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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 672 · 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 does this job need: telling a cat photo from a dog photo? (a) a simple rule is enough (b) needs AI (learning)
  2. 2.✍️ How does an AI chatbot write its replies? (a) copies one website (b) predicts likely words (c) uses magic (d) asks a person
  3. 3.True or false: sorting names in A to Z order only needs a simple fixed rule. (a) False (b) True
  4. 4.True or false: a leaf app trained only on summer leaves is one-sided data (likely to make a biased model). (a) False (b) True
  5. 5.🧐 Fact or myth: “AI is never wrong”? (a) a fact (b) a myth
  6. 6.True or false: “AI learns from data” is a myth about AI. (a) False (b) True
  7. 7.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
  8. 8.Which of these is generative AI (it makes something new)? (a) inventing a recipe (b) suggesting a video (c) sorting photos by face (d) turning speech to text
  9. 9.True or false: “a soft lullaby melody” asks for an image. (a) False (b) True
  10. 10.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
  11. 11.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
  12. 12.True or false: “the learned patterns” is the model. (a) True (b) False
  13. 13.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its training data (c) the weather (d) its screen size
  14. 14.Which of these is a fact about AI? (a) confident means correct (b) AI always checks facts (c) AI can be wrong (d) AI has real feelings
  15. 15.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  16. 16.⏩ In machine learning, which comes first: naming a new leaf or labelled fruit photos? (a) naming a new leaf (b) labelled fruit photos
  17. 17.🔍 Which is the odd one out? (a) AI always checks facts (b) AI is never wrong (c) AI answers need checking (d) AI has real feelings
  18. 18.🤖 AI or a normal program: which one “spots patterns in data”? (a) AI (b) a normal program
  19. 19.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on many groups (c) fix wrong labels (d) balance the data
  20. 20.🤔 An AI answer sounds very confident. Does that mean it is right? (a) no, check it anyway (b) yes, if it is long (c) yes, always
  21. 21.📺 A video app suggests what to watch next. What is the AI doing? (a) printing (b) charging (c) recommending (d) cooking
  22. 22.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
  23. 23.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
  24. 24.Which of these is good for training a model? (a) very few examples (b) new data for testing (c) only one kind of example (d) wrong labels
  25. 25.🔍 Which is the odd one out? (a) new data for testing (b) clear, sharp examples (c) examples of every type (d) labels added at random
  26. 26.🤖 AI or a normal program: which one “one set rule per case”? (a) a normal program (b) AI
  27. 27.🔍 Which is the odd one out? (a) clear, sharp examples (b) new data for testing (c) very few examples (d) checking labels twice
  28. 28.🔍 Which is the odd one out? (a) labels added at random (b) checking labels twice (c) very few examples (d) wrong labels
  29. 29.True or false: “add varied examples” adds to bias. (a) True (b) False
  30. 30.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  31. 31.Which of these uses AI? (a) a torch (b) a digital watch (c) a calculator (d) an image generator
  32. 32.⏩ In machine learning, which comes first: what training builds or labelled fruit photos? (a) what training builds (b) labelled fruit photos
  33. 33.📱 What is the AI doing here: an AI chatbot writing a poem about rain? (a) recommending (b) recognising (c) predicting (d) generating
  34. 34.True or false: “five quiz questions on fractions” asks for an image. (a) False (b) True
  35. 35.🤖 AI or a normal program: which one “learns from examples”? (a) AI (b) a normal program
  36. 36.🎨 This prompt asks a generative AI for: a story about a brave ant. What kind of output is that? (a) an image (b) music or sound (c) text
  37. 37.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
  38. 38.True or false: “AI answers need checking” is a myth about AI. (a) False (b) True
  39. 39.🔍 Which is the odd one out? (a) a spam filter (b) a map predicting traffic (c) a doorbell (d) video suggestions
  40. 40.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training

Answer key

  1. needs AI (learning)
  2. predicts likely words
  3. True
  4. True
  5. a myth
  6. False
  7. good for training
  8. inventing a recipe
  9. False
  10. good for training
  11. one-sided data
  12. True
  13. its training data
  14. AI can be wrong
  15. False
  16. labelled fruit photos
  17. AI answers need checking
  18. AI
  19. copy old unfair choices
  20. no, check it anyway
  21. recommending
  22. spam
  23. False
  24. new data for testing
  25. labels added at random
  26. a normal program
  27. very few examples
  28. checking labels twice
  29. False
  30. adds to bias
  31. an image generator
  32. labelled fruit photos
  33. generating
  34. False
  35. AI
  36. text
  37. False
  38. False
  39. a doorbell
  40. good for training

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