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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 10 · 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.Which of these is a myth about AI? (a) AI has real feelings (b) AI can be wrong (c) AI can make up facts (d) AI copies data patterns
  2. 2.✅ Is this a real check of an AI answer: find a trusted source? (a) a real check (b) not a real check
  3. 3.⌨️ Your phone keyboard suggests the next word. What is the AI doing? (a) sleeping (b) drawing (c) singing (d) predicting
  4. 4.True or false: “match it to the textbook” is NOT a real way to check an AI answer. (a) False (b) True
  5. 5.True or false: “labels added at random” is bad for training a model. (a) True (b) False
  6. 6.Which of these uses AI? (a) face unlock on a phone (b) a digital watch (c) a stopwatch (d) a fan regulator
  7. 7.💬 What do we call the request you type to a generative AI? (a) a password (b) a battery (c) a label (d) a prompt
  8. 8.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) the model (c) a prediction
  9. 9.🔍 Which is the odd one out? (a) an app translating signs (b) a map predicting traffic (c) a torch (d) voice typing
  10. 10.🔍 Which is the odd one out? (a) add varied examples (b) balance the data (c) test on many groups (d) skip testing
  11. 11.True or false: “long answers are true” is a fact about AI. (a) False (b) True
  12. 12.🏷️ 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
  13. 13.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) add varied examples (d) add more of the same
  14. 14.Which of these is a myth about AI? (a) AI always checks facts (b) AI can make up facts (c) AI copies data patterns (d) AI learns from data
  15. 15.🔍 Which is the odd one out? (a) ask different people (b) add varied examples (c) test on many groups (d) use one group only
  16. 16.📱 What is the AI doing here: a keyboard guessing your next word? (a) recommending (b) predicting (c) generating (d) recognising
  17. 17.🎨 This prompt asks a generative AI for: a thank-you note to the bus driver. What kind of output is that? (a) an image (b) music or sound (c) text
  18. 18.✨ Generative or not: inventing a recipe? (a) sorts or predicts (b) makes something new
  19. 19.True or false: “ask your teacher” is a real way to check an AI answer. (a) False (b) True
  20. 20.🧐 Fact or myth: “AI learns from data”? (a) a myth (b) a fact
  21. 21.True or false: suggesting the next word as you type only needs a simple fixed rule. (a) True (b) False
  22. 22.📅 An AI gives a date for a history event. How can you check? (a) ask it to repeat (b) pick a lucky number (c) guess (d) look in your textbook
  23. 23.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) the phone was new (c) it was a rainy day (d) few child voices in data
  24. 24.Which of these adds to bias? (a) ignore complaints (b) add varied examples (c) check who is missing (d) ask different people
  25. 25.✅ Is this a real check of an AI answer: match it to the textbook? (a) a real check (b) not a real check
  26. 26.True or false: “AI answers need checking” is a fact about AI. (a) False (b) True
  27. 27.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
  28. 28.🔍 Which is the odd one out? (a) unlocking with a face (b) sorting photos by face (c) suggesting a video (d) drafting a letter
  29. 29.True or false: a book app suggesting a story like your last one is AI generating something new. (a) False (b) True
  30. 30.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
  31. 31.Which of these describes how a normal program works? (a) learns from examples (b) same steps every time (c) improves with more data (d) makes a best guess
  32. 32.🔍 Which is the odd one out? (a) check who is missing (b) test on one group only (c) add varied examples (d) ask different people
  33. 33.Which of these adds to bias? (a) test on many groups (b) use one group only (c) check who is missing (d) balance the data
  34. 34.Which of these describes how a normal program works? (a) makes a best guess (b) learns from examples (c) improves with more data (d) uses rules people wrote
  35. 35.🤖 Does an AI system think and feel like a person? (a) yes, just like us (b) yes, it gets sad (c) only on weekends (d) no, it finds patterns
  36. 36.📦 What happens during training? (a) it charges up (b) it prints labels (c) it takes photos (d) it learns patterns
  37. 37.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  38. 38.✨ Generative or not: drawing a new picture? (a) makes something new (b) sorts or predicts
  39. 39.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
  40. 40.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a prediction (c) training data (d) a password

Answer key

  1. AI has real feelings
  2. a real check
  3. predicting
  4. False
  5. True
  6. face unlock on a phone
  7. a prompt
  8. the model
  9. a torch
  10. skip testing
  11. False
  12. spam
  13. add more of the same
  14. AI always checks facts
  15. use one group only
  16. predicting
  17. text
  18. makes something new
  19. True
  20. a fact
  21. False
  22. look in your textbook
  23. few child voices in data
  24. ignore complaints
  25. a real check
  26. True
  27. one-sided data
  28. drafting a letter
  29. False
  30. False
  31. same steps every time
  32. test on one group only
  33. use one group only
  34. uses rules people wrote
  35. no, it finds patterns
  36. it learns patterns
  37. balanced data
  38. makes something new
  39. False
  40. a prediction

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