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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 47 · 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 is the odd one out? (a) fix wrong labels (b) copy old unfair choices (c) add varied examples (d) ask different people
  2. 2.Which of these is generative AI (it makes something new)? (a) suggesting a video (b) turning speech to text (c) unlocking with a face (d) making up a story
  3. 3.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
  4. 4.Which of these uses AI? (a) a printed timetable (b) a spam filter (c) a torch (d) a calculator
  5. 5.🔍 Which is the odd one out? (a) voice typing (b) a voice assistant (c) photos grouped by face (d) a digital watch
  6. 6.True or false: “a summary of chapter 3” asks for text. (a) True (b) False
  7. 7.🔍 Which is the odd one out? (a) examples of every type (b) only one kind of example (c) clear, sharp examples (d) new data for testing
  8. 8.🛠️ Which does this job need: recognising a friend’s face in photos? (a) a simple rule is enough (b) needs AI (learning)
  9. 9.🔍 Which is the odd one out? (a) a digital watch (b) a stopwatch (c) a fan regulator (d) video suggestions
  10. 10.Which of these is good for training a model? (a) checking labels twice (b) testing on training data (c) copies of one photo (d) labels added at random
  11. 11.🔍 Which is the odd one out? (a) add more of the same (b) test on many groups (c) fix wrong labels (d) ask different people
  12. 12.🤖 AI or a normal program: which one “one set rule per case”? (a) a normal program (b) AI
  13. 13.True or false: adding up the marks on a report card only needs a simple fixed rule. (a) True (b) False
  14. 14.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
  15. 15.🎨 This prompt asks a generative AI for: a soft lullaby melody. What kind of output is that? (a) text (b) an image (c) music or sound
  16. 16.True or false: “one set rule per case” describes how AI works. (a) True (b) False
  17. 17.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its screen size (d) its colour
  18. 18.True or false: a camera app naming the flower you point at is AI predicting something. (a) False (b) True
  19. 19.Which of these adds to bias? (a) ask different people (b) check who is missing (c) test on many groups (d) copy old unfair choices
  20. 20.📅 An AI gives a date for a history event. How can you check? (a) ask it to repeat (b) guess (c) look in your textbook (d) pick a lucky number
  21. 21.🏠 AI or not AI: a printed timetable? (a) uses AI (b) not AI
  22. 22.🔍 Which is the odd one out? (a) writing a new poem (b) drafting a letter (c) drawing a new picture (d) reading number plates
  23. 23.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
  24. 24.🔍 Which is the odd one out? (a) spotting a sick leaf (b) sorting photos by face (c) writing quiz questions (d) turning speech to text
  25. 25.🏷️ 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
  26. 26.🤖 What does AI stand for? (a) actual information (b) artificial intelligence (c) app installer (d) automatic internet
  27. 27.📦 What happens during training? (a) it charges up (b) it prints labels (c) it takes photos (d) it learns patterns
  28. 28.True or false: “test on one group only” adds to bias. (a) True (b) False
  29. 29.🛠️ Which does this job need: suggesting the next word as you type? (a) a simple rule is enough (b) needs AI (learning)
  30. 30.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it may get it wrong (c) it will shut down (d) it will turn yellow
  31. 31.Which of these adds to bias? (a) ask different people (b) add more of the same (c) fix wrong labels (d) balance the data
  32. 32.⌨️ Your phone keyboard suggests the next word. What is the AI doing? (a) predicting (b) drawing (c) singing (d) sleeping
  33. 33.Which of these is AI that sorts, spots or predicts (not generative)? (a) sorting photos by face (b) inventing a recipe (c) drafting a letter (d) creating a cartoon
  34. 34.Which of these is AI that sorts, spots or predicts (not generative)? (a) drawing a new picture (b) turning speech to text (c) writing a new poem (d) drafting a letter
  35. 35.True or false: “write a poem about the monsoon” asks for an image. (a) False (b) True
  36. 36.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  37. 37.⏩ In machine learning, which comes first: naming a new leaf or what training builds? (a) naming a new leaf (b) what training builds
  38. 38.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  39. 39.🤖 AI or a normal program: which one “follows fixed rules”? (a) AI (b) a normal program
  40. 40.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True

Answer key

  1. copy old unfair choices
  2. making up a story
  3. False
  4. a spam filter
  5. a digital watch
  6. True
  7. only one kind of example
  8. needs AI (learning)
  9. video suggestions
  10. checking labels twice
  11. add more of the same
  12. a normal program
  13. True
  14. False
  15. music or sound
  16. False
  17. its training data
  18. False
  19. copy old unfair choices
  20. look in your textbook
  21. not AI
  22. reading number plates
  23. False
  24. writing quiz questions
  25. spam
  26. artificial intelligence
  27. it learns patterns
  28. True
  29. needs AI (learning)
  30. it may get it wrong
  31. add more of the same
  32. predicting
  33. sorting photos by face
  34. turning speech to text
  35. False
  36. balanced data
  37. what training builds
  38. balanced data
  39. a normal program
  40. False

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