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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 682 · 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.🤖 AI or a normal program: which one “follows fixed rules”? (a) AI (b) a normal program
  2. 2.🏷️ 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
  3. 3.🤖 Does an AI system think and feel like a person? (a) only on weekends (b) no, it finds patterns (c) yes, it gets sad (d) yes, just like us
  4. 4.📱 What is the AI doing here: an AI tool making a brand-new tune? (a) recommending (b) recognising (c) predicting (d) generating
  5. 5.🧐 Fact or myth: “long answers are true”? (a) a myth (b) a fact
  6. 6.True or false: “never learns from data” describes how a normal program works. (a) False (b) True
  7. 7.🤖 AI or a normal program: which one “does exactly as coded”? (a) a normal program (b) AI
  8. 8.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) one single photo (c) a song about dogs (d) many labelled photos
  9. 9.True or false: “many varied examples” is good for training a model. (a) False (b) True
  10. 10.🏠 AI or not AI: an app translating signs? (a) uses AI (b) not AI
  11. 11.True or false: leaves from every season of the year is balanced data (fairer for everyone). (a) False (b) True
  12. 12.True or false: “wrong labels” is bad for training a model. (a) False (b) True
  13. 13.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  14. 14.🛠️ Which does this job need: telling a cat photo from a dog photo? (a) needs AI (learning) (b) a simple rule is enough
  15. 15.🧐 Fact or myth: “AI always checks facts”? (a) a fact (b) a myth
  16. 16.Which of these helps reduce bias? (a) ignore complaints (b) fix wrong labels (c) copy old unfair choices (d) use one group only
  17. 17.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) True (b) False
  18. 18.True or false: “copy old unfair choices” helps reduce bias. (a) True (b) False
  19. 19.🔍 Which is the odd one out? (a) only one kind of example (b) labels added at random (c) copies of one photo (d) examples of every type
  20. 20.🔁 What is the right order? (a) prediction, data, model (b) data, model, prediction (c) data, prediction, model (d) model, prediction, data
  21. 21.True or false: “AI can be wrong” is a myth about AI. (a) False (b) True
  22. 22.🤖 Who builds and trains AI systems? (a) the Moon (b) the weather (c) nobody at all (d) people
  23. 23.🔍 Which is the odd one out? (a) new data for testing (b) clear, sharp examples (c) photos in many lights (d) blurry, unclear photos
  24. 24.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the price (d) the right answer tag
  25. 25.True or false: a phone unlocking when it sees your face is AI recommending something. (a) True (b) False
  26. 26.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) the model (c) a prediction
  27. 27.Which of these is a fact about AI? (a) AI has real feelings (b) AI can make up facts (c) AI is never wrong (d) long answers are true
  28. 28.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
  29. 29.🧐 Fact or myth: “AI knows everything”? (a) a fact (b) a myth
  30. 30.🛠️ Which does this job need: translating a sentence into Hindi? (a) needs AI (learning) (b) a simple rule is enough
  31. 31.📺 A video app suggests what to watch next. What is the AI doing? (a) charging (b) recommending (c) printing (d) cooking
  32. 32.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) the model (c) a prediction
  33. 33.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) a prediction (c) training data
  34. 34.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
  35. 35.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  36. 36.Which of these is generative AI (it makes something new)? (a) reading number plates (b) drawing a new picture (c) spotting spam (d) sorting photos by face
  37. 37.🔍 Which is the odd one out? (a) reading number plates (b) unlocking with a face (c) turning speech to text (d) drafting a letter
  38. 38.🧪 Why test a model on NEW examples? (a) to delete its data (b) to make it slower (c) to change its name (d) to see if it works
  39. 39.🔁 Training data, the model or a prediction: “the trained program”? (a) the model (b) training data (c) a prediction
  40. 40.🔍 Which is the odd one out? (a) spotting spam (b) making up a story (c) drafting a letter (d) writing quiz questions

Answer key

  1. a normal program
  2. spam
  3. no, it finds patterns
  4. generating
  5. a myth
  6. True
  7. a normal program
  8. many labelled photos
  9. True
  10. uses AI
  11. True
  12. True
  13. one-sided data
  14. needs AI (learning)
  15. a myth
  16. fix wrong labels
  17. False
  18. False
  19. examples of every type
  20. data, model, prediction
  21. False
  22. people
  23. blurry, unclear photos
  24. the right answer tag
  25. False
  26. the model
  27. AI can make up facts
  28. False
  29. a myth
  30. needs AI (learning)
  31. recommending
  32. a prediction
  33. training data
  34. True
  35. reduces bias
  36. drawing a new picture
  37. drafting a letter
  38. to see if it works
  39. the model
  40. spotting spam

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