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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 304 · 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: “past weather records” is the model. (a) False (b) True
  2. 2.Which of these is training data? (a) naming a new fruit photo (b) typing a new spoken word (c) 500 labelled leaf photos (d) flagging a new email
  3. 3.⚖️ One-sided or balanced data: stories with doctors of every gender? (a) balanced data (b) one-sided data
  4. 4.True or false: an image generator drawing a robot from a sentence is AI generating something new. (a) False (b) True
  5. 5.🧠 Machine learning is a way for computers to… (a) charge faster (b) learn from data (c) clean screens (d) print pages
  6. 6.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) correct labels (d) only one kind of example
  7. 7.Which of these describes how a normal program works? (a) handles unseen examples (b) spots patterns in data (c) gives a likely answer (d) same steps every time
  8. 8.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
  9. 9.Which of these describes how AI works? (a) uses rules people wrote (b) one set rule per case (c) never learns from data (d) gives a likely answer
  10. 10.📚 An AI chatbot names a book that does not exist. This is called… (a) a backup (b) a hallucination (c) a password (d) a summary
  11. 11.🔍 Which is the odd one out? (a) making up a story (b) drawing a new picture (c) drafting a letter (d) spotting a sick leaf
  12. 12.🏠 AI or not AI: face unlock on a phone? (a) uses AI (b) not AI
  13. 13.True or false: “same steps every time” describes how a normal program works. (a) False (b) True
  14. 14.🧪 Why test a model on NEW examples? (a) to delete its data (b) to see if it works (c) to make it slower (d) to change its name
  15. 15.✨ What does generative AI do? (a) only adds numbers (b) charges batteries (c) makes new content (d) only stores files
  16. 16.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a list of rules only (d) a song about dogs
  17. 17.🧐 Fact or myth: “AI always checks facts”? (a) a fact (b) a myth
  18. 18.⏩ In machine learning, which comes first: flagging a new email or old emails marked spam? (a) flagging a new email (b) old emails marked spam
  19. 19.True or false: “learns from examples” describes how AI works. (a) False (b) True
  20. 20.Which of these is bad for training a model? (a) examples of every type (b) correct labels (c) testing on training data (d) new data for testing
  21. 21.✍️ How does an AI chatbot write its replies? (a) uses magic (b) predicts likely words (c) asks a person (d) copies one website
  22. 22.🏠 AI or not AI: a map predicting traffic? (a) uses AI (b) not AI
  23. 23.🏠 AI or not AI: a doorbell? (a) not AI (b) uses AI
  24. 24.Which of these is a fact about AI? (a) AI can be wrong (b) long answers are true (c) AI always checks facts (d) AI is never wrong
  25. 25.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
  26. 26.📱 What is the AI doing here: turning your spoken words into typed text? (a) predicting (b) generating (c) recommending (d) recognising
  27. 27.Which of these is the model? (a) old emails marked spam (b) the learned patterns (c) typing a new spoken word (d) flagging a new email
  28. 28.True or false: “testing on training data” is bad for training a model. (a) True (b) False
  29. 29.📱 What is the AI doing here: a car camera reading a speed sign? (a) predicting (b) recognising (c) recommending (d) generating
  30. 30.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
  31. 31.True or false: “copy old unfair choices” adds to bias. (a) False (b) True
  32. 32.🔁 Training data, the model or a prediction: “what training builds”? (a) a prediction (b) training data (c) the model
  33. 33.Which of these is a myth about AI? (a) AI can mix up numbers (b) AI can make up facts (c) AI can be wrong (d) long answers are true
  34. 34.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
  35. 35.Which of these helps reduce bias? (a) use one group only (b) check who is missing (c) test on one group only (d) skip testing
  36. 36.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
  37. 37.True or false: “gives a likely answer” describes how AI works. (a) False (b) True
  38. 38.🔍 Which is the odd one out? (a) skip testing (b) copy old unfair choices (c) use one group only (d) add varied examples
  39. 39.🔍 Which is the odd one out? (a) composing a new tune (b) reading number plates (c) unlocking with a face (d) suggesting a video
  40. 40.True or false: “what training builds” is the model. (a) True (b) False

Answer key

  1. False
  2. 500 labelled leaf photos
  3. balanced data
  4. True
  5. learn from data
  6. correct labels
  7. same steps every time
  8. False
  9. gives a likely answer
  10. a hallucination
  11. spotting a sick leaf
  12. uses AI
  13. True
  14. to see if it works
  15. makes new content
  16. many labelled photos
  17. a myth
  18. old emails marked spam
  19. True
  20. testing on training data
  21. predicts likely words
  22. uses AI
  23. not AI
  24. AI can be wrong
  25. True
  26. recognising
  27. the learned patterns
  28. True
  29. recognising
  30. True
  31. True
  32. the model
  33. long answers are true
  34. False
  35. check who is missing
  36. reduces bias
  37. True
  38. add varied examples
  39. composing a new tune
  40. True

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