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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 229 · 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.🎨 This prompt asks a generative AI for: a thank-you note to the bus driver. What kind of output is that? (a) text (b) an image (c) music or sound
  2. 2.Which of these describes how AI works? (a) follows fixed rules (b) never learns from data (c) makes a best guess (d) does exactly as coded
  3. 3.🗣️ A voice app understands adults but not children. What is the likely reason? (a) it was a rainy day (b) few child voices in data (c) children speak too fast (d) the phone was new
  4. 4.🔢 An AI chatbot gives a sum’s answer. What is the smart thing to do? (a) copy it straight away (b) ask it to hurry (c) check it on paper (d) never do maths
  5. 5.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
  6. 6.📦 What happens during training? (a) it takes photos (b) it prints labels (c) it learns patterns (d) it charges up
  7. 7.True or false: “learns from examples” describes how AI works. (a) False (b) True
  8. 8.🧪 Why test a model on NEW examples? (a) to make it slower (b) to see if it works (c) to delete its data (d) to change its name
  9. 9.🔁 What is the right order? (a) prediction, data, model (b) data, prediction, model (c) model, prediction, data (d) data, model, prediction
  10. 10.🤖 AI or a normal program: which one “gives a likely answer”? (a) a normal program (b) AI
  11. 11.True or false: “it has a neat layout” is a real way to check an AI answer. (a) False (b) True
  12. 12.Which of these is AI that sorts, spots or predicts (not generative)? (a) drawing a new picture (b) writing a new poem (c) drafting a letter (d) turning speech to text
  13. 13.True or false: “ignore complaints” adds to bias. (a) True (b) False
  14. 14.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
  15. 15.🛠️ Which does this job need: translating a sentence into Hindi? (a) a simple rule is enough (b) needs AI (learning)
  16. 16.True or false: a map app guessing how long a trip will take is AI predicting something. (a) False (b) True
  17. 17.True or false: “labelled fruit photos” is a prediction. (a) True (b) False
  18. 18.🔍 Which is the odd one out? (a) a stopwatch (b) a calculator (c) an AI chatbot (d) a fan regulator
  19. 19.🔍 Which is the odd one out? (a) test on many groups (b) check who is missing (c) use one group only (d) fix wrong labels
  20. 20.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
  21. 21.📅 An AI gives a date for a history event. How can you check? (a) guess (b) look in your textbook (c) pick a lucky number (d) ask it to repeat
  22. 22.True or false: “confident means correct” is a myth about AI. (a) True (b) False
  23. 23.True or false: a keyboard guessing your next word is AI recognising something. (a) False (b) True
  24. 24.🔍 Which is the odd one out? (a) new data for testing (b) checking labels twice (c) only one kind of example (d) photos in many lights
  25. 25.Which of these is bad for training a model? (a) many varied examples (b) new data for testing (c) checking labels twice (d) blurry, unclear photos
  26. 26.🛠️ Does this reduce bias or add to it: test on one group only? (a) reduces bias (b) adds to bias
  27. 27.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
  28. 28.⏩ In machine learning, which comes first: 500 labelled leaf photos or what training builds? (a) 500 labelled leaf photos (b) what training builds
  29. 29.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
  30. 30.🔍 Which is the odd one out? (a) AI is never wrong (b) AI learns from data (c) AI can mix up numbers (d) AI can make up facts
  31. 31.Which of these uses AI? (a) a light switch (b) a printed timetable (c) a fan regulator (d) an AI chatbot
  32. 32.🔮 A trained model sees a new photo and says “mango”. This is… (a) training data (b) a prediction (c) a password (d) a label error
  33. 33.🤖 AI or a normal program: which one “one set rule per case”? (a) AI (b) a normal program
  34. 34.True or false: neat and messy writing from many people is one-sided data (likely to make a biased model). (a) False (b) True
  35. 35.True or false: a book app suggesting a story like your last one is AI recognising something. (a) False (b) True
  36. 36.True or false: “write a poem about the monsoon” asks for text. (a) True (b) False
  37. 37.Which of these is training data? (a) old emails marked spam (b) the learned patterns (c) what training builds (d) typing a new spoken word
  38. 38.Which of these is good for training a model? (a) blurry, unclear photos (b) wrong labels (c) only one kind of example (d) checking labels twice
  39. 39.🔍 Which is the odd one out? (a) face unlock on a phone (b) an image generator (c) an AI chatbot (d) a doorbell
  40. 40.True or false: recognising a bird from its song only needs a simple fixed rule. (a) True (b) False

Answer key

  1. text
  2. makes a best guess
  3. few child voices in data
  4. check it on paper
  5. learn from data
  6. it learns patterns
  7. True
  8. to see if it works
  9. data, model, prediction
  10. AI
  11. False
  12. turning speech to text
  13. True
  14. the trained program
  15. needs AI (learning)
  16. True
  17. False
  18. an AI chatbot
  19. use one group only
  20. bad for training
  21. look in your textbook
  22. True
  23. False
  24. only one kind of example
  25. blurry, unclear photos
  26. adds to bias
  27. False
  28. 500 labelled leaf photos
  29. False
  30. AI is never wrong
  31. an AI chatbot
  32. a prediction
  33. a normal program
  34. False
  35. False
  36. True
  37. old emails marked spam
  38. checking labels twice
  39. a doorbell
  40. False

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