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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 339 · 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: “uses rules people wrote” describes how a normal program works. (a) False (b) True
  2. 2.True or false: “handles unseen examples” describes how a normal program works. (a) True (b) False
  3. 3.Which of these is AI that sorts, spots or predicts (not generative)? (a) composing a new tune (b) writing a new poem (c) reading number plates (d) drafting a letter
  4. 4.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) one-sided data (b) balanced data
  5. 5.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
  6. 6.🔍 Which is the odd one out? (a) writing a new poem (b) creating a cartoon (c) inventing a recipe (d) predicting rain
  7. 7.🤔 An AI answer sounds very confident. Does that mean it is right? (a) no, check it anyway (b) yes, always (c) yes, if it is long
  8. 8.🧐 Fact or myth: “long answers are true”? (a) a myth (b) a fact
  9. 9.🔍 Which is the odd one out? (a) copies of one photo (b) many varied examples (c) very few examples (d) testing on training data
  10. 10.True or false: an online shop showing “you may also like” is AI recommending something. (a) False (b) True
  11. 11.True or false: “AI can be wrong” is a myth about AI. (a) True (b) False
  12. 12.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) a prediction (c) training data
  13. 13.🔁 Training data, the model or a prediction: “the trained program”? (a) a prediction (b) training data (c) the model
  14. 14.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
  15. 15.True or false: “write a poem about the monsoon” asks for music or sound. (a) False (b) True
  16. 16.🤖 AI or a normal program: which one “one set rule per case”? (a) a normal program (b) AI
  17. 17.Which of these adds to bias? (a) add more of the same (b) add varied examples (c) test on many groups (d) check who is missing
  18. 18.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
  19. 19.🎨 This prompt asks a generative AI for: a poster of a rainforest. What kind of output is that? (a) text (b) music or sound (c) an image
  20. 20.✨ Generative or not: creating a cartoon? (a) sorts or predicts (b) makes something new
  21. 21.Which of these helps reduce bias? (a) use one group only (b) fix wrong labels (c) add more of the same (d) copy old unfair choices
  22. 22.True or false: “uses rules people wrote” describes how AI works. (a) True (b) False
  23. 23.⚖️ One-sided or balanced data: mangoes both raw and ripe? (a) balanced data (b) one-sided data
  24. 24.📱 What is the AI doing here: an AI chatbot writing a poem about rain? (a) recognising (b) generating (c) recommending (d) predicting
  25. 25.Which of these is bad for training a model? (a) blurry, unclear photos (b) clear, sharp examples (c) correct labels (d) many varied examples
  26. 26.🤖 What does AI stand for? (a) artificial intelligence (b) automatic internet (c) actual information (d) app installer
  27. 27.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
  28. 28.✨ Generative or not: inventing a recipe? (a) sorts or predicts (b) makes something new
  29. 29.🧐 Fact or myth: “AI answers need checking”? (a) a fact (b) a myth
  30. 30.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) True (b) False
  31. 31.🧐 Fact or myth: “AI can mix up numbers”? (a) a myth (b) a fact
  32. 32.🔍 Which is the odd one out? (a) skip testing (b) balance the data (c) copy old unfair choices (d) use one group only
  33. 33.True or false: suggesting the next word as you type only needs a simple fixed rule. (a) False (b) True
  34. 34.Which of these adds to bias? (a) add varied examples (b) balance the data (c) ignore complaints (d) fix wrong labels
  35. 35.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it may get it wrong (c) it will shut down (d) it will turn yellow
  36. 36.🔍 Which is the odd one out? (a) AI learns from data (b) AI can make up facts (c) AI can mix up numbers (d) AI is never wrong
  37. 37.🔍 Which is the odd one out? (a) skip testing (b) test on many groups (c) add more of the same (d) ignore complaints
  38. 38.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) training data (c) the model
  39. 39.🤖 AI or a normal program: which one “makes a best guess”? (a) AI (b) a normal program
  40. 40.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) training data (c) a prediction

Answer key

  1. True
  2. False
  3. reading number plates
  4. balanced data
  5. True
  6. predicting rain
  7. no, check it anyway
  8. a myth
  9. many varied examples
  10. True
  11. False
  12. a prediction
  13. the model
  14. good for training
  15. False
  16. a normal program
  17. add more of the same
  18. True
  19. an image
  20. makes something new
  21. fix wrong labels
  22. False
  23. balanced data
  24. generating
  25. blurry, unclear photos
  26. artificial intelligence
  27. not spam
  28. makes something new
  29. a fact
  30. False
  31. a fact
  32. balance the data
  33. False
  34. ignore complaints
  35. it may get it wrong
  36. AI is never wrong
  37. test on many groups
  38. training data
  39. AI
  40. a prediction

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