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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 635 · 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.🔍 Which is the odd one out? (a) wrong labels (b) blurry, unclear photos (c) many varied examples (d) very few examples
  2. 2.🛠️ Which does this job need: recognising a bird from its song? (a) needs AI (learning) (b) a simple rule is enough
  3. 3.Which of these uses AI? (a) a digital watch (b) a doorbell (c) a light switch (d) an app translating signs
  4. 4.Which of these is training data? (a) 500 labelled leaf photos (b) typing a new spoken word (c) naming a new fruit photo (d) the trained program
  5. 5.True or false: “what training builds” is the model. (a) False (b) True
  6. 6.⏩ In machine learning, which comes first: tomorrow’s rain guess or the learned patterns? (a) tomorrow’s rain guess (b) the learned patterns
  7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
  8. 8.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) a prediction (b) the model (c) training data
  9. 9.🔍 Which is the odd one out? (a) a voice assistant (b) an image generator (c) a doorbell (d) a map predicting traffic
  10. 10.True or false: “labelled fruit photos” is training data. (a) False (b) True
  11. 11.🎨 This prompt asks a generative AI for: a cheerful tune for Sports Day. What kind of output is that? (a) text (b) an image (c) music or sound
  12. 12.⚖️ One-sided or balanced data: a fruit sorter shown only ripe mangoes? (a) balanced data (b) one-sided data
  13. 13.True or false: “AI is never wrong” is a fact about AI. (a) True (b) False
  14. 14.True or false: “labelled fruit photos” is the model. (a) True (b) False
  15. 15.✨ Generative or not: unlocking with a face? (a) makes something new (b) sorts or predicts
  16. 16.🏠 AI or not AI: a calculator? (a) not AI (b) uses AI
  17. 17.True or false: an online shop showing “you may also like” is AI predicting something. (a) False (b) True
  18. 18.⚖️ Where does bias in an AI system usually come from? (a) its screen size (b) its training data (c) the weather (d) its colour
  19. 19.True or false: “skip testing” helps reduce bias. (a) False (b) True
  20. 20.True or false: “never learns from data” describes how AI works. (a) True (b) False
  21. 21.🤖 Does an AI system think and feel like a person? (a) yes, just like us (b) yes, it gets sad (c) no, it finds patterns (d) only on weekends
  22. 22.True or false: “a drawing of a cat on the Moon” asks for text. (a) True (b) False
  23. 23.🤔 An AI answer sounds very confident. Does that mean it is right? (a) yes, if it is long (b) no, check it anyway (c) yes, always
  24. 24.📅 An AI gives a date for a history event. How can you check? (a) pick a lucky number (b) guess (c) look in your textbook (d) ask it to repeat
  25. 25.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  26. 26.🧐 Fact or myth: “AI is never wrong”? (a) a fact (b) a myth
  27. 27.Which of these is good for training a model? (a) very few examples (b) wrong labels (c) blurry, unclear photos (d) photos in many lights
  28. 28.Which of these is not AI? (a) face unlock on a phone (b) video suggestions (c) a torch (d) an AI chatbot
  29. 29.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) the model (c) training data
  30. 30.🔍 Which is the odd one out? (a) check who is missing (b) add varied examples (c) ignore complaints (d) balance the data
  31. 31.🛠️ Which does this job need: turning spoken words into typed text? (a) needs AI (learning) (b) a simple rule is enough
  32. 32.True or false: “labels added at random” is bad for training a model. (a) False (b) True
  33. 33.Which of these is generative AI (it makes something new)? (a) spotting spam (b) composing a new tune (c) reading number plates (d) predicting rain
  34. 34.🐶 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
  35. 35.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) training data (c) the model
  36. 36.Which of these uses AI? (a) an AI chatbot (b) a stopwatch (c) a digital watch (d) a fan regulator
  37. 37.Which of these helps reduce bias? (a) test on one group only (b) fix wrong labels (c) copy old unfair choices (d) use one group only
  38. 38.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) better at its task (d) slower to switch on
  39. 39.🔍 Which is the odd one out? (a) a calculator (b) a voice assistant (c) a kitchen weighing scale (d) a doorbell
  40. 40.📱 What is the AI doing here: a weather app guessing tomorrow’s rain? (a) generating (b) predicting (c) recognising (d) recommending

Answer key

  1. many varied examples
  2. needs AI (learning)
  3. an app translating signs
  4. 500 labelled leaf photos
  5. True
  6. the learned patterns
  7. spam
  8. training data
  9. a doorbell
  10. True
  11. music or sound
  12. one-sided data
  13. False
  14. False
  15. sorts or predicts
  16. not AI
  17. False
  18. its training data
  19. False
  20. False
  21. no, it finds patterns
  22. False
  23. no, check it anyway
  24. look in your textbook
  25. reduces bias
  26. a myth
  27. photos in many lights
  28. a torch
  29. training data
  30. ignore complaints
  31. needs AI (learning)
  32. True
  33. composing a new tune
  34. many labelled photos
  35. a prediction
  36. an AI chatbot
  37. fix wrong labels
  38. better at its task
  39. a voice assistant
  40. predicting

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