AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 681 · 40 questions

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.

1. 1.True or false: “copy old unfair choices” adds to bias. (a) True (b) False
2. 2.True or false: “redo the maths yourself” is NOT a real way to check an AI answer. (a) False (b) True
3. 3.🧐 Fact or myth: “AI learns from data”? (a) a myth (b) a fact
4. 4.🤖 AI or a normal program: which one “one set rule per case”? (a) a normal program (b) AI
5. 5.🗣️ 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) the phone was new (d) children speak too fast
6. 6.🔍 Which is the odd one out? (a) a torch (b) a digital watch (c) a spam filter (d) a fan regulator
7. 7.✨ Generative or not: drafting a letter? (a) sorts or predicts (b) makes something new
8. 8.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
9. 9.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
10. 10.🛠️ Which does this job need: telling a cat photo from a dog photo? (a) needs AI (learning) (b) a simple rule is enough
11. 11.🔍 Which is the odd one out? (a) examples of every type (b) very few examples (c) photos in many lights (d) checking labels twice
12. 12.Which of these is not AI? (a) a calculator (b) an app translating signs (c) face unlock on a phone (d) an image generator
13. 13.🔍 Which is the odd one out? (a) composing a new tune (b) unlocking with a face (c) inventing a recipe (d) drawing a new picture
14. 14.🛠️ Does this reduce bias or add to it: ask different people? (a) adds to bias (b) reduces bias
15. 15.Which of these is a fact about AI? (a) confident means correct (b) long answers are true (c) AI always checks facts (d) AI can mix up numbers
16. 16.Which of these describes how AI works? (a) uses rules people wrote (b) never learns from data (c) makes a best guess (d) one set rule per case
17. 17.True or false: “handles unseen examples” describes how a normal program works. (a) True (b) False
18. 18.🔢 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
19. 19.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
20. 20.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
21. 21.🎨 This prompt asks a generative AI for: write a poem about the monsoon. What kind of output is that? (a) an image (b) music or sound (c) text
22. 22.🎨 An image generator makes a picture from… (a) a phone number (b) the weather (c) your password (d) a text description
23. 23.Which of these is a fact about AI? (a) long answers are true (b) AI can be wrong (c) AI is never wrong (d) AI knows everything
24. 24.🛠️ Which does this job need: reading messy handwriting? (a) a simple rule is enough (b) needs AI (learning)
25. 25.🔍 Which is the odd one out? (a) inventing a recipe (b) predicting rain (c) writing quiz questions (d) drawing a new picture
26. 26.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) training data (b) a prediction (c) the model
27. 27.🛠️ Which does this job need: showing the time on a clock? (a) needs AI (learning) (b) a simple rule is enough
28. 28.Which of these is good for training a model? (a) copies of one photo (b) only one kind of example (c) clear, sharp examples (d) blurry, unclear photos
29. 29.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) testing on training data (d) photos in many lights
30. 30.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the font size (d) the price
31. 31.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) the model (b) training data (c) a prediction
32. 32.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
33. 33.🏠 AI or not AI: an image generator? (a) uses AI (b) not AI
34. 34.Which of these is a myth about AI? (a) AI learns from data (b) AI can be wrong (c) AI can mix up numbers (d) AI knows everything
35. 35.Which of these is the model? (a) past weather records (b) 500 labelled leaf photos (c) the trained program (d) tomorrow’s rain guess
36. 36.⚖️ One-sided or balanced data: photos of people from only one age group? (a) one-sided data (b) balanced data
37. 37.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
38. 38.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
39. 39.🔍 Which is the odd one out? (a) many varied examples (b) labels added at random (c) clear, sharp examples (d) photos in many lights
40. 40.Which of these adds to bias? (a) copy old unfair choices (b) balance the data (c) test on many groups (d) check who is missing

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