AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 812 · 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 is the odd one out? (a) making up a story (b) creating a cartoon (c) reading number plates (d) writing a new poem
2. 2.True or false: “a story about a brave ant” asks for music or sound. (a) False (b) True
3. 3.Which of these is a prediction? (a) the learned patterns (b) what training builds (c) 500 labelled leaf photos (d) typing a new spoken word
4. 4.🤖 Who builds and trains AI systems? (a) the weather (b) the Moon (c) people (d) nobody at all
5. 5.True or false: recognising a friend’s face in photos only needs a simple fixed rule. (a) False (b) True
6. 6.True or false: “labelled fruit photos” is a prediction. (a) False (b) True
7. 7.🤖 Does an AI system think and feel like a person? (a) no, it finds patterns (b) yes, it gets sad (c) yes, just like us (d) only on weekends
8. 8.🛠️ Which does this job need: suggesting the next word as you type? (a) needs AI (learning) (b) a simple rule is enough
9. 9.🎨 An image generator makes a picture from… (a) your password (b) the weather (c) a phone number (d) a text description
10. 10.Which of these is good for training a model? (a) many varied examples (b) wrong labels (c) blurry, unclear photos (d) very few examples
11. 11.🔁 What is the right order? (a) model, prediction, data (b) data, model, prediction (c) data, prediction, model (d) prediction, data, model
12. 12.⏩ In machine learning, which comes first: the trained program or labelled fruit photos? (a) the trained program (b) labelled fruit photos
13. 13.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
14. 14.🔍 Which is the odd one out? (a) long answers are true (b) AI can make up facts (c) AI is never wrong (d) AI has real feelings
15. 15.🤖 AI or a normal program: which one “gives a likely answer”? (a) AI (b) a normal program
16. 16.Which of these uses AI? (a) a light switch (b) a stopwatch (c) a torch (d) a map predicting traffic
17. 17.True or false: “test on many groups” adds to bias. (a) False (b) True
18. 18.Which of these is not AI? (a) voice typing (b) photos grouped by face (c) an image generator (d) a digital watch
19. 19.⚖️ One-sided or balanced data: a leaf app trained only on summer leaves? (a) balanced data (b) one-sided data
20. 20.🧪 Why test a model on NEW examples? (a) to delete its data (b) to make it slower (c) to change its name (d) to see if it works
21. 21.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
22. 22.🧐 Fact or myth: “long answers are true”? (a) a fact (b) a myth
23. 23.Which of these is a prediction? (a) past weather records (b) the learned patterns (c) old emails marked spam (d) tomorrow’s rain guess
24. 24.🛠️ Which does this job need: turning spoken words into typed text? (a) a simple rule is enough (b) needs AI (learning)
25. 25.⏩ In machine learning, which comes first: past weather records or flagging a new email? (a) past weather records (b) flagging a new email
26. 26.Which of these is generative AI (it makes something new)? (a) sorting photos by face (b) spotting spam (c) turning speech to text (d) making up a story
27. 27.True or false: “naming a new leaf” is the model. (a) True (b) False
28. 28.🤖 AI or a normal program: which one “spots patterns in data”? (a) AI (b) a normal program
29. 29.Which of these describes how AI works? (a) uses rules people wrote (b) never learns from data (c) same steps every time (d) spots patterns in data
30. 30.🤖 AI or a normal program: which one “one set rule per case”? (a) a normal program (b) AI
31. 31.Which of these describes how a normal program works? (a) handles unseen examples (b) improves with more data (c) uses rules people wrote (d) spots patterns in data
32. 32.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
33. 33.Which of these describes how a normal program works? (a) learns from examples (b) does exactly as coded (c) spots patterns in data (d) makes a best guess
34. 34.Which of these uses AI? (a) a printed timetable (b) video suggestions (c) a microwave timer (d) a calculator
35. 35.🖼️ If an image generator always draws a scientist as a man, that shows… (a) a strong battery (b) bias from data (c) a fair model (d) a broken screen
36. 36.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
37. 37.🛠️ Does this reduce bias or add to it: ignore complaints? (a) reduces bias (b) adds to bias
38. 38.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) a prediction (c) the model
39. 39.Which of these is a myth about AI? (a) AI can mix up numbers (b) AI learns from data (c) AI answers need checking (d) AI is never wrong
40. 40.Which of these adds to bias? (a) balance the data (b) skip testing (c) fix wrong labels (d) test on many groups

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