AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 373 · 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.⏩ In machine learning, which comes first: what training builds or flagging a new email? (a) what training builds (b) flagging a new email
2. 2.🏠 AI or not AI: a voice assistant? (a) uses AI (b) not AI
3. 3.Which of these is a myth about AI? (a) AI can mix up numbers (b) AI can be wrong (c) confident means correct (d) AI answers need checking
4. 4.🔍 Which is the odd one out? (a) sorting photos by face (b) spotting a sick leaf (c) turning speech to text (d) drawing a new picture
5. 5.🔍 Which is the odd one out? (a) inventing a recipe (b) reading number plates (c) writing quiz questions (d) drawing a new picture
6. 6.True or false: “improves with more data” describes how a normal program works. (a) False (b) True
7. 7.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its colour (c) the weather (d) its training data
8. 8.🤖 AI or a normal program: which one “same steps every time”? (a) AI (b) a normal program
9. 9.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
10. 10.Which of these is a fact about AI? (a) AI is never wrong (b) AI always checks facts (c) AI can mix up numbers (d) confident means correct
11. 11.⏩ In machine learning, which comes first: naming a new fruit photo or labelled fruit photos? (a) naming a new fruit photo (b) labelled fruit photos
12. 12.Which of these is training data? (a) the trained program (b) naming a new leaf (c) 500 labelled leaf photos (d) what training builds
13. 13.🛠️ Does this reduce bias or add to it: skip testing? (a) reduces bias (b) adds to bias
14. 14.📺 A video app suggests what to watch next. What is the AI doing? (a) printing (b) charging (c) recommending (d) cooking
15. 15.🤖 AI or a normal program: which one “learns from examples”? (a) AI (b) a normal program
16. 16.Which of these is bad for training a model? (a) examples of every type (b) clear, sharp examples (c) blurry, unclear photos (d) checking labels twice
17. 17.🏷️ 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
18. 18.Which of these is training data? (a) past weather records (b) the trained program (c) what training builds (d) flagging a new email
19. 19.🏠 AI or not AI: photos grouped by face? (a) uses AI (b) not AI
20. 20.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
21. 21.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) one-sided data (b) balanced data
22. 22.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
23. 23.Which of these describes how AI works? (a) uses rules people wrote (b) does exactly as coded (c) follows fixed rules (d) learns from examples
24. 24.Which of these is a myth about AI? (a) AI copies data patterns (b) AI learns from data (c) long answers are true (d) AI can be wrong
25. 25.Which of these is the model? (a) 500 labelled leaf photos (b) the trained program (c) recorded spoken words (d) naming a new leaf
26. 26.✍️ How does an AI chatbot write its replies? (a) asks a person (b) predicts likely words (c) uses magic (d) copies one website
27. 27.🔍 Which is the odd one out? (a) writing quiz questions (b) writing a new poem (c) spotting a sick leaf (d) making up a story
28. 28.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) children speak too fast (d) few child voices in data
29. 29.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) the model (c) training data
30. 30.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) more varied data (d) fewer tests
31. 31.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) not spam (b) spam
32. 32.Which of these is the model? (a) what training builds (b) naming a new fruit photo (c) recorded spoken words (d) old emails marked spam
33. 33.⏩ In machine learning, which comes first: flagging a new email or the learned patterns? (a) flagging a new email (b) the learned patterns
34. 34.🎨 This prompt asks a generative AI for: a soft lullaby melody. What kind of output is that? (a) an image (b) text (c) music or sound
35. 35.True or false: “correct labels” is good for training a model. (a) True (b) False
36. 36.Which of these adds to bias? (a) test on many groups (b) check who is missing (c) fix wrong labels (d) add more of the same
37. 37.Which of these adds to bias? (a) test on one group only (b) balance the data (c) check who is missing (d) test on many groups
38. 38.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) balanced data (b) one-sided data
39. 39.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the font size (c) a sticker on a laptop (d) the price
40. 40.Which of these is generative AI (it makes something new)? (a) writing a new poem (b) reading number plates (c) unlocking with a face (d) sorting photos by face

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