AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 661 · 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.🛠️ Which does this job need: reading messy handwriting? (a) needs AI (learning) (b) a simple rule is enough
2. 2.Which of these describes how a normal program works? (a) follows fixed rules (b) makes a best guess (c) gives a likely answer (d) learns from examples
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 describes how AI works? (a) learns from examples (b) uses rules people wrote (c) follows fixed rules (d) one set rule per case
5. 5.🔁 Training data, the model or a prediction: “flagging a new email”? (a) a prediction (b) the model (c) training data
6. 6.True or false: “ignore complaints” adds to bias. (a) False (b) True
7. 7.True or false: stories with doctors of every gender is one-sided data (likely to make a biased model). (a) False (b) True
8. 8.Which of these describes how a normal program works? (a) spots patterns in data (b) one set rule per case (c) handles unseen examples (d) gives a likely answer
9. 9.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
10. 10.🔍 Which is the odd one out? (a) a digital watch (b) a map predicting traffic (c) a kitchen weighing scale (d) a printed timetable
11. 11.✅ Is this a real check of an AI answer: it is long and detailed? (a) not a real check (b) a real check
12. 12.🏠 AI or not AI: a doorbell? (a) not AI (b) uses AI
13. 13.Which of these is generative AI (it makes something new)? (a) spotting a sick leaf (b) inventing a recipe (c) predicting rain (d) reading number plates
14. 14.True or false: “AI is never wrong” is a myth about AI. (a) True (b) False
15. 15.🏠 AI or not AI: face unlock on a phone? (a) not AI (b) uses AI
16. 16.🤔 An AI answer sounds very confident. Does that mean it is right? (a) no, check it anyway (b) yes, if it is long (c) yes, always
17. 17.📚 Usually, more good examples make a model… (a) better at its task (b) slower to switch on (c) change colour (d) forget everything
18. 18.True or false: “add varied examples” adds to bias. (a) False (b) True
19. 19.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) only one kind of example (d) new data for testing
20. 20.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
21. 21.Which of these is bad for training a model? (a) examples of every type (b) photos in many lights (c) checking labels twice (d) testing on training data
22. 22.🧐 Fact or myth: “confident means correct”? (a) a myth (b) a fact
23. 23.True or false: “a drum beat for a dance” asks for text. (a) True (b) False
24. 24.True or false: a fruit sorter shown only ripe mangoes is one-sided data (likely to make a biased model). (a) False (b) True
25. 25.🔍 Which is the odd one out? (a) examples of every type (b) correct labels (c) photos in many lights (d) testing on training data
26. 26.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
27. 27.📱 What is the AI doing here: an AI tool making a brand-new tune? (a) generating (b) recognising (c) recommending (d) predicting
28. 28.⌨️ Your phone keyboard suggests the next word. What is the AI doing? (a) drawing (b) singing (c) predicting (d) sleeping
29. 29.Which of these describes how a normal program works? (a) never learns from data (b) handles unseen examples (c) gives a likely answer (d) learns from examples
30. 30.🔍 Which is the odd one out? (a) AI knows everything (b) AI can make up facts (c) AI copies data patterns (d) AI can mix up numbers
31. 31.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
32. 32.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
33. 33.True or false: converting metres into centimetres needs AI that learns from examples. (a) False (b) True
34. 34.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam
35. 35.True or false: turning your spoken words into typed text is AI predicting something. (a) True (b) False
36. 36.True or false: “old emails marked spam” is a prediction. (a) False (b) True
37. 37.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) training data (c) the model
38. 38.True or false: “typing a new spoken word” is training data. (a) True (b) False
39. 39.🔍 Which is the odd one out? (a) examples of every type (b) copies of one photo (c) many varied examples (d) correct labels
40. 40.⏩ In machine learning, which comes first: the trained program or flagging a new email? (a) the trained program (b) flagging a new email

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