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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 42 · 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: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
  2. 2.Which of these is training data? (a) the learned patterns (b) the trained program (c) naming a new fruit photo (d) old emails marked spam
  3. 3.True or false: “examples of every type” is good for training a model. (a) False (b) True
  4. 4.🔍 Which is the odd one out? (a) an image generator (b) a voice assistant (c) face unlock on a phone (d) a torch
  5. 5.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  6. 6.🔍 Which is the odd one out? (a) photos in many lights (b) testing on training data (c) very few examples (d) copies of one photo
  7. 7.📱 What is the AI doing here: a phone unlocking when it sees your face? (a) recommending (b) predicting (c) generating (d) recognising
  8. 8.True or false: “flagging a new email” is training data. (a) True (b) False
  9. 9.🧐 Fact or myth: “AI can make up facts”? (a) a fact (b) a myth
  10. 10.✨ Generative or not: suggesting a video? (a) sorts or predicts (b) makes something new
  11. 11.Which of these is AI that sorts, spots or predicts (not generative)? (a) sorting photos by face (b) writing quiz questions (c) writing a new poem (d) making up a story
  12. 12.True or false: “naming a new fruit photo” is training data. (a) False (b) True
  13. 13.🤖 AI or a normal program: which one “spots patterns in data”? (a) a normal program (b) AI
  14. 14.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
  15. 15.True or false: “only one kind of example” is good for training a model. (a) False (b) True
  16. 16.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
  17. 17.🔁 What is the right order? (a) model, prediction, data (b) data, prediction, model (c) prediction, data, model (d) data, model, prediction
  18. 18.📱 What is the AI doing here: a shop guessing how many umbrellas will sell? (a) recommending (b) recognising (c) predicting (d) generating
  19. 19.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
  20. 20.Which of these is bad for training a model? (a) examples of every type (b) many varied examples (c) only one kind of example (d) new data for testing
  21. 21.🏠 AI or not AI: an AI chatbot? (a) not AI (b) uses AI
  22. 22.True or false: “new data for testing” is good for training a model. (a) False (b) True
  23. 23.True or false: a weather app guessing tomorrow’s rain is AI predicting something. (a) False (b) True
  24. 24.🔍 Which is the odd one out? (a) a digital watch (b) a calculator (c) an image generator (d) a fan regulator
  25. 25.Which of these is AI that sorts, spots or predicts (not generative)? (a) drawing a new picture (b) unlocking with a face (c) drafting a letter (d) inventing a recipe
  26. 26.⚖️ One-sided or balanced data: leaves from every season of the year? (a) balanced data (b) one-sided data
  27. 27.🔍 Which is the odd one out? (a) confident means correct (b) long answers are true (c) AI knows everything (d) AI can be wrong
  28. 28.True or false: “AI knows everything” is a fact about AI. (a) True (b) False
  29. 29.⏩ In machine learning, which comes first: what training builds or labelled fruit photos? (a) what training builds (b) labelled fruit photos
  30. 30.🧪 Why test a model on NEW examples? (a) to make it slower (b) to delete its data (c) to change its name (d) to see if it works
  31. 31.True or false: “recorded spoken words” is the model. (a) False (b) True
  32. 32.🛠️ What helps make an AI system fairer? (a) fewer tests (b) more varied data (c) ignoring mistakes (d) one group only
  33. 33.True or false: “handles unseen examples” describes how a normal program works. (a) True (b) False
  34. 34.Which of these is not AI? (a) photos grouped by face (b) an AI chatbot (c) a map predicting traffic (d) a microwave timer
  35. 35.Which of these adds to bias? (a) skip testing (b) check who is missing (c) add varied examples (d) balance the data
  36. 36.True or false: “AI can make up facts” is a fact about AI. (a) True (b) False
  37. 37.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
  38. 38.🔍 Which is the odd one out? (a) a kitchen weighing scale (b) face unlock on a phone (c) a light switch (d) a digital watch
  39. 39.True or false: “the learned patterns” is a prediction. (a) True (b) False
  40. 40.True or false: ringing the school bell at 9 am needs AI that learns from examples. (a) True (b) False

Answer key

  1. False
  2. old emails marked spam
  3. True
  4. a torch
  5. False
  6. photos in many lights
  7. recognising
  8. False
  9. a fact
  10. sorts or predicts
  11. sorting photos by face
  12. False
  13. AI
  14. False
  15. False
  16. spam
  17. data, model, prediction
  18. predicting
  19. bad for training
  20. only one kind of example
  21. uses AI
  22. True
  23. True
  24. an image generator
  25. unlocking with a face
  26. balanced data
  27. AI can be wrong
  28. False
  29. labelled fruit photos
  30. to see if it works
  31. False
  32. more varied data
  33. False
  34. a microwave timer
  35. skip testing
  36. True
  37. False
  38. face unlock on a phone
  39. False
  40. False

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