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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 269 · 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: “a cartoon of a dancing elephant” asks for text. (a) True (b) False
  2. 2.🔁 Training data, the model or a prediction: “what training builds”? (a) a prediction (b) the model (c) training data
  3. 3.True or false: “naming a new fruit photo” is the model. (a) False (b) True
  4. 4.Which of these uses AI? (a) a voice assistant (b) a torch (c) a microwave timer (d) a light switch
  5. 5.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
  6. 6.Which of these is AI that sorts, spots or predicts (not generative)? (a) writing a new poem (b) sorting photos by face (c) writing quiz questions (d) inventing a recipe
  7. 7.Which of these describes how a normal program works? (a) follows fixed rules (b) improves with more data (c) handles unseen examples (d) makes a best guess
  8. 8.True or false: “500 labelled leaf photos” is training data. (a) False (b) True
  9. 9.Which of these uses AI? (a) a fan regulator (b) a microwave timer (c) an AI chatbot (d) a doorbell
  10. 10.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
  11. 11.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) a list of rules only (d) many labelled photos
  12. 12.🔍 Which is the odd one out? (a) copy old unfair choices (b) test on many groups (c) skip testing (d) use one group only
  13. 13.True or false: an image generator drawing a robot from a sentence is AI recognising something. (a) False (b) True
  14. 14.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) spam (b) not spam
  15. 15.🏷️ 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
  16. 16.Which of these is training data? (a) typing a new spoken word (b) recorded spoken words (c) what training builds (d) flagging a new email
  17. 17.🌍 An AI chatbot says the Earth has two moons. Your textbook says one. What now? (a) share it online (b) trust the textbook (c) stop studying (d) trust the AI
  18. 18.🔮 A trained model sees a new photo and says “mango”. This is… (a) training data (b) a password (c) a label error (d) a prediction
  19. 19.🔍 Which is the odd one out? (a) a torch (b) a printed timetable (c) a microwave timer (d) an AI chatbot
  20. 20.🔍 Which is the odd one out? (a) a kitchen weighing scale (b) an AI chatbot (c) an image generator (d) voice typing
  21. 21.Which of these is a prediction? (a) old emails marked spam (b) flagging a new email (c) recorded spoken words (d) past weather records
  22. 22.🛠️ Which does this job need: adding up the marks on a report card? (a) a simple rule is enough (b) needs AI (learning)
  23. 23.True or false: mangoes both raw and ripe is one-sided data (likely to make a biased model). (a) True (b) False
  24. 24.🔍 Which is the odd one out? (a) wrong labels (b) very few examples (c) photos in many lights (d) only one kind of example
  25. 25.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
  26. 26.True or false: “AI has real feelings” is a myth about AI. (a) True (b) False
  27. 27.🔍 Which is the odd one out? (a) use one group only (b) skip testing (c) copy old unfair choices (d) balance the data
  28. 28.True or false: “same steps every time” describes how AI works. (a) True (b) False
  29. 29.Which of these is the model? (a) past weather records (b) the learned patterns (c) tomorrow’s rain guess (d) flagging a new email
  30. 30.Which of these uses AI? (a) a printed timetable (b) a map predicting traffic (c) a microwave timer (d) a digital watch
  31. 31.Which of these describes how AI works? (a) never learns from data (b) one set rule per case (c) follows fixed rules (d) makes a best guess
  32. 32.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) children speak too fast (c) the phone was new (d) few child voices in data
  33. 33.True or false: “improves with more data” describes how AI works. (a) True (b) False
  34. 34.True or false: “handles unseen examples” describes how AI works. (a) False (b) True
  35. 35.True or false: finding 10% of a price needs AI that learns from examples. (a) True (b) False
  36. 36.Which of these is good for training a model? (a) many varied examples (b) only one kind of example (c) very few examples (d) testing on training data
  37. 37.True or false: a phone unlocking when it sees your face is AI recognising something. (a) True (b) False
  38. 38.🤖 AI or a normal program: which one “spots patterns in data”? (a) a normal program (b) AI
  39. 39.Which of these adds to bias? (a) ask different people (b) fix wrong labels (c) skip testing (d) check who is missing
  40. 40.🧪 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

Answer key

  1. False
  2. the model
  3. False
  4. a voice assistant
  5. False
  6. sorting photos by face
  7. follows fixed rules
  8. True
  9. an AI chatbot
  10. good for training
  11. many labelled photos
  12. test on many groups
  13. False
  14. not spam
  15. spam
  16. recorded spoken words
  17. trust the textbook
  18. a prediction
  19. an AI chatbot
  20. a kitchen weighing scale
  21. flagging a new email
  22. a simple rule is enough
  23. False
  24. photos in many lights
  25. False
  26. True
  27. balance the data
  28. False
  29. the learned patterns
  30. a map predicting traffic
  31. makes a best guess
  32. few child voices in data
  33. True
  34. True
  35. False
  36. many varied examples
  37. True
  38. AI
  39. skip testing
  40. to see if it works

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