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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 728 · 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.✨ Generative or not: composing a new tune? (a) makes something new (b) sorts or predicts
  2. 2.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) a prediction (c) training data
  3. 3.True or false: an AI tool making a brand-new tune is AI generating something new. (a) True (b) False
  4. 4.True or false: “AI has real feelings” is a myth about AI. (a) True (b) False
  5. 5.True or false: “new data for testing” is good for training a model. (a) False (b) True
  6. 6.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  7. 7.Which of these is AI that sorts, spots or predicts (not generative)? (a) composing a new tune (b) writing a new poem (c) drafting a letter (d) predicting rain
  8. 8.🔍 Which is the odd one out? (a) ask different people (b) skip testing (c) use one group only (d) copy old unfair choices
  9. 9.🔍 Which is the odd one out? (a) drafting a letter (b) drawing a new picture (c) writing quiz questions (d) spotting a sick leaf
  10. 10.True or false: “a picture of a castle made of mangoes” asks for text. (a) True (b) False
  11. 11.Which of these is bad for training a model? (a) testing on training data (b) checking labels twice (c) examples of every type (d) correct labels
  12. 12.True or false: “old emails marked spam” is the model. (a) True (b) False
  13. 13.🔍 Which is the odd one out? (a) copies of one photo (b) examples of every type (c) checking labels twice (d) correct labels
  14. 14.Which of these describes how a normal program works? (a) does exactly as coded (b) handles unseen examples (c) spots patterns in data (d) gives a likely answer
  15. 15.🔍 Which is the odd one out? (a) examples of every type (b) clear, sharp examples (c) new data for testing (d) labels added at random
  16. 16.True or false: “examples of every type” is bad for training a model. (a) True (b) False
  17. 17.True or false: “a poster of a rainforest” asks for text. (a) True (b) False
  18. 18.🛠️ Does this reduce bias or add to it: skip testing? (a) adds to bias (b) reduces bias
  19. 19.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
  20. 20.Which of these helps reduce bias? (a) copy old unfair choices (b) skip testing (c) balance the data (d) use one group only
  21. 21.📚 An AI chatbot names a book that does not exist. This is called… (a) a hallucination (b) a summary (c) a backup (d) a password
  22. 22.True or false: “spots patterns in data” describes how a normal program works. (a) True (b) False
  23. 23.Which of these describes how a normal program works? (a) improves with more data (b) gives a likely answer (c) spots patterns in data (d) uses rules people wrote
  24. 24.True or false: a weather app guessing tomorrow’s rain is AI predicting something. (a) False (b) True
  25. 25.🔦 Is a torch with an on/off switch AI? (a) only if it is bright (b) yes (c) no
  26. 26.True or false: “AI has real feelings” is a fact about AI. (a) False (b) True
  27. 27.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) slower to switch on (d) better at its task
  28. 28.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  29. 29.✨ Generative or not: reading number plates? (a) sorts or predicts (b) makes something new
  30. 30.True or false: switching on a street light at 6 pm only needs a simple fixed rule. (a) True (b) False
  31. 31.True or false: “many varied examples” is good for training a model. (a) True (b) False
  32. 32.🔍 Which is the odd one out? (a) a calculator (b) photos grouped by face (c) a microwave timer (d) a stopwatch
  33. 33.📱 What is the AI doing here: a book app suggesting a story like your last one? (a) predicting (b) recognising (c) generating (d) recommending
  34. 34.Which of these adds to bias? (a) copy old unfair choices (b) balance the data (c) add varied examples (d) ask different people
  35. 35.Which of these is a prediction? (a) old emails marked spam (b) what training builds (c) labelled fruit photos (d) tomorrow’s rain guess
  36. 36.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
  37. 37.🛠️ Does this reduce bias or add to it: ask different people? (a) reduces bias (b) adds to bias
  38. 38.🛠️ Which does this job need: counting the words in an essay? (a) a simple rule is enough (b) needs AI (learning)
  39. 39.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
  40. 40.Which of these is a prediction? (a) old emails marked spam (b) labelled fruit photos (c) the learned patterns (d) naming a new fruit photo

Answer key

  1. makes something new
  2. training data
  3. True
  4. True
  5. True
  6. reduces bias
  7. predicting rain
  8. ask different people
  9. spotting a sick leaf
  10. False
  11. testing on training data
  12. False
  13. copies of one photo
  14. does exactly as coded
  15. labels added at random
  16. False
  17. False
  18. adds to bias
  19. True
  20. balance the data
  21. a hallucination
  22. False
  23. uses rules people wrote
  24. True
  25. no
  26. False
  27. better at its task
  28. one-sided data
  29. sorts or predicts
  30. True
  31. True
  32. photos grouped by face
  33. recommending
  34. copy old unfair choices
  35. tomorrow’s rain guess
  36. good for training
  37. reduces bias
  38. a simple rule is enough
  39. spam
  40. naming a new fruit photo

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