AI for Kids Worksheet: Part 1 Test

AI for students lesson 8: Part 1 test: what AI is · Set 755 · 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 AI doing here: a weather app guessing tomorrow’s rain? (a) recommending (b) generating (c) recognising (d) predicting
2. 2.🧐 Fact or myth: “AI always checks facts”? (a) a fact (b) a myth
3. 3.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) training data (b) the model (c) a prediction
4. 4.True or false: “ask your teacher” is NOT a real way to check an AI answer. (a) False (b) True
5. 5.True or false: “it answered quickly” is NOT a real way to check an AI answer. (a) True (b) False
6. 6.True or false: an AI chatbot writing a poem about rain is AI generating something new. (a) True (b) False
7. 7.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
8. 8.🧐 Fact or myth: “AI copies data patterns”? (a) a fact (b) a myth
9. 9.Which of these is good for training a model? (a) photos in many lights (b) testing on training data (c) only one kind of example (d) labels added at random
10. 10.True or false: a map app guessing how long a trip will take is AI predicting something. (a) True (b) False
11. 11.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the price (d) the font size
12. 12.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
13. 13.🔍 Which is the odd one out? (a) many varied examples (b) copies of one photo (c) blurry, unclear photos (d) testing on training data
14. 14.True or false: converting metres into centimetres only needs a simple fixed rule. (a) True (b) False
15. 15.Which of these is a fact about AI? (a) AI can be wrong (b) long answers are true (c) AI is never wrong (d) confident means correct
16. 16.🤖 AI or a normal program: which one “learns from examples”? (a) a normal program (b) AI
17. 17.🤖 AI or a normal program: which one “does exactly as coded”? (a) AI (b) a normal program
18. 18.📱 What is the AI doing here: a car camera reading a speed sign? (a) predicting (b) recognising (c) generating (d) recommending
19. 19.Which of these is a myth about AI? (a) AI can be wrong (b) AI can make up facts (c) AI knows everything (d) AI can mix up numbers
20. 20.🏠 AI or not AI: a spam filter? (a) uses AI (b) not AI
21. 21.Which of these describes how a normal program works? (a) spots patterns in data (b) improves with more data (c) makes a best guess (d) one set rule per case
22. 22.Which of these is a myth about AI? (a) long answers are true (b) AI can make up facts (c) AI can be wrong (d) AI answers need checking
23. 23.True or false: stories where every doctor is a man is balanced data (fairer for everyone). (a) False (b) True
24. 24.🤖 What does AI stand for? (a) app installer (b) artificial intelligence (c) actual information (d) automatic internet
25. 25.True or false: “a soft lullaby melody” asks for text. (a) True (b) False
26. 26.Which of these describes how AI works? (a) never learns from data (b) uses rules people wrote (c) learns from examples (d) same steps every time
27. 27.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
28. 28.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) change colour (d) slower to switch on
29. 29.Which of these is not AI? (a) a voice assistant (b) photos grouped by face (c) a stopwatch (d) a map predicting traffic
30. 30.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it may get it wrong (c) it will be perfect (d) it will turn yellow
31. 31.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) training data (c) a password (d) a prediction
32. 32.🧐 Fact or myth: “AI knows everything”? (a) a fact (b) a myth
33. 33.Which of these is bad for training a model? (a) many varied examples (b) wrong labels (c) examples of every type (d) photos in many lights
34. 34.True or false: switching on a street light at 6 pm needs AI that learns from examples. (a) False (b) True
35. 35.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
36. 36.⏩ In machine learning, which comes first: flagging a new email or recorded spoken words? (a) flagging a new email (b) recorded spoken words
37. 37.📺 A video app suggests what to watch next. What is the AI doing? (a) printing (b) charging (c) recommending (d) cooking
38. 38.True or false: a voice app trained only on adult voices is one-sided data (likely to make a biased model). (a) True (b) False
39. 39.True or false: finding 10% of a price needs AI that learns from examples. (a) False (b) True
40. 40.✨ What does generative AI do? (a) only adds numbers (b) charges batteries (c) makes new content (d) only stores files

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