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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 68 · 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.⏩ In machine learning, which comes first: the trained program or flagging a new email? (a) the trained program (b) flagging a new email
  2. 2.🔁 Training data, the model or a prediction: “past weather records”? (a) a prediction (b) the model (c) training data
  3. 3.True or false: “spots patterns in data” describes how AI works. (a) False (b) True
  4. 4.🔍 Which is the odd one out? (a) many varied examples (b) checking labels twice (c) wrong labels (d) clear, sharp examples
  5. 5.Which of these is not AI? (a) a spam filter (b) a map predicting traffic (c) a fan regulator (d) voice typing
  6. 6.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) checking labels twice (d) copies of one photo
  7. 7.⏩ In machine learning, which comes first: what training builds or old emails marked spam? (a) what training builds (b) old emails marked spam
  8. 8.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
  9. 9.True or false: converting metres into centimetres needs AI that learns from examples. (a) False (b) True
  10. 10.True or false: “use one group only” adds to bias. (a) False (b) True
  11. 11.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
  12. 12.✅ Is this a real check of an AI answer: it is long and detailed? (a) a real check (b) not a real check
  13. 13.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
  14. 14.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  15. 15.True or false: “AI knows everything” is a myth about AI. (a) False (b) True
  16. 16.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) a prediction (b) the model (c) training data
  17. 17.🔁 What is the right order? (a) model, prediction, data (b) data, prediction, model (c) prediction, data, model (d) data, model, prediction
  18. 18.Which of these is a myth about AI? (a) AI can mix up numbers (b) AI knows everything (c) AI learns from data (d) AI can make up facts
  19. 19.🔍 Which is the odd one out? (a) drawing a new picture (b) writing quiz questions (c) drafting a letter (d) predicting rain
  20. 20.Which of these describes how a normal program works? (a) makes a best guess (b) learns from examples (c) uses rules people wrote (d) handles unseen examples
  21. 21.✨ Generative or not: composing a new tune? (a) makes something new (b) sorts or predicts
  22. 22.True or false: “correct labels” is good for training a model. (a) True (b) False
  23. 23.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  24. 24.Which of these uses AI? (a) voice typing (b) a light switch (c) a digital watch (d) a torch
  25. 25.🤔 An AI answer sounds very confident. Does that mean it is right? (a) yes, always (b) yes, if it is long (c) no, check it anyway
  26. 26.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
  27. 27.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
  28. 28.✨ Generative or not: unlocking with a face? (a) makes something new (b) sorts or predicts
  29. 29.🔍 Which is the odd one out? (a) blurry, unclear photos (b) examples of every type (c) wrong labels (d) copies of one photo
  30. 30.True or false: “spots patterns in data” describes how a normal program works. (a) True (b) False
  31. 31.True or false: “see if the book exists” is a real way to check an AI answer. (a) False (b) True
  32. 32.True or false: “follows fixed rules” describes how a normal program works. (a) False (b) True
  33. 33.True or false: “a logo idea for the eco club” asks for an image. (a) False (b) True
  34. 34.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) one group only (d) ignoring mistakes
  35. 35.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
  36. 36.🔍 Which is the odd one out? (a) predicting rain (b) suggesting a video (c) sorting photos by face (d) drawing a new picture
  37. 37.🔍 Which is the odd one out? (a) confident means correct (b) AI answers need checking (c) AI can mix up numbers (d) AI copies data patterns
  38. 38.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) better at its task (d) change colour
  39. 39.True or false: “same steps every time” describes how AI works. (a) False (b) True
  40. 40.True or false: “one set rule per case” describes how a normal program works. (a) True (b) False

Answer key

  1. the trained program
  2. training data
  3. True
  4. wrong labels
  5. a fan regulator
  6. copies of one photo
  7. old emails marked spam
  8. bad for training
  9. False
  10. True
  11. True
  12. not a real check
  13. not spam
  14. True
  15. True
  16. training data
  17. data, model, prediction
  18. AI knows everything
  19. predicting rain
  20. uses rules people wrote
  21. makes something new
  22. True
  23. reduces bias
  24. voice typing
  25. no, check it anyway
  26. good for training
  27. bad for training
  28. sorts or predicts
  29. examples of every type
  30. False
  31. True
  32. True
  33. True
  34. more varied data
  35. False
  36. drawing a new picture
  37. confident means correct
  38. better at its task
  39. False
  40. True

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