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AI for Students Worksheet: Final Test

AI for students lesson 24: Final test: AI for students · Set 123 · 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.✍️ Clear or vague: “Translate.”? (a) clear (b) vague
  2. 2.True or false: “AI copies data patterns” is a myth about AI. (a) False (b) True
  3. 3.Which of these is a prediction? (a) labelled fruit photos (b) the trained program (c) the learned patterns (d) naming a new fruit photo
  4. 4.✍️ Which prompt is clearer?
    A: “Summarise this paragraph about volcanoes in 3 bullet points for revision.”
    B: “Make it short.”
    (a) Prompt A (b) Prompt B
  5. 5.True or false: “Make cards with a term on one side, its meaning on the other.” is asking for a summary. (a) True (b) False
  6. 6.Which of these makes a weak prompt better? (a) say what it is for (b) add exclamation marks (c) make it vaguer (d) use unclear slang
  7. 7.📱 What is the AI doing here: a music app suggesting a song you may like? (a) recognising (b) recommending (c) generating (d) predicting
  8. 8.🏠 AI or not AI: a doorbell? (a) not AI (b) uses AI
  9. 9.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) True (b) False
  10. 10.Which of these is someone else’s work (credit it)? (a) my own survey results (b) a photo I took (c) a photo from a website (d) my own diary
  11. 11.❌ Vihaan’s bird-song app was tested on 39 new bird songs and got 35 right. How many did it get wrong?
  12. 12.🎯 Diya’s spam spotter was tested on 20 new messages and got 16 right. What is its accuracy in percent?
  13. 13.🔍 Which is the odd one out? (a) a cartoon from a comic (b) a story I made up (c) a song on the radio (d) a map from an atlas
  14. 14.📄 A chapter has 1000 words. Diya asks an AI chatbot for a summary one tenth as long. How many words should the summary have?
  15. 15.True or false: “skip testing” helps reduce bias. (a) True (b) False
  16. 16.⚖️ Where does bias in an AI system usually come from? (a) the weather (b) its colour (c) its screen size (d) its training data
  17. 17.Which of these is the model? (a) the trained program (b) flagging a new email (c) labelled fruit photos (d) recorded spoken words
  18. 18.📲 Input, model or output: “the part that decides”? (a) the input (b) the model (c) the output
  19. 19.True or false: “answers at the end” sets a length limit. (a) False (b) True
  20. 20.Which of these should be kept private — never share it with an AI chatbot? (a) a chapter name (b) your home address (c) a science question (d) a word you don’t know
  21. 21.🤖 Does an AI system think and feel like a person? (a) yes, it gets sad (b) yes, just like us (c) only on weekends (d) no, it finds patterns
  22. 22.📏 What kind of constraint is “in 2 lines only”? (a) a reading level (b) what to include (c) a length limit (d) a style or tone
  23. 23.True or false: “handles unseen examples” describes how a normal program works. (a) False (b) True
  24. 24.🪜 What does this prompt ask for? “Break this word problem into small steps.” (a) asks only for the answer (b) asks for steps
  25. 25.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a prediction (c) a password (d) training data
  26. 26.❌ Meera’s leaf identifier was tested on 45 new leaf photos and got 36 right. How many did it get wrong?
  27. 27.True or false: “List 6 science fair ideas about water that use cheap materials.” is a vague prompt. (a) False (b) True
  28. 28.Which of these makes a weak prompt better? (a) add your class level (b) write it in capitals (c) use unclear slang (d) type it twice
  29. 29.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) learn from data (d) print pages
  30. 30.🪜 Why ask an AI chatbot to show the steps? (a) to learn the method (b) to copy faster (c) to make it slower (d) to get a longer answer
  31. 31.True or false: the request “explain this step” does the work for you. (a) False (b) True
  32. 32.True or false: “AI copies data patterns” is a fact about AI. (a) True (b) False
  33. 33.📏 What kind of constraint is “for a Class 3 student”? (a) a style or tone (b) a length limit (c) what to include (d) a reading level
  34. 34.⏩ In machine learning, which comes first: 500 labelled leaf photos or what training builds? (a) 500 labelled leaf photos (b) what training builds
  35. 35.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
  36. 36.Which of these is NOT a good source to check against? (a) your school textbook (b) the same chatbot again (c) your teacher (d) a government website
  37. 37.📋 Planning an AI project: which comes first — label each example or pick a question to solve? (a) label each example (b) pick a question to solve
  38. 38.©️ Whose work is it: my own survey results? (a) my own work (b) someone else’s work
  39. 39.💬 Rule-based or learning chatbot: “every reply hand-written”? (a) learning (b) rule-based
  40. 40.True or false: making practice questions to test yourself is NOT honest use. (a) False (b) True

Answer key

  1. vague
  2. False
  3. naming a new fruit photo
  4. Prompt A
  5. False
  6. say what it is for
  7. recommending
  8. not AI
  9. True
  10. a photo from a website
  11. 4
  12. 80
  13. a story I made up
  14. 100
  15. False
  16. its training data
  17. the trained program
  18. the model
  19. False
  20. your home address
  21. no, it finds patterns
  22. a length limit
  23. False
  24. asks for steps
  25. a prediction
  26. 9
  27. False
  28. add your class level
  29. learn from data
  30. to learn the method
  31. False
  32. True
  33. a reading level
  34. 500 labelled leaf photos
  35. good for training
  36. the same chatbot again
  37. pick a question to solve
  38. my own work
  39. rule-based
  40. False

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