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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 307 · 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: “a drawing of a cat on the Moon” asks for an image. (a) False (b) True
  2. 2.Which of these is training data? (a) tomorrow’s rain guess (b) the learned patterns (c) 500 labelled leaf photos (d) the trained program
  3. 3.True or false: “what training builds” is a prediction. (a) True (b) False
  4. 4.True or false: “a logo idea for the eco club” asks for an image. (a) False (b) True
  5. 5.Which of these is AI that sorts, spots or predicts (not generative)? (a) unlocking with a face (b) inventing a recipe (c) making up a story (d) drafting a letter
  6. 6.✅ Is this a real check of an AI answer: ask your teacher? (a) a real check (b) not a real check
  7. 7.🧐 Fact or myth: “AI always checks facts”? (a) a fact (b) a myth
  8. 8.🧐 Fact or myth: “AI has real feelings”? (a) a fact (b) a myth
  9. 9.🔍 Which is the odd one out? (a) blurry, unclear photos (b) copies of one photo (c) testing on training data (d) new data for testing
  10. 10.🔍 Which is the odd one out? (a) AI can be wrong (b) confident means correct (c) long answers are true (d) AI knows everything
  11. 11.📚 An AI chatbot names a book that does not exist. This is called… (a) a backup (b) a summary (c) a hallucination (d) a password
  12. 12.True or false: ringing the school bell at 9 am needs AI that learns from examples. (a) True (b) False
  13. 13.🧐 Fact or myth: “AI is never wrong”? (a) a fact (b) a myth
  14. 14.Which of these is bad for training a model? (a) testing on training data (b) many varied examples (c) clear, sharp examples (d) photos in many lights
  15. 15.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
  16. 16.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
  17. 17.🧠 Machine learning is a way for computers to… (a) print pages (b) learn from data (c) clean screens (d) charge faster
  18. 18.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) children speak too fast (c) it was a rainy day (d) few child voices in data
  19. 19.⏩ In machine learning, which comes first: naming a new leaf or the learned patterns? (a) naming a new leaf (b) the learned patterns
  20. 20.True or false: “one set rule per case” describes how AI works. (a) False (b) True
  21. 21.🔍 Which is the odd one out? (a) AI can mix up numbers (b) long answers are true (c) AI has real feelings (d) AI knows everything
  22. 22.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) a prediction (c) training data
  23. 23.🎨 This prompt asks a generative AI for: a soft lullaby melody. What kind of output is that? (a) an image (b) text (c) music or sound
  24. 24.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
  25. 25.True or false: “match it to the textbook” is a real way to check an AI answer. (a) True (b) False
  26. 26.🔍 Which is the odd one out? (a) photos in many lights (b) very few examples (c) testing on training data (d) copies of one photo
  27. 27.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) the model (c) a prediction
  28. 28.True or false: “ignore complaints” helps reduce bias. (a) False (b) True
  29. 29.Which of these describes how a normal program works? (a) improves with more data (b) handles unseen examples (c) gives a likely answer (d) same steps every time
  30. 30.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) very few examples (d) correct labels
  31. 31.True or false: “very few examples” is bad for training a model. (a) True (b) False
  32. 32.🔍 Which is the odd one out? (a) AI has real feelings (b) AI learns from data (c) AI can be wrong (d) AI can mix up numbers
  33. 33.True or false: “what training builds” is the model. (a) True (b) False
  34. 34.🧐 Fact or myth: “AI can make up facts”? (a) a myth (b) a fact
  35. 35.Which of these is a myth about AI? (a) AI copies data patterns (b) AI knows everything (c) AI can make up facts (d) AI can mix up numbers
  36. 36.🏠 AI or not AI: a calculator? (a) uses AI (b) not AI
  37. 37.🛠️ Does this reduce bias or add to it: check who is missing? (a) adds to bias (b) reduces bias
  38. 38.Which of these describes how a normal program works? (a) spots patterns in data (b) does exactly as coded (c) makes a best guess (d) handles unseen examples
  39. 39.Which of these uses AI? (a) a light switch (b) video suggestions (c) a torch (d) a microwave timer
  40. 40.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) a prediction (c) the model

Answer key

  1. True
  2. 500 labelled leaf photos
  3. False
  4. True
  5. unlocking with a face
  6. a real check
  7. a myth
  8. a myth
  9. new data for testing
  10. AI can be wrong
  11. a hallucination
  12. False
  13. a myth
  14. testing on training data
  15. bad for training
  16. bad for training
  17. learn from data
  18. few child voices in data
  19. the learned patterns
  20. False
  21. AI can mix up numbers
  22. training data
  23. music or sound
  24. False
  25. True
  26. photos in many lights
  27. a prediction
  28. False
  29. same steps every time
  30. very few examples
  31. True
  32. AI has real feelings
  33. True
  34. a fact
  35. AI knows everything
  36. not AI
  37. reduces bias
  38. does exactly as coded
  39. video suggestions
  40. a prediction

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