AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 249 · 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.🔁 What is the right order? (a) prediction, data, model (b) data, model, prediction (c) model, prediction, data (d) data, prediction, model
2. 2.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
3. 3.⏩ In machine learning, which comes first: the trained program or tomorrow’s rain guess? (a) the trained program (b) tomorrow’s rain guess
4. 4.Which of these is training data? (a) flagging a new email (b) labelled fruit photos (c) typing a new spoken word (d) the trained program
5. 5.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
6. 6.⏩ In machine learning, which comes first: what training builds or naming a new fruit photo? (a) what training builds (b) naming a new fruit photo
7. 7.🎨 This prompt asks a generative AI for: a summary of chapter 3. What kind of output is that? (a) an image (b) music or sound (c) text
8. 8.🤖 AI or a normal program: which one “makes a best guess”? (a) AI (b) a normal program
9. 9.Which of these is AI that sorts, spots or predicts (not generative)? (a) writing a new poem (b) writing quiz questions (c) creating a cartoon (d) spotting spam
10. 10.🗣️ A voice app understands adults but not children. What is the likely reason? (a) the phone was new (b) it was a rainy day (c) children speak too fast (d) few child voices in data
11. 11.True or false: “it is long and detailed” is a real way to check an AI answer. (a) False (b) True
12. 12.True or false: “a thank-you note to the bus driver” asks for text. (a) True (b) False
13. 13.⚖️ One-sided or balanced data: a handwriting app shown only neat writing? (a) balanced data (b) one-sided data
14. 14.Which of these is a fact about AI? (a) confident means correct (b) long answers are true (c) AI answers need checking (d) AI knows everything
15. 15.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) False (b) True
16. 16.True or false: “improves with more data” describes how a normal program works. (a) True (b) False
17. 17.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) the model (b) training data (c) a prediction
18. 18.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
19. 19.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
20. 20.🔢 An AI chatbot gives a sum’s answer. What is the smart thing to do? (a) ask it to hurry (b) never do maths (c) check it on paper (d) copy it straight away
21. 21.Which of these describes how a normal program works? (a) learns from examples (b) same steps every time (c) gives a likely answer (d) spots patterns in data
22. 22.⏩ In machine learning, which comes first: the trained program or recorded spoken words? (a) the trained program (b) recorded spoken words
23. 23.🔍 Which is the odd one out? (a) balance the data (b) check who is missing (c) fix wrong labels (d) copy old unfair choices
24. 24.Which of these is a fact about AI? (a) long answers are true (b) confident means correct (c) AI can make up facts (d) AI has real feelings
25. 25.True or false: “AI copies data patterns” is a myth about AI. (a) False (b) True
26. 26.True or false: turning spoken words into typed text only needs a simple fixed rule. (a) True (b) False
27. 27.🤖 Who builds and trains AI systems? (a) nobody at all (b) the weather (c) people (d) the Moon
28. 28.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) better at its task (d) slower to switch on
29. 29.Which of these is good for training a model? (a) many varied examples (b) copies of one photo (c) blurry, unclear photos (d) wrong labels
30. 30.🧐 Fact or myth: “AI knows everything”? (a) a myth (b) a fact
31. 31.True or false: “AI learns from data” is a fact about AI. (a) False (b) True
32. 32.True or false: “wrong labels” is bad for training a model. (a) False (b) True
33. 33.True or false: “AI can be wrong” is a myth about AI. (a) False (b) True
34. 34.True or false: “naming a new leaf” is a prediction. (a) True (b) False
35. 35.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
36. 36.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
37. 37.True or false: “labelled fruit photos” is training data. (a) False (b) True
38. 38.Which of these adds to bias? (a) ask different people (b) test on many groups (c) ignore complaints (d) check who is missing
39. 39.🛠️ Does this reduce bias or add to it: fix wrong labels? (a) adds to bias (b) reduces bias
40. 40.True or false: “uses rules people wrote” describes how AI works. (a) True (b) False

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