking” is a myth about AI. (a) True (b) False
29. 29.True or false: recognising a bird from its song only needs a simple fixed rule. (a) True (b) False
30. 30.True or false: “a thank-you note to the bus driver” asks for text. (a) True (b) False
31. 31.🎨 An image generator makes a picture from… (a) a text description (b) the weather (c) your password (d) a phone number
32. 32.True or false: “use one group only” adds to bias. (a) True (b) False
33. 33.True or false: “recorded spoken words” is a prediction. (a) False (b) True
34. 34.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) photos in many lights (d) copies of one photo
35. 35.True or false: “uses rules people wrote” describes how AI works. (a) False (b) True
36. 36.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) examples of every type (d) very few examples
37. 37.True or false: “ask your teacher” is a real way to check an AI answer. (a) True (b) False
38. 38.🏠 AI or not AI: face unlock on a phone? (a) not AI (b) uses AI
39. 39.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) change colour (d) better at its task
40. 40.🔍 Which is the odd one out? (a) drawing a new picture (b) sorting photos by face (c) making up a story (d) creating a cartoon

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