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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 103 · 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 of these uses AI? (a) a kitchen weighing scale (b) a calculator (c) a spam filter (d) a digital watch
  2. 2.Which of these is the model? (a) recorded spoken words (b) labelled fruit photos (c) typing a new spoken word (d) what training builds
  3. 3.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
  4. 4.True or false: “wrong labels” is good for training a model. (a) False (b) True
  5. 5.🔍 Which is the odd one out? (a) a calculator (b) a printed timetable (c) a fan regulator (d) an image generator
  6. 6.⏩ In machine learning, which comes first: the trained program or labelled fruit photos? (a) the trained program (b) labelled fruit photos
  7. 7.🧐 Fact or myth: “confident means correct”? (a) a fact (b) a myth
  8. 8.True or false: “AI answers need checking” is a myth about AI. (a) False (b) True
  9. 9.🔁 What is the right order? (a) data, model, prediction (b) data, prediction, model (c) model, prediction, data (d) prediction, data, model
  10. 10.🔍 Which is the odd one out? (a) an AI chatbot (b) a voice assistant (c) voice typing (d) a doorbell
  11. 11.Which of these is training data? (a) the learned patterns (b) flagging a new email (c) old emails marked spam (d) what training builds
  12. 12.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
  13. 13.True or false: spotting spam emails by their patterns only needs a simple fixed rule. (a) False (b) True
  14. 14.True or false: an AI chatbot writing a poem about rain is AI recommending something. (a) False (b) True
  15. 15.Which of these adds to bias? (a) check who is missing (b) use one group only (c) fix wrong labels (d) balance the data
  16. 16.🤖 What does AI stand for? (a) automatic internet (b) actual information (c) app installer (d) artificial intelligence
  17. 17.🔍 Which is the odd one out? (a) photos in many lights (b) correct labels (c) copies of one photo (d) many varied examples
  18. 18.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) a prediction (c) the model
  19. 19.Which of these describes how a normal program works? (a) spots patterns in data (b) learns from examples (c) makes a best guess (d) does exactly as coded
  20. 20.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it will turn yellow (d) it may get it wrong
  21. 21.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
  22. 22.Which of these is AI that sorts, spots or predicts (not generative)? (a) drafting a letter (b) reading number plates (c) inventing a recipe (d) drawing a new picture
  23. 23.📱 What is the AI doing here: a phone unlocking when it sees your face? (a) predicting (b) generating (c) recommending (d) recognising
  24. 24.True or false: “recorded spoken words” is training data. (a) False (b) True
  25. 25.Which of these is good for training a model? (a) copies of one photo (b) very few examples (c) wrong labels (d) many varied examples
  26. 26.🎨 This prompt asks a generative AI for: a soft lullaby melody. What kind of output is that? (a) text (b) music or sound (c) an image
  27. 27.True or false: “gives a likely answer” describes how a normal program works. (a) True (b) False
  28. 28.🗣️ A voice app understands adults but not children. What is the likely reason? (a) few child voices in data (b) it was a rainy day (c) children speak too fast (d) the phone was new
  29. 29.True or false: adding up the marks on a report card needs AI that learns from examples. (a) True (b) False
  30. 30.Which of these is AI that sorts, spots or predicts (not generative)? (a) predicting rain (b) drafting a letter (c) composing a new tune (d) making up a story
  31. 31.🔍 Which is the odd one out? (a) many varied examples (b) blurry, unclear photos (c) very few examples (d) copies of one photo
  32. 32.📱 What is the AI doing here: an AI chatbot writing a poem about rain? (a) recognising (b) generating (c) predicting (d) recommending
  33. 33.Which of these describes how a normal program works? (a) spots patterns in data (b) one set rule per case (c) improves with more data (d) makes a best guess
  34. 34.True or false: mangoes both raw and ripe is balanced data (fairer for everyone). (a) True (b) False
  35. 35.True or false: “AI copies data patterns” is a fact about AI. (a) False (b) True
  36. 36.🔍 Which is the odd one out? (a) drawing a new picture (b) inventing a recipe (c) predicting rain (d) writing quiz questions
  37. 37.Which of these is not AI? (a) face unlock on a phone (b) a voice assistant (c) a light switch (d) a spam filter
  38. 38.📺 A video app suggests what to watch next. What is the AI doing? (a) printing (b) cooking (c) charging (d) recommending
  39. 39.Which of these is AI that sorts, spots or predicts (not generative)? (a) spotting spam (b) drafting a letter (c) creating a cartoon (d) writing a new poem
  40. 40.Which of these is the model? (a) naming a new fruit photo (b) recorded spoken words (c) naming a new leaf (d) the trained program

Answer key

  1. a spam filter
  2. what training builds
  3. False
  4. False
  5. an image generator
  6. labelled fruit photos
  7. a myth
  8. False
  9. data, model, prediction
  10. a doorbell
  11. old emails marked spam
  12. False
  13. False
  14. False
  15. use one group only
  16. artificial intelligence
  17. copies of one photo
  18. a prediction
  19. does exactly as coded
  20. it may get it wrong
  21. bad for training
  22. reading number plates
  23. recognising
  24. True
  25. many varied examples
  26. music or sound
  27. False
  28. few child voices in data
  29. False
  30. predicting rain
  31. many varied examples
  32. generating
  33. one set rule per case
  34. True
  35. True
  36. predicting rain
  37. a light switch
  38. recommending
  39. spotting spam
  40. the trained program

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