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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 707 · 40 questions
TalentJR

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.

NameDateTime takenScore ___ / 40
  1. 1.🤖 Who builds and trains AI systems? (a) people (b) nobody at all (c) the Moon (d) the weather
  2. 2.⚖️ One-sided or balanced data: photos of people of many ages and skin tones? (a) one-sided data (b) balanced data
  3. 3.Which of these is a fact about AI? (a) AI has real feelings (b) AI can mix up numbers (c) confident means correct (d) long answers are true
  4. 4.🔁 What is the right order? (a) prediction, data, model (b) data, prediction, model (c) data, model, prediction (d) model, prediction, data
  5. 5.🏠 AI or not AI: a torch? (a) uses AI (b) not AI
  6. 6.⏩ In machine learning, which comes first: what training builds or labelled fruit photos? (a) what training builds (b) labelled fruit photos
  7. 7.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  8. 8.Which of these is a fact about AI? (a) confident means correct (b) long answers are true (c) AI is never wrong (d) AI can make up facts
  9. 9.True or false: a car camera reading a speed sign is AI generating something new. (a) True (b) False
  10. 10.Which of these is the model? (a) what training builds (b) tomorrow’s rain guess (c) 500 labelled leaf photos (d) old emails marked spam
  11. 11.🛠️ Which does this job need: sorting names in A to Z order? (a) needs AI (learning) (b) a simple rule is enough
  12. 12.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
  13. 13.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its screen size (c) its training data (d) its colour
  14. 14.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  15. 15.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
  16. 16.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
  17. 17.True or false: “500 labelled leaf photos” is the model. (a) False (b) True
  18. 18.🤖 Does an AI system think and feel like a person? (a) yes, just like us (b) no, it finds patterns (c) only on weekends (d) yes, it gets sad
  19. 19.🔍 Which is the odd one out? (a) reading number plates (b) unlocking with a face (c) turning speech to text (d) writing a new poem
  20. 20.True or false: “old emails marked spam” is the model. (a) True (b) False
  21. 21.🧪 Why test a model on NEW examples? (a) to make it slower (b) to delete its data (c) to change its name (d) to see if it works
  22. 22.True or false: “AI knows everything” is a myth about AI. (a) False (b) True
  23. 23.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  24. 24.Which of these is training data? (a) what training builds (b) typing a new spoken word (c) 500 labelled leaf photos (d) naming a new fruit photo
  25. 25.🧐 Fact or myth: “AI always checks facts”? (a) a fact (b) a myth
  26. 26.True or false: “makes a best guess” describes how AI works. (a) True (b) False
  27. 27.🛠️ Which does this job need: suggesting the next word as you type? (a) a simple rule is enough (b) needs AI (learning)
  28. 28.Which of these is good for training a model? (a) correct labels (b) wrong labels (c) copies of one photo (d) blurry, unclear photos
  29. 29.True or false: “AI can mix up numbers” is a myth about AI. (a) True (b) False
  30. 30.🧐 Fact or myth: “long answers are true”? (a) a myth (b) a fact
  31. 31.🏠 AI or not AI: an app translating signs? (a) uses AI (b) not AI
  32. 32.🛠️ Which does this job need: counting the words in an essay? (a) a simple rule is enough (b) needs AI (learning)
  33. 33.True or false: “labels added at random” is bad for training a model. (a) False (b) True
  34. 34.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
  35. 35.True or false: “does exactly as coded” describes how AI works. (a) True (b) False
  36. 36.True or false: “typing a new spoken word” is a prediction. (a) True (b) False
  37. 37.Which of these is AI that sorts, spots or predicts (not generative)? (a) making up a story (b) writing a new poem (c) sorting photos by face (d) creating a cartoon
  38. 38.Which of these is bad for training a model? (a) checking labels twice (b) only one kind of example (c) many varied examples (d) photos in many lights
  39. 39.Which of these is a myth about AI? (a) AI can make up facts (b) AI copies data patterns (c) AI learns from data (d) long answers are true
  40. 40.Which of these describes how a normal program works? (a) same steps every time (b) handles unseen examples (c) learns from examples (d) improves with more data

Answer key

  1. people
  2. balanced data
  3. AI can mix up numbers
  4. data, model, prediction
  5. not AI
  6. labelled fruit photos
  7. balanced data
  8. AI can make up facts
  9. False
  10. what training builds
  11. a simple rule is enough
  12. good for training
  13. its training data
  14. reduces bias
  15. True
  16. bad for training
  17. False
  18. no, it finds patterns
  19. writing a new poem
  20. False
  21. to see if it works
  22. True
  23. False
  24. 500 labelled leaf photos
  25. a myth
  26. True
  27. needs AI (learning)
  28. correct labels
  29. False
  30. a myth
  31. uses AI
  32. a simple rule is enough
  33. True
  34. good for training
  35. False
  36. True
  37. sorting photos by face
  38. only one kind of example
  39. long answers are true
  40. same steps every time

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