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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 381 · 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: “follows fixed rules” describes how AI works. (a) True (b) False
  2. 2.Which of these is AI that sorts, spots or predicts (not generative)? (a) inventing a recipe (b) writing a new poem (c) drafting a letter (d) spotting spam
  3. 3.✨ Generative or not: writing quiz questions? (a) makes something new (b) sorts or predicts
  4. 4.True or false: “a soft lullaby melody” asks for music or sound. (a) True (b) False
  5. 5.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
  6. 6.True or false: “a cartoon of a dancing elephant” asks for an image. (a) False (b) True
  7. 7.🔍 Which is the odd one out? (a) test on many groups (b) ask different people (c) check who is missing (d) add more of the same
  8. 8.Which of these adds to bias? (a) ask different people (b) check who is missing (c) fix wrong labels (d) ignore complaints
  9. 9.🛠️ Does this reduce bias or add to it: balance the data? (a) adds to bias (b) reduces bias
  10. 10.Which of these describes how a normal program works? (a) spots patterns in data (b) makes a best guess (c) improves with more data (d) one set rule per case
  11. 11.🔁 Training data, the model or a prediction: “the learned patterns”? (a) the model (b) training data (c) a prediction
  12. 12.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  13. 13.✨ Generative or not: inventing a recipe? (a) makes something new (b) sorts or predicts
  14. 14.Which of these is a prediction? (a) typing a new spoken word (b) the trained program (c) old emails marked spam (d) 500 labelled leaf photos
  15. 15.⏩ In machine learning, which comes first: naming a new fruit photo or the learned patterns? (a) naming a new fruit photo (b) the learned patterns
  16. 16.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
  17. 17.🔍 Which is the odd one out? (a) spotting spam (b) spotting a sick leaf (c) drafting a letter (d) unlocking with a face
  18. 18.True or false: “the learned patterns” is the model. (a) True (b) False
  19. 19.Which of these is a myth about AI? (a) AI learns from data (b) AI can make up facts (c) AI is never wrong (d) AI can mix up numbers
  20. 20.True or false: a map app guessing how long a trip will take is AI recommending something. (a) True (b) False
  21. 21.True or false: “AI is never wrong” is a myth about AI. (a) False (b) True
  22. 22.🤖 AI or a normal program: which one “handles unseen examples”? (a) a normal program (b) AI
  23. 23.✍️ How does an AI chatbot write its replies? (a) predicts likely words (b) asks a person (c) uses magic (d) copies one website
  24. 24.🤖 AI or a normal program: which one “gives a likely answer”? (a) a normal program (b) AI
  25. 25.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
  26. 26.🛠️ Which does this job need: recognising a friend’s face in photos? (a) a simple rule is enough (b) needs AI (learning)
  27. 27.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
  28. 28.🧐 Fact or myth: “AI can be wrong”? (a) a fact (b) a myth
  29. 29.Which of these is bad for training a model? (a) photos in many lights (b) new data for testing (c) checking labels twice (d) only one kind of example
  30. 30.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  31. 31.Which of these uses AI? (a) a stopwatch (b) a digital watch (c) a map predicting traffic (d) a printed timetable
  32. 32.🔍 Which is the odd one out? (a) correct labels (b) testing on training data (c) photos in many lights (d) examples of every type
  33. 33.✨ Generative or not: making up a story? (a) makes something new (b) sorts or predicts
  34. 34.🧐 Fact or myth: “long answers are true”? (a) a myth (b) a fact
  35. 35.🛠️ Which does this job need: spotting spam emails by their patterns? (a) a simple rule is enough (b) needs AI (learning)
  36. 36.✨ Generative or not: unlocking with a face? (a) makes something new (b) sorts or predicts
  37. 37.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam
  38. 38.Which of these is good for training a model? (a) photos in many lights (b) testing on training data (c) labels added at random (d) very few examples
  39. 39.True or false: “a thank-you note to the bus driver” asks for text. (a) True (b) False
  40. 40.True or false: “ask different people” adds to bias. (a) False (b) True

Answer key

  1. False
  2. spotting spam
  3. makes something new
  4. True
  5. bad for training
  6. True
  7. add more of the same
  8. ignore complaints
  9. reduces bias
  10. one set rule per case
  11. the model
  12. False
  13. makes something new
  14. typing a new spoken word
  15. the learned patterns
  16. not spam
  17. drafting a letter
  18. True
  19. AI is never wrong
  20. False
  21. True
  22. AI
  23. predicts likely words
  24. AI
  25. spam
  26. needs AI (learning)
  27. good for training
  28. a fact
  29. only one kind of example
  30. True
  31. a map predicting traffic
  32. testing on training data
  33. makes something new
  34. a myth
  35. needs AI (learning)
  36. sorts or predicts
  37. not spam
  38. photos in many lights
  39. True
  40. False

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