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AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 664 · 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.🤖 AI or a normal program: which one “gives a likely answer”? (a) AI (b) a normal program
  2. 2.True or false: “correct labels” is bad for training a model. (a) True (b) False
  3. 3.🔁 What is the right order? (a) data, model, prediction (b) prediction, data, model (c) model, prediction, data (d) data, prediction, model
  4. 4.Which of these describes how AI works? (a) uses rules people wrote (b) one set rule per case (c) handles unseen examples (d) never learns from data
  5. 5.True or false: “wrong labels” is bad for training a model. (a) False (b) True
  6. 6.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
  7. 7.True or false: “check who is missing” helps reduce bias. (a) True (b) False
  8. 8.🛠️ Does this reduce bias or add to it: add more of the same? (a) reduces bias (b) adds to bias
  9. 9.🔍 Which is the odd one out? (a) inventing a recipe (b) drafting a letter (c) spotting a sick leaf (d) composing a new tune
  10. 10.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a password (c) a prediction (d) training data
  11. 11.True or false: photos of people of many ages and skin tones is one-sided data (likely to make a biased model). (a) False (b) True
  12. 12.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the font size (c) a sticker on a laptop (d) the price
  13. 13.🎨 An image generator makes a picture from… (a) a phone number (b) the weather (c) a text description (d) your password
  14. 14.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) change colour (d) better at its task
  15. 15.🧪 Why test a model on NEW examples? (a) to make it slower (b) to delete its data (c) to change its name (d) to see if it works
  16. 16.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
  17. 17.True or false: “naming a new fruit photo” is the model. (a) True (b) False
  18. 18.Which of these is AI that sorts, spots or predicts (not generative)? (a) suggesting a video (b) writing a new poem (c) creating a cartoon (d) writing quiz questions
  19. 19.True or false: voices of children and adults from many regions is balanced data (fairer for everyone). (a) False (b) True
  20. 20.✅ Is this a real check of an AI answer: it is long and detailed? (a) a real check (b) not a real check
  21. 21.🎨 This prompt asks a generative AI for: a cheerful tune for Sports Day. What kind of output is that? (a) text (b) an image (c) music or sound
  22. 22.⏩ In machine learning, which comes first: recorded spoken words or the learned patterns? (a) recorded spoken words (b) the learned patterns
  23. 23.✨ Generative or not: reading number plates? (a) makes something new (b) sorts or predicts
  24. 24.🛠️ Does this reduce bias or add to it: ignore complaints? (a) adds to bias (b) reduces bias
  25. 25.Which of these is generative AI (it makes something new)? (a) composing a new tune (b) sorting photos by face (c) turning speech to text (d) spotting a sick leaf
  26. 26.🛠️ Which does this job need: turning spoken words into typed text? (a) a simple rule is enough (b) needs AI (learning)
  27. 27.🔍 Which is the odd one out? (a) a light switch (b) a doorbell (c) a microwave timer (d) a spam filter
  28. 28.✨ Generative or not: writing a new poem? (a) sorts or predicts (b) makes something new
  29. 29.📱 What is the AI doing here: a shop guessing how many umbrellas will sell? (a) recognising (b) recommending (c) generating (d) predicting
  30. 30.🔍 Which is the odd one out? (a) checking labels twice (b) labels added at random (c) many varied examples (d) clear, sharp examples
  31. 31.True or false: “one set rule per case” describes how a normal program works. (a) True (b) False
  32. 32.📅 An AI gives a date for a history event. How can you check? (a) look in your textbook (b) pick a lucky number (c) ask it to repeat (d) guess
  33. 33.Which of these adds to bias? (a) ignore complaints (b) fix wrong labels (c) add varied examples (d) balance the data
  34. 34.Which of these is the model? (a) old emails marked spam (b) recorded spoken words (c) the learned patterns (d) 500 labelled leaf photos
  35. 35.True or false: “new data for testing” is good for training a model. (a) True (b) False
  36. 36.🛠️ Which does this job need: switching on a street light at 6 pm? (a) a simple rule is enough (b) needs AI (learning)
  37. 37.True or false: voices of children and adults from many regions is one-sided data (likely to make a biased model). (a) False (b) True
  38. 38.True or false: “checking labels twice” is good for training a model. (a) True (b) False
  39. 39.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) a prediction (b) the model (c) training data
  40. 40.🧮 Is a basic calculator an AI system? (a) only at night (b) yes, it is smart (c) no, it follows rules (d) yes, it can think

Answer key

  1. AI
  2. False
  3. data, model, prediction
  4. handles unseen examples
  5. True
  6. False
  7. True
  8. adds to bias
  9. spotting a sick leaf
  10. a prediction
  11. False
  12. the right answer tag
  13. a text description
  14. better at its task
  15. to see if it works
  16. good for training
  17. False
  18. suggesting a video
  19. True
  20. not a real check
  21. music or sound
  22. recorded spoken words
  23. sorts or predicts
  24. adds to bias
  25. composing a new tune
  26. needs AI (learning)
  27. a spam filter
  28. makes something new
  29. predicting
  30. labels added at random
  31. True
  32. look in your textbook
  33. ignore complaints
  34. the learned patterns
  35. True
  36. a simple rule is enough
  37. False
  38. True
  39. training data
  40. no, it follows rules

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