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AI for Students Worksheet: Final Test

AI for students lesson 24: Final test: AI for students · Set 650 · 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: “Give me 10 questions on the 7 times table, with answers at the end.”? (a) clear (b) vague
  2. 2.🛠️ Does this make a weak prompt better: make it vaguer? (a) a good fix (b) not a fix
  3. 3.🪜 What does this prompt ask for? “Give me just the answers to all 10 sums.” (a) asks only for the answer (b) asks for steps
  4. 4.Which of these makes a weak prompt better? (a) add unrelated details (b) leave out the topic (c) give an example (d) write it in capitals
  5. 5.📝 Why is copying an AI essay a problem? (a) it is too short (b) you do not learn (c) it is too neat (d) it uses paper
  6. 6.📖 Is this a good source to check an AI answer against: a library book? (a) a good source (b) not a good source
  7. 7.📖 Is this a good source to check an AI answer against: your teacher? (a) a good source (b) not a good source
  8. 8.🌍 An AI chatbot says the Earth has two moons. Your textbook says one. What now? (a) stop studying (b) trust the AI (c) share it online (d) trust the textbook
  9. 9.True or false: “Suggest 5 healthy tiffin ideas for a 10-year-old, without nuts.” is a vague prompt. (a) True (b) False
  10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
  11. 11.Which of these is bad for training a model? (a) many varied examples (b) only one kind of example (c) correct labels (d) photos in many lights
  12. 12.🎯 Tara’s spam spotter was tested on 20 new messages and got 18 right. What is its accuracy in percent?
  13. 13.🧩 Which part of a prompt is this? “Write it as a short dialogue.” (a) context (b) format (c) role (d) task
  14. 14.True or false: “List the steps to make a paper boat.” asks only for the final answer. (a) False (b) True
  15. 15.🛠️ Which does this job need: translating a sentence into Hindi? (a) needs AI (learning) (b) a simple rule is enough
  16. 16.🗂️ An AI flashcard has a wrong fact. What should you do? (a) throw all cards away (b) ignore it (c) learn it anyway (d) correct the card
  17. 17.True or false: stories with doctors of every gender is balanced data (fairer for everyone). (a) False (b) True
  18. 18.🧩 Which part of a prompt is this? “It is for a school assembly.” (a) task (b) example (c) role (d) context
  19. 19.🔍 Which is the odd one out? (a) only one kind of example (b) checking labels twice (c) new data for testing (d) clear, sharp examples
  20. 20.🧐 Fact or myth: “AI always checks facts”? (a) a myth (b) a fact
  21. 21.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) learn from data (d) print pages
  22. 22.©️ Does this need credit: a story you made up? (a) your own work (b) needs credit
  23. 23.📺 A video app suggests what to watch next. What is the AI doing? (a) recommending (b) printing (c) charging (d) cooking
  24. 24.Which of these is someone else’s work (credit it)? (a) a quote from a book (b) a picture I drew (c) a poem I wrote (d) my own survey results
  25. 25.⏩ In machine learning, which comes first: the learned patterns or flagging a new email? (a) the learned patterns (b) flagging a new email
  26. 26.🤖 Who builds and trains AI systems? (a) the weather (b) people (c) nobody at all (d) the Moon
  27. 27.🔎 Checking an AI answer: which do you do first — underline each fact or fix your notes? (a) underline each fact (b) fix your notes
  28. 28.🧩 Which part of a prompt is this? “I have a test on Friday.” (a) task (b) example (c) context (d) role
  29. 29.📱 What is the AI doing here: a shop guessing how many umbrellas will sell? (a) recommending (b) generating (c) predicting (d) recognising
  30. 30.Which of these adds to bias? (a) add varied examples (b) test on many groups (c) ask different people (d) test on one group only
  31. 31.True or false: “ignore complaints” helps reduce bias. (a) True (b) False
  32. 32.🤖 What makes an AI system different from a normal program? (a) it has a screen (b) it uses electricity (c) it is always right (d) it learns from examples
  33. 33.🚩 A study chatbot asks for money. Is that a red flag? (a) normal (b) a red flag
  34. 34.Which of these is not AI? (a) a digital watch (b) a voice assistant (c) an AI chatbot (d) video suggestions
  35. 35.Which of these is AI that sorts, spots or predicts (not generative)? (a) writing a new poem (b) drawing a new picture (c) creating a cartoon (d) unlocking with a face
  36. 36.📏 Why add a constraint to a prompt? (a) to make AI angry (b) to waste time (c) to get what you need (d) to hide the task
  37. 37.True or false: “Break this word problem into small steps.” asks the AI to show the steps. (a) False (b) True
  38. 38.📱 What is the AI doing here: an AI chatbot writing a story about a lost kite? (a) recognising (b) predicting (c) generating (d) recommending
  39. 39.🧐 Fact or myth: “AI is never wrong”? (a) a myth (b) a fact
  40. 40.⏩ When you use an AI app, which happens first: the model makes a guess or the label “cat” shows? (a) the model makes a guess (b) the label “cat” shows

Answer key

  1. clear
  2. not a fix
  3. asks only for the answer
  4. give an example
  5. you do not learn
  6. a good source
  7. a good source
  8. trust the textbook
  9. False
  10. spam
  11. only one kind of example
  12. 90
  13. format
  14. False
  15. needs AI (learning)
  16. correct the card
  17. True
  18. context
  19. only one kind of example
  20. a myth
  21. learn from data
  22. your own work
  23. recommending
  24. a quote from a book
  25. the learned patterns
  26. people
  27. underline each fact
  28. context
  29. predicting
  30. test on one group only
  31. False
  32. it learns from examples
  33. a red flag
  34. a digital watch
  35. unlocking with a face
  36. to get what you need
  37. True
  38. generating
  39. a myth
  40. the model makes a guess

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