AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 718 · 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: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) True (b) False
2. 2.🔍 Which is the odd one out? (a) photos in many lights (b) testing on training data (c) correct labels (d) examples of every type
3. 3.🔍 Which is the odd one out? (a) a spam filter (b) face unlock on a phone (c) photos grouped by face (d) a printed timetable
4. 4.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
5. 5.⏩ In machine learning, which comes first: flagging a new email or 500 labelled leaf photos? (a) flagging a new email (b) 500 labelled leaf photos
6. 6.Which of these is generative AI (it makes something new)? (a) sorting photos by face (b) creating a cartoon (c) unlocking with a face (d) spotting spam
7. 7.True or false: an AI tool making a brand-new tune is AI generating something new. (a) True (b) False
8. 8.True or false: “what training builds” is the model. (a) False (b) True
9. 9.✨ Generative or not: creating a cartoon? (a) makes something new (b) sorts or predicts
10. 10.⏩ In machine learning, which comes first: what training builds or 500 labelled leaf photos? (a) what training builds (b) 500 labelled leaf photos
11. 11.True or false: photos of people from only one age group is balanced data (fairer for everyone). (a) True (b) False
12. 12.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
13. 13.✨ Generative or not: turning speech to text? (a) sorts or predicts (b) makes something new
14. 14.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) a fair model (c) a broken screen (d) bias from data
15. 15.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) copy old unfair choices (d) check who is missing
16. 16.⏩ In machine learning, which comes first: 500 labelled leaf photos or naming a new fruit photo? (a) 500 labelled leaf photos (b) naming a new fruit photo
17. 17.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
18. 18.🏠 AI or not AI: face unlock on a phone? (a) not AI (b) uses AI
19. 19.✨ What does generative AI do? (a) only adds numbers (b) makes new content (c) only stores files (d) charges batteries
20. 20.True or false: recognising a friend’s face in photos only needs a simple fixed rule. (a) True (b) False
21. 21.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
22. 22.True or false: “handles unseen examples” describes how a normal program works. (a) False (b) True
23. 23.🛠️ Does this reduce bias or add to it: test on many groups? (a) reduces bias (b) adds to bias
24. 24.⏩ In machine learning, which comes first: the learned patterns or old emails marked spam? (a) the learned patterns (b) old emails marked spam
25. 25.🤖 AI or a normal program: which one “uses rules people wrote”? (a) a normal program (b) AI
26. 26.True or false: “copies of one photo” is good for training a model. (a) True (b) False
27. 27.🧐 Fact or myth: “AI has real feelings”? (a) a myth (b) a fact
28. 28.Which of these is training data? (a) past weather records (b) the trained program (c) naming a new leaf (d) naming a new fruit photo
29. 29.🛠️ Which does this job need: counting the words in an essay? (a) a simple rule is enough (b) needs AI (learning)
30. 30.🔮 A trained model sees a new photo and says “mango”. This is… (a) a password (b) training data (c) a label error (d) a prediction
31. 31.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) True (b) False
32. 32.Which of these adds to bias? (a) add varied examples (b) copy old unfair choices (c) balance the data (d) check who is missing
33. 33.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
34. 34.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) reduces bias (b) adds to bias
35. 35.Which of these is bad for training a model? (a) correct labels (b) only one kind of example (c) photos in many lights (d) examples of every type
36. 36.🧠 Machine learning is a way for computers to… (a) charge faster (b) learn from data (c) clean screens (d) print pages
37. 37.True or false: suggesting the next word as you type needs AI that learns from examples. (a) False (b) True
38. 38.✅ Is this a real check of an AI answer: ask your teacher? (a) a real check (b) not a real check
39. 39.True or false: “it answered quickly” is a real way to check an AI answer. (a) False (b) True
40. 40.True or false: “rain sound effects for a play” asks for music or sound. (a) True (b) False

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