Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 873 · 40 questions

AI tip (Accuracy = correct ÷ total × 100): Steps: 1 choose a problem → 2 collect examples → 3 label them → 4 check and correct bad labels (clean the data) → 5 split into training and test sets → 6 train → 7 test with new examples → 8 measure accuracy → 9 improve the data and retest. Accuracy = correct ÷ total × 100. Wrong = total − correct. Expected right = accuracy ÷ 100 × total. Keep some examples apart for testing (for example one fifth); the rest are for training.

1. 1.🎯 Aarav’s fruit sorter is 50% accurate. If it is tested on 20 new fruit photos, how many should it get right?
2. 2.📋 Planning an AI project: which comes first — fix it and test again or work out the accuracy? (a) fix it and test again (b) work out the accuracy
3. 3.🎯 Diya’s leaf identifier was tested on 10 new leaf photos and got 6 right. What is its accuracy in percent?
4. 4.🎯 Vihaan’s fruit sorter was tested on 25 new fruit photos and got 25 right. What is its accuracy in percent?
5. 5.📦 Tara collected 130 labelled bird songs for a bird-song app and keeps one fifth of them for testing. How many are left for training?
6. 6.📋 Planning an AI project: which comes first — train the model or improve data, retest? (a) train the model (b) improve data, retest
7. 7.🎯 Ishaan’s recycling sorter is 50% accurate. If it is tested on 40 new waste photos, how many should it get right?
8. 8.📋 Planning an AI project: which comes first — tag each photo by type or collect fruit photos? (a) tag each photo by type (b) collect fruit photos
9. 9.📋 Planning an AI project: which comes first — test on new photos or improve data, retest? (a) test on new photos (b) improve data, retest
10. 10.📋 Planning an AI project: which comes first — try unseen examples or correct bad labels? (a) try unseen examples (b) correct bad labels
11. 11.🎯 Meera’s spam spotter is 80% accurate. If it is tested on 40 new messages, how many should it get right?
12. 12.📋 Planning an AI project: which comes first — let it learn patterns or try unseen examples? (a) let it learn patterns (b) try unseen examples
13. 13.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) a louder speaker (b) a new colour screen (c) fewer examples (d) more labelled examples
14. 14.🧪 Why must you test a model on examples it has NOT seen? (a) to make it bigger (b) to make it faster (c) to see if it learned (d) to save paper
15. 15.📋 Planning an AI project: which comes first — pick a question to solve or train the model? (a) pick a question to solve (b) train the model
16. 16.📋 Planning an AI project: which comes first — fix it and test again or gather many examples? (a) fix it and test again (b) gather many examples
17. 17.📋 Planning an AI project: which comes first — work out the accuracy or fix it and test again? (a) work out the accuracy (b) fix it and test again
18. 18.❌ Saanvi’s handwriting reader was tested on 22 new handwritten letters and got 18 right. How many did it get wrong?
19. 19.📋 Planning an AI project: which comes first — label each example or recheck every label? (a) label each example (b) recheck every label
20. 20.📦 Arjun collected 100 labelled bird songs for a bird-song app and keeps one fifth of them for testing. How many are left for training?
21. 21.⚖️ Two fruit sorter models were tested on the same 20 fruit photos. Model A got 20 right and Model B got 19 right. How many percentage points more accurate is Model A?
22. 22.🎯 Diya’s spam spotter is 70% accurate. If it is tested on 20 new messages, how many should it get right?
23. 23.📋 Planning an AI project: which comes first — collect fruit photos or choose what to sort? (a) collect fruit photos (b) choose what to sort
24. 24.⚖️ Two spam spotter models were tested on the same 25 messages. Model A got 25 right and Model B got 24 right. How many percentage points more accurate is Model A?
25. 25.📋 Planning an AI project: which comes first — set aside test photos or correct bad labels? (a) set aside test photos (b) correct bad labels
26. 26.🎯 Meera’s handwriting reader is 50% accurate. If it is tested on 90 new handwritten letters, how many should it get right?
27. 27.📋 Planning an AI project: which comes first — try unseen examples or work out the accuracy? (a) try unseen examples (b) work out the accuracy
28. 28.🎯 Saanvi’s recycling sorter was tested on 20 new waste photos and got 20 right. What is its accuracy in percent?
29. 29.📦 Kabir collected 130 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
30. 30.🎯 Diya’s leaf identifier is 50% accurate. If it is tested on 80 new leaf photos, how many should it get right?
31. 31.📋 Planning an AI project: which comes first — work out the accuracy or test on new photos? (a) work out the accuracy (b) test on new photos
32. 32.🎯 Tara’s leaf identifier was tested on 5 new leaf photos and got 3 right. What is its accuracy in percent?
33. 33.🎯 Vihaan’s spam spotter was tested on 5 new messages and got 5 right. What is its accuracy in percent?
34. 34.⚖️ Two fruit sorter models were tested on the same 50 fruit photos. Model A got 45 right and Model B got 28 right. How many percentage points more accurate is Model A?
35. 35.🎯 Aarav’s handwriting reader is 70% accurate. If it is tested on 30 new handwritten letters, how many should it get right?
36. 36.📋 Planning an AI project: which comes first — label each example or correct bad labels? (a) label each example (b) correct bad labels
37. 37.📋 Planning an AI project: which comes first — calculate percent right or fix it and test again? (a) calculate percent right (b) fix it and test again
38. 38.❌ Ishaan’s recycling sorter was tested on 22 new waste photos and got 21 right. How many did it get wrong?
39. 39.🎯 Rohan’s spam spotter was tested on 20 new messages and got 19 right. What is its accuracy in percent?
40. 40.🎯 Arjun’s leaf identifier is 70% accurate. If it is tested on 90 new leaf photos, how many should it get right?

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