AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 823 · 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.🤖 AI or a normal program: which one “same steps every time”? (a) a normal program (b) AI
2. 2.🔍 Which is the odd one out? (a) photos in many lights (b) many varied examples (c) correct labels (d) blurry, unclear photos
3. 3.✨ Generative or not: spotting spam? (a) makes something new (b) sorts or predicts
4. 4.⌨️ Your phone keyboard suggests the next word. What is the AI doing? (a) predicting (b) drawing (c) singing (d) sleeping
5. 5.Which of these uses AI? (a) a light switch (b) an AI chatbot (c) a doorbell (d) a calculator
6. 6.Which of these helps reduce bias? (a) test on many groups (b) copy old unfair choices (c) skip testing (d) test on one group only
7. 7.True or false: “learns from examples” describes how AI works. (a) True (b) False
8. 8.🧪 Why test a model on NEW examples? (a) to change its name (b) to make it slower (c) to see if it works (d) to delete its data
9. 9.Which of these is AI that sorts, spots or predicts (not generative)? (a) making up a story (b) writing a new poem (c) unlocking with a face (d) inventing a recipe
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.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
12. 12.⏩ In machine learning, which comes first: the trained program or 500 labelled leaf photos? (a) the trained program (b) 500 labelled leaf photos
13. 13.🔍 Which is the odd one out? (a) a digital watch (b) a fan regulator (c) a stopwatch (d) a spam filter
14. 14.📱 What is the AI doing here: a keyboard guessing your next word? (a) generating (b) recognising (c) recommending (d) predicting
15. 15.True or false: “learns from examples” describes how a normal program works. (a) False (b) True
16. 16.🤖 What does AI stand for? (a) automatic internet (b) actual information (c) artificial intelligence (d) app installer
17. 17.Which of these uses AI? (a) photos grouped by face (b) a printed timetable (c) a kitchen weighing scale (d) a light switch
18. 18.Which of these is good for training a model? (a) testing on training data (b) wrong labels (c) copies of one photo (d) many varied examples
19. 19.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
20. 20.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) change colour (d) slower to switch on
21. 21.True or false: “flagging a new email” is training data. (a) True (b) False
22. 22.🎨 This prompt asks a generative AI for: a drum beat for a dance. What kind of output is that? (a) music or sound (b) an image (c) text
23. 23.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
24. 24.🤖 AI or a normal program: which one “one set rule per case”? (a) AI (b) a normal program
25. 25.Which of these adds to bias? (a) add varied examples (b) check who is missing (c) fix wrong labels (d) ignore complaints
26. 26.🧐 Fact or myth: “AI knows everything”? (a) a fact (b) a myth
27. 27.Which of these is the model? (a) typing a new spoken word (b) 500 labelled leaf photos (c) flagging a new email (d) the trained program
28. 28.True or false: a handwriting app shown only neat writing is balanced data (fairer for everyone). (a) False (b) True
29. 29.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its training data (c) its colour (d) its screen size
30. 30.Which of these describes how AI works? (a) one set rule per case (b) uses rules people wrote (c) does exactly as coded (d) makes a best guess
31. 31.🧐 Fact or myth: “confident means correct”? (a) a myth (b) a fact
32. 32.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) few child voices in data (c) children speak too fast (d) it was a rainy day
33. 33.🧐 Fact or myth: “AI can make up facts”? (a) a myth (b) a fact
34. 34.💬 What do we call the request you type to a generative AI? (a) a label (b) a prompt (c) a battery (d) a password
35. 35.True or false: “flagging a new email” is a prediction. (a) False (b) True
36. 36.🔍 Which is the odd one out? (a) AI always checks facts (b) AI has real feelings (c) AI knows everything (d) AI can mix up numbers
37. 37.⏩ In machine learning, which comes first: tomorrow’s rain guess or 500 labelled leaf photos? (a) tomorrow’s rain guess (b) 500 labelled leaf photos
38. 38.🏠 AI or not AI: a doorbell? (a) uses AI (b) not AI
39. 39.🏠 AI or not AI: a calculator? (a) not AI (b) uses AI
40. 40.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True

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