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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 742 · 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 describes how AI works? (a) uses rules people wrote (b) makes a best guess (c) never learns from data (d) follows fixed rules
  2. 2.True or false: “correct labels” is bad for training a model. (a) True (b) False
  3. 3.True or false: a camera app naming the flower you point at is AI predicting something. (a) False (b) True
  4. 4.⏩ In machine learning, which comes first: typing a new spoken word or the trained program? (a) typing a new spoken word (b) the trained program
  5. 5.⚖️ One-sided or balanced data: neat and messy writing from many people? (a) one-sided data (b) balanced data
  6. 6.🤖 AI or a normal program: which one “learns from examples”? (a) a normal program (b) AI
  7. 7.🧠 Machine learning is a way for computers to… (a) print pages (b) clean screens (c) charge faster (d) learn from data
  8. 8.🔁 What is the right order? (a) data, model, prediction (b) data, prediction, model (c) prediction, data, model (d) model, prediction, data
  9. 9.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) a prediction (c) the model
  10. 10.🛠️ Which does this job need: turning spoken words into typed text? (a) a simple rule is enough (b) needs AI (learning)
  11. 11.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
  12. 12.🧐 Fact or myth: “AI can mix up numbers”? (a) a myth (b) a fact
  13. 13.🗣️ A voice app understands adults but not children. What is the likely reason? (a) children speak too fast (b) the phone was new (c) it was a rainy day (d) few child voices in data
  14. 14.⏩ In machine learning, which comes first: the learned patterns or typing a new spoken word? (a) the learned patterns (b) typing a new spoken word
  15. 15.Which of these is a myth about AI? (a) AI answers need checking (b) AI can mix up numbers (c) AI can make up facts (d) confident means correct
  16. 16.True or false: “naming a new fruit photo” is the model. (a) False (b) True
  17. 17.🧐 Fact or myth: “AI can be wrong”? (a) a fact (b) a myth
  18. 18.True or false: “examples of every type” is good for training a model. (a) False (b) True
  19. 19.Which of these adds to bias? (a) balance the data (b) test on one group only (c) add varied examples (d) ask different people
  20. 20.📚 An AI chatbot names a book that does not exist. This is called… (a) a hallucination (b) a summary (c) a backup (d) a password
  21. 21.🛠️ Which does this job need: telling a cat photo from a dog photo? (a) a simple rule is enough (b) needs AI (learning)
  22. 22.⚖️ One-sided or balanced data: a voice app trained only on adult voices? (a) balanced data (b) one-sided data
  23. 23.✨ Generative or not: sorting photos by face? (a) sorts or predicts (b) makes something new
  24. 24.⚖️ Where does bias in an AI system usually come from? (a) its colour (b) its screen size (c) the weather (d) its training data
  25. 25.Which of these describes how a normal program works? (a) spots patterns in data (b) learns from examples (c) one set rule per case (d) handles unseen examples
  26. 26.🧐 Fact or myth: “AI always checks facts”? (a) a fact (b) a myth
  27. 27.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
  28. 28.✨ Generative or not: suggesting a video? (a) makes something new (b) sorts or predicts
  29. 29.Which of these is AI that sorts, spots or predicts (not generative)? (a) writing a new poem (b) creating a cartoon (c) drawing a new picture (d) unlocking with a face
  30. 30.🧐 Fact or myth: “long answers are true”? (a) a fact (b) a myth
  31. 31.Which of these is a fact about AI? (a) AI always checks facts (b) AI has real feelings (c) AI is never wrong (d) AI answers need checking
  32. 32.Which of these adds to bias? (a) test on many groups (b) fix wrong labels (c) balance the data (d) use one group only
  33. 33.🤖 AI or a normal program: which one “gives a likely answer”? (a) a normal program (b) AI
  34. 34.True or false: “learns from examples” describes how a normal program works. (a) False (b) True
  35. 35.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
  36. 36.🛠️ Does this reduce bias or add to it: test on many groups? (a) adds to bias (b) reduces bias
  37. 37.🔍 Which is the odd one out? (a) a microwave timer (b) a doorbell (c) an AI chatbot (d) a printed timetable
  38. 38.True or false: “the learned patterns” is a prediction. (a) False (b) True
  39. 39.Which of these is bad for training a model? (a) checking labels twice (b) correct labels (c) new data for testing (d) labels added at random
  40. 40.True or false: showing the time on a clock needs AI that learns from examples. (a) True (b) False

Answer key

  1. makes a best guess
  2. False
  3. False
  4. the trained program
  5. balanced data
  6. AI
  7. learn from data
  8. data, model, prediction
  9. the model
  10. needs AI (learning)
  11. good for training
  12. a fact
  13. few child voices in data
  14. the learned patterns
  15. confident means correct
  16. False
  17. a fact
  18. True
  19. test on one group only
  20. a hallucination
  21. needs AI (learning)
  22. one-sided data
  23. sorts or predicts
  24. its training data
  25. one set rule per case
  26. a myth
  27. good for training
  28. sorts or predicts
  29. unlocking with a face
  30. a myth
  31. AI answers need checking
  32. use one group only
  33. AI
  34. False
  35. bad for training
  36. reduces bias
  37. an AI chatbot
  38. False
  39. labels added at random
  40. False

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