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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 36 · 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.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
  2. 2.🤖 Does an AI system think and feel like a person? (a) yes, just like us (b) yes, it gets sad (c) no, it finds patterns (d) only on weekends
  3. 3.True or false: “recorded spoken words” is training data. (a) False (b) True
  4. 4.True or false: “find a trusted source” is NOT a real way to check an AI answer. (a) False (b) True
  5. 5.True or false: stories where every doctor is a man is one-sided data (likely to make a biased model). (a) False (b) True
  6. 6.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
  7. 7.True or false: “asking the same AI again” is a real way to check an AI answer. (a) False (b) True
  8. 8.True or false: “makes a best guess” describes how AI works. (a) True (b) False
  9. 9.True or false: translating a sentence into Hindi only needs a simple fixed rule. (a) False (b) True
  10. 10.Which of these is bad for training a model? (a) many varied examples (b) only one kind of example (c) examples of every type (d) new data for testing
  11. 11.🌍 An AI chatbot says the Earth has two moons. Your textbook says one. What now? (a) trust the textbook (b) trust the AI (c) stop studying (d) share it online
  12. 12.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
  13. 13.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
  14. 14.🤖 AI or a normal program: which one “uses rules people wrote”? (a) AI (b) a normal program
  15. 15.Which of these is AI that sorts, spots or predicts (not generative)? (a) drafting a letter (b) spotting spam (c) making up a story (d) writing a new poem
  16. 16.True or false: “500 labelled leaf photos” is training data. (a) True (b) False
  17. 17.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  18. 18.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  19. 19.🧮 Which of these is NOT AI? (a) face unlock (b) a basic calculator (c) voice typing (d) a spam filter
  20. 20.✨ Generative or not: composing a new tune? (a) makes something new (b) sorts or predicts
  21. 21.Which of these helps reduce bias? (a) copy old unfair choices (b) test on many groups (c) skip testing (d) add more of the same
  22. 22.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) blurry, unclear photos (d) checking labels twice
  23. 23.✨ Generative or not: making up a story? (a) sorts or predicts (b) makes something new
  24. 24.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  25. 25.Which of these is bad for training a model? (a) clear, sharp examples (b) examples of every type (c) new data for testing (d) wrong labels
  26. 26.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) a prediction (c) training data
  27. 27.True or false: telling a cat photo from a dog photo only needs a simple fixed rule. (a) False (b) True
  28. 28.Which of these is not AI? (a) a spam filter (b) an AI chatbot (c) an image generator (d) a light switch
  29. 29.🔍 Which is the odd one out? (a) test on many groups (b) fix wrong labels (c) copy old unfair choices (d) add varied examples
  30. 30.🔍 Which is the odd one out? (a) AI answers need checking (b) AI always checks facts (c) AI is never wrong (d) AI knows everything
  31. 31.📱 What is the AI doing here: an online shop showing “you may also like”? (a) recognising (b) generating (c) predicting (d) recommending
  32. 32.⏩ In machine learning, which comes first: 500 labelled leaf photos or naming a new leaf? (a) 500 labelled leaf photos (b) naming a new leaf
  33. 33.🏠 AI or not AI: a microwave timer? (a) uses AI (b) not AI
  34. 34.Which of these is bad for training a model? (a) checking labels twice (b) correct labels (c) testing on training data (d) photos in many lights
  35. 35.🧪 Why test a model on NEW examples? (a) to see if it works (b) to delete its data (c) to change its name (d) to make it slower
  36. 36.True or false: “500 labelled leaf photos” is the model. (a) False (b) True
  37. 37.True or false: “spots patterns in data” describes how a normal program works. (a) False (b) True
  38. 38.True or false: “add varied examples” helps reduce bias. (a) True (b) False
  39. 39.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
  40. 40.🛠️ Which does this job need: showing the time on a clock? (a) needs AI (learning) (b) a simple rule is enough

Answer key

  1. True
  2. no, it finds patterns
  3. True
  4. False
  5. True
  6. False
  7. False
  8. True
  9. False
  10. only one kind of example
  11. trust the textbook
  12. good for training
  13. False
  14. a normal program
  15. spotting spam
  16. True
  17. reduces bias
  18. reduces bias
  19. a basic calculator
  20. makes something new
  21. test on many groups
  22. blurry, unclear photos
  23. makes something new
  24. True
  25. wrong labels
  26. a prediction
  27. False
  28. a light switch
  29. copy old unfair choices
  30. AI answers need checking
  31. recommending
  32. 500 labelled leaf photos
  33. not AI
  34. testing on training data
  35. to see if it works
  36. False
  37. False
  38. True
  39. True
  40. a simple rule is enough

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