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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 714 · 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: “500 labelled leaf photos” is a prediction. (a) False (b) True
  2. 2.True or false: “labels added at random” is good for training a model. (a) True (b) False
  3. 3.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  4. 4.🔍 Which is the odd one out? (a) voice typing (b) an app translating signs (c) a doorbell (d) an image generator
  5. 5.💬 What do we call the request you type to a generative AI? (a) a battery (b) a prompt (c) a label (d) a password
  6. 6.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
  7. 7.🏠 AI or not AI: a digital watch? (a) uses AI (b) not AI
  8. 8.📦 What happens during training? (a) it takes photos (b) it charges up (c) it prints labels (d) it learns patterns
  9. 9.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
  10. 10.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
  11. 11.🤖 Who builds and trains AI systems? (a) nobody at all (b) the weather (c) the Moon (d) people
  12. 12.True or false: “a poster of a rainforest” asks for an image. (a) False (b) True
  13. 13.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
  14. 14.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  15. 15.True or false: “the trained program” is a prediction. (a) True (b) False
  16. 16.True or false: “fix wrong labels” helps reduce bias. (a) True (b) False
  17. 17.🛠️ Which does this job need: adding up the marks on a report card? (a) a simple rule is enough (b) needs AI (learning)
  18. 18.🔍 Which is the odd one out? (a) photos grouped by face (b) face unlock on a phone (c) video suggestions (d) a microwave timer
  19. 19.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) training data (c) a prediction
  20. 20.🔍 Which is the odd one out? (a) copy old unfair choices (b) check who is missing (c) ignore complaints (d) test on one group only
  21. 21.True or false: “AI can make up facts” is a myth about AI. (a) True (b) False
  22. 22.Which of these is generative AI (it makes something new)? (a) suggesting a video (b) unlocking with a face (c) reading number plates (d) inventing a recipe
  23. 23.Which of these is good for training a model? (a) very few examples (b) many varied examples (c) copies of one photo (d) blurry, unclear photos
  24. 24.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) False (b) True
  25. 25.True or false: “AI learns from data” is a myth about AI. (a) False (b) True
  26. 26.⏩ In machine learning, which comes first: the learned patterns or naming a new leaf? (a) the learned patterns (b) naming a new leaf
  27. 27.True or false: “follows fixed rules” describes how AI works. (a) True (b) False
  28. 28.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
  29. 29.🎨 This prompt asks a generative AI for: a summary of chapter 3. What kind of output is that? (a) text (b) an image (c) music or sound
  30. 30.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  31. 31.True or false: “improves with more data” describes how a normal program works. (a) True (b) False
  32. 32.✅ Is this a real check of an AI answer: see if the book exists? (a) a real check (b) not a real check
  33. 33.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) a prediction (c) training data
  34. 34.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
  35. 35.✨ Generative or not: writing a new poem? (a) makes something new (b) sorts or predicts
  36. 36.⏩ In machine learning, which comes first: naming a new fruit photo or 500 labelled leaf photos? (a) naming a new fruit photo (b) 500 labelled leaf photos
  37. 37.🏠 AI or not AI: a stopwatch? (a) not AI (b) uses AI
  38. 38.🛠️ Does this reduce bias or add to it: copy old unfair choices? (a) adds to bias (b) reduces bias
  39. 39.⏩ In machine learning, which comes first: typing a new spoken word or what training builds? (a) typing a new spoken word (b) what training builds
  40. 40.True or false: “checking labels twice” is good for training a model. (a) True (b) False

Answer key

  1. False
  2. False
  3. reduces bias
  4. a doorbell
  5. a prompt
  6. False
  7. not AI
  8. it learns patterns
  9. True
  10. bad for training
  11. people
  12. True
  13. False
  14. False
  15. False
  16. True
  17. a simple rule is enough
  18. a microwave timer
  19. training data
  20. check who is missing
  21. False
  22. inventing a recipe
  23. many varied examples
  24. False
  25. False
  26. the learned patterns
  27. False
  28. True
  29. text
  30. False
  31. False
  32. a real check
  33. a prediction
  34. good for training
  35. makes something new
  36. 500 labelled leaf photos
  37. not AI
  38. adds to bias
  39. what training builds
  40. True

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