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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 14 · 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.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam
  2. 2.🤖 AI or a normal program: which one “same steps every time”? (a) AI (b) a normal program
  3. 3.True or false: “500 labelled leaf photos” is a prediction. (a) False (b) True
  4. 4.🔍 Which is the odd one out? (a) add more of the same (b) add varied examples (c) copy old unfair choices (d) skip testing
  5. 5.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
  6. 6.Which of these is bad for training a model? (a) photos in many lights (b) examples of every type (c) labels added at random (d) many varied examples
  7. 7.True or false: counting the words in an essay only needs a simple fixed rule. (a) True (b) False
  8. 8.🧐 Fact or myth: “AI learns from data”? (a) a myth (b) a fact
  9. 9.Which of these is not AI? (a) a torch (b) an app translating signs (c) an image generator (d) a voice assistant
  10. 10.🛠️ Which does this job need: suggesting the next word as you type? (a) needs AI (learning) (b) a simple rule is enough
  11. 11.🔁 Training data, the model or a prediction: “the trained program”? (a) training data (b) the model (c) a prediction
  12. 12.🛠️ What helps make an AI system fairer? (a) one group only (b) ignoring mistakes (c) more varied data (d) fewer tests
  13. 13.True or false: “redo the maths yourself” is a real way to check an AI answer. (a) False (b) True
  14. 14.True or false: “past weather records” is a prediction. (a) False (b) True
  15. 15.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) balanced data (b) one-sided data
  16. 16.⚖️ One-sided or balanced data: stories where every doctor is a man? (a) one-sided data (b) balanced data
  17. 17.🔍 Which is the odd one out? (a) confident means correct (b) AI can mix up numbers (c) AI has real feelings (d) AI is never wrong
  18. 18.True or false: a phone unlocking when it sees your face is AI recognising something. (a) False (b) True
  19. 19.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) training data (c) a prediction
  20. 20.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a song about dogs (c) a list of rules only (d) one single photo
  21. 21.🤖 AI or a normal program: which one “learns from examples”? (a) AI (b) a normal program
  22. 22.🛠️ Does this reduce bias or add to it: use one group only? (a) adds to bias (b) reduces bias
  23. 23.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) True (b) False
  24. 24.🛠️ Does this reduce bias or add to it: add varied examples? (a) adds to bias (b) reduces bias
  25. 25.🔍 Which is the odd one out? (a) AI can make up facts (b) AI has real feelings (c) AI is never wrong (d) AI knows everything
  26. 26.🏷️ 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
  27. 27.🛠️ Which does this job need: finding 10% of a price? (a) a simple rule is enough (b) needs AI (learning)
  28. 28.🏠 AI or not AI: a fan regulator? (a) uses AI (b) not AI
  29. 29.True or false: a book app suggesting a story like your last one is AI recognising something. (a) False (b) True
  30. 30.🏠 AI or not AI: an AI chatbot? (a) not AI (b) uses AI
  31. 31.Which of these helps reduce bias? (a) test on one group only (b) copy old unfair choices (c) skip testing (d) test on many groups
  32. 32.🔍 Which is the odd one out? (a) AI answers need checking (b) AI always checks facts (c) AI can mix up numbers (d) AI can be wrong
  33. 33.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
  34. 34.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
  35. 35.🔁 What is the right order? (a) prediction, data, model (b) model, prediction, data (c) data, prediction, model (d) data, model, prediction
  36. 36.True or false: a voice app trained only on adult voices is balanced data (fairer for everyone). (a) True (b) False
  37. 37.🤖 What does AI stand for? (a) automatic internet (b) app installer (c) actual information (d) artificial intelligence
  38. 38.Which of these is a myth about AI? (a) AI can make up facts (b) AI knows everything (c) AI can mix up numbers (d) AI answers need checking
  39. 39.🔍 Which is the odd one out? (a) testing on training data (b) correct labels (c) only one kind of example (d) wrong labels
  40. 40.🔍 Which is the odd one out? (a) AI copies data patterns (b) AI has real feelings (c) AI answers need checking (d) AI learns from data

Answer key

  1. not spam
  2. a normal program
  3. False
  4. add varied examples
  5. bad for training
  6. labels added at random
  7. True
  8. a fact
  9. a torch
  10. needs AI (learning)
  11. the model
  12. more varied data
  13. True
  14. False
  15. balanced data
  16. one-sided data
  17. AI can mix up numbers
  18. True
  19. training data
  20. many labelled photos
  21. AI
  22. adds to bias
  23. False
  24. reduces bias
  25. AI can make up facts
  26. spam
  27. a simple rule is enough
  28. not AI
  29. False
  30. uses AI
  31. test on many groups
  32. AI always checks facts
  33. spam
  34. bad for training
  35. data, model, prediction
  36. False
  37. artificial intelligence
  38. AI knows everything
  39. correct labels
  40. AI has real feelings

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