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AI for Students Worksheet: Final Test

AI for students lesson 24: Final test: AI for students · Set 732 · 40 questions
TalentJR

AI tip (Know it, prompt it, check it): Find the topic: what AI is, prompts, honest use, checking, privacy, age rules, fakes, credit, study, apps or projects. Use that lesson’s rule and read every option before you choose. For number questions, write the calculation first: words over = written − limit, accuracy = correct ÷ total × 100.

NameDateTime takenScore ___ / 40
  1. 1.Which of these uses AI? (a) a doorbell (b) face unlock on a phone (c) a microwave timer (d) a light switch
  2. 2.⏩ In machine learning, which comes first: what training builds or labelled fruit photos? (a) what training builds (b) labelled fruit photos
  3. 3.Which of these makes a weak prompt better? (a) write it in capitals (b) name the exact topic (c) leave out the topic (d) add lots of emojis
  4. 4.⚖️ One-sided or balanced data: voices of children and adults from many regions? (a) one-sided data (b) balanced data
  5. 5.📖 Is this a good source to check an AI answer against: a forwarded message? (a) a good source (b) not a good source
  6. 6.Which of these is okay to share with an AI chatbot? (a) your password (b) a friend’s secret (c) your live location (d) a history question
  7. 7.True or false: “My class just learned fractions.” is the task part of a prompt. (a) False (b) True
  8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
  9. 9.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
  10. 10.🔍 Which is the odd one out? (a) add lots of emojis (b) add exclamation marks (c) add your class level (d) write it in capitals
  11. 11.💬 Does this request help you learn, or do the work for you? “finish my homework” (a) helps you learn (b) does it for you
  12. 12.🧩 Which part of a prompt says WHAT you want done? (a) the example (b) the format (c) the task (d) the role
  13. 13.📏 What kind of constraint is “in a funny way”? (a) what to include (b) a reading level (c) a length limit (d) a style or tone
  14. 14.Which of these is a good source to check against? (a) a funny meme (b) a forwarded message (c) the same chatbot again (d) your school textbook
  15. 15.🔍 Which is the odd one out? (a) a map predicting traffic (b) a stopwatch (c) a light switch (d) a microwave timer
  16. 16.📏 What kind of constraint is “like a news report”? (a) a reading level (b) a style or tone (c) a length limit (d) what to include
  17. 17.🔍 Which is the odd one out? (a) checking labels twice (b) clear, sharp examples (c) many varied examples (d) labels added at random
  18. 18.Which of these is generative AI (it makes something new)? (a) turning speech to text (b) writing a new poem (c) spotting a sick leaf (d) predicting rain
  19. 19.🔒 What happens to what you type into many AI apps? (a) it vanishes forever (b) it turns into music (c) it may be stored (d) it becomes secret
  20. 20.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) fewer examples (b) a louder speaker (c) a new colour screen (d) more labelled examples
  21. 21.📄 A chapter has 1100 words. Aarav asks an AI chatbot for a summary one tenth as long. How many words should the summary have?
  22. 22.⚖️ Where does bias in an AI system usually come from? (a) its training data (b) the weather (c) its colour (d) its screen size
  23. 23.✍️ Clear or vague: “Tell me about plants.”? (a) vague (b) clear
  24. 24.🔍 Which is the odd one out? (a) wrong labels (b) new data for testing (c) checking labels twice (d) photos in many lights
  25. 25.🔁 What is the right order? (a) model, prediction, data (b) data, model, prediction (c) data, prediction, model (d) prediction, data, model
  26. 26.True or false: “split into small asks” makes a weak prompt better. (a) False (b) True
  27. 27.Which of these is not AI? (a) face unlock on a phone (b) a spam filter (c) video suggestions (d) a doorbell
  28. 28.True or false: “Write a poem.” is a vague prompt. (a) False (b) True
  29. 29.✍️ Which prompt is clearer?
    A: “Write a polite 3-line message asking my teacher for extra practice sheets.”
    B: “Write a message.”
    (a) Prompt A (b) Prompt B
  30. 30.True or false: “Suggest 5 healthy tiffin ideas for a 10-year-old, without nuts.” is a clear prompt. (a) True (b) False
  31. 31.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) the model (b) a prediction (c) training data
  32. 32.True or false: “write it in capitals” makes a weak prompt better. (a) True (b) False
  33. 33.True or false: “naming a new leaf” is the model. (a) False (b) True
  34. 34.Which of these uses AI? (a) a stopwatch (b) a microwave timer (c) a spam filter (d) a fan regulator
  35. 35.✍️ Which prompt is clearer?
    A: “Sky colour?”
    B: “Explain why the sky looks blue in under 60 words for a 10-year-old.”
    (a) Prompt A (b) Prompt B
  36. 36.True or false: “One word answer: is 91 prime?” asks the AI to show the steps. (a) True (b) False
  37. 37.⏩ In machine learning, which comes first: naming a new leaf or what training builds? (a) naming a new leaf (b) what training builds
  38. 38.Which of these is a good sign (but still check)? (a) too good to be true (b) a shocking headline (c) shadows going two ways (d) many trusted sources
  39. 39.©️ Can you sell copies of someone else’s comic? (a) not without permission (b) yes, any time (c) yes, if you colour it
  40. 40.🔒 Keep it private or okay to share with an AI chatbot: your name and school? (a) okay to share (b) keep it private

Answer key

  1. face unlock on a phone
  2. labelled fruit photos
  3. name the exact topic
  4. balanced data
  5. not a good source
  6. a history question
  7. False
  8. spam
  9. bad for training
  10. add your class level
  11. does it for you
  12. the task
  13. a style or tone
  14. your school textbook
  15. a map predicting traffic
  16. a style or tone
  17. labels added at random
  18. writing a new poem
  19. it may be stored
  20. more labelled examples
  21. 110
  22. its training data
  23. vague
  24. wrong labels
  25. data, model, prediction
  26. True
  27. a doorbell
  28. True
  29. Prompt A
  30. True
  31. a prediction
  32. False
  33. False
  34. a spam filter
  35. Prompt B
  36. False
  37. what training builds
  38. many trusted sources
  39. not without permission
  40. keep it private

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