Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 267 · 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.📋 Planning an AI project: which comes first — tag each photo by type or calculate percent right? (a) tag each photo by type (b) calculate percent right
2. 2.🎯 Nisha’s leaf identifier was tested on 4 new leaf photos and got 2 right. What is its accuracy in percent?
3. 3.🎯 Diya’s bird-song app was tested on 20 new bird songs and got 12 right. What is its accuracy in percent?
4. 4.📋 Planning an AI project: which comes first — split train and test or calculate percent right? (a) split train and test (b) calculate percent right
5. 5.🎯 Nisha’s recycling sorter was tested on 5 new waste photos and got 4 right. What is its accuracy in percent?
6. 6.📋 Planning an AI project: which comes first — test on new photos or train the model? (a) test on new photos (b) train the model
7. 7.🧪 What is accuracy? (a) total × 2 (b) correct ÷ total × 100 (c) total − correct (d) correct + total
8. 8.📋 Planning an AI project: which comes first — label each example or calculate percent right? (a) label each example (b) calculate percent right
9. 9.📋 Planning an AI project: which comes first — try unseen examples or tag each photo by type? (a) try unseen examples (b) tag each photo by type
10. 10.📋 Planning an AI project: which comes first — improve data, retest or try unseen examples? (a) improve data, retest (b) try unseen examples
11. 11.📦 Rohan collected 170 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
12. 12.📋 Planning an AI project: which comes first — label each example or set aside test photos? (a) label each example (b) set aside test photos
13. 13.📋 Planning an AI project: which comes first — work out the accuracy or pick a question to solve? (a) work out the accuracy (b) pick a question to solve
14. 14.📦 Diya collected 200 labelled handwritten letters for a handwriting reader and keeps one fifth of them for testing. How many are left for training?
15. 15.🧪 Why must you test a model on examples it has NOT seen? (a) to make it bigger (b) to save paper (c) to see if it learned (d) to make it faster
16. 16.❌ Diya’s fruit sorter was tested on 43 new fruit photos and got 35 right. How many did it get wrong?
17. 17.📋 Planning an AI project: which comes first — split train and test or tag each photo by type? (a) split train and test (b) tag each photo by type
18. 18.🎯 Arjun’s leaf identifier is 70% accurate. If it is tested on 10 new leaf photos, how many should it get right?
19. 19.📋 Planning an AI project: which comes first — gather many examples or tag each photo by type? (a) gather many examples (b) tag each photo by type
20. 20.📋 Planning an AI project: which comes first — let it learn patterns or improve data, retest? (a) let it learn patterns (b) improve data, retest
21. 21.❌ Tara’s leaf identifier was tested on 29 new leaf photos and got 26 right. How many did it get wrong?
22. 22.🧪 You can plan a simple AI project with… (a) only a big robot (b) a secret password (c) paper and examples (d) nothing at all
23. 23.📋 Planning an AI project: which comes first — correct bad labels or split train and test? (a) correct bad labels (b) split train and test
24. 24.📋 Planning an AI project: which comes first — fix it and test again or calculate percent right? (a) fix it and test again (b) calculate percent right
25. 25.⚖️ Two leaf identifier models were tested on the same 50 leaf photos. Model A got 39 right and Model B got 38 right. How many percentage points more accurate is Model A?
26. 26.📋 Planning an AI project: which comes first — pick a question to solve or collect fruit photos? (a) pick a question to solve (b) collect fruit photos
27. 27.🎯 Ananya’s leaf identifier was tested on 50 new leaf photos and got 27 right. What is its accuracy in percent?
28. 28.🎯 Arjun’s leaf identifier was tested on 25 new leaf photos and got 14 right. What is its accuracy in percent?
29. 29.⚖️ Two spam spotter models were tested on the same 20 messages. Model A got 15 right and Model B got 11 right. How many percentage points more accurate is Model A?
30. 30.📋 Planning an AI project: which comes first — split train and test or let it learn patterns? (a) split train and test (b) let it learn patterns
31. 31.📋 Planning an AI project: which comes first — collect fruit photos or recheck every label? (a) collect fruit photos (b) recheck every label
32. 32.🎯 Nisha’s handwriting reader is 50% accurate. If it is tested on 80 new handwritten letters, how many should it get right?
33. 33.📦 Vihaan collected 170 labelled waste photos for a recycling sorter and keeps one fifth of them for testing. How many are left for training?
34. 34.🎯 Ishaan’s leaf identifier was tested on 50 new leaf photos and got 38 right. What is its accuracy in percent?
35. 35.📋 Planning an AI project: which comes first — split train and test or correct bad labels? (a) split train and test (b) correct bad labels
36. 36.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) more labelled examples (b) a louder speaker (c) a new colour screen (d) fewer examples
37. 37.❌ Arjun’s fruit sorter was tested on 43 new fruit photos and got 35 right. How many did it get wrong?
38. 38.📋 Planning an AI project: which comes first — try unseen examples or work out the accuracy? (a) try unseen examples (b) work out the accuracy
39. 39.❌ Saanvi’s fruit sorter was tested on 45 new fruit photos and got 40 right. How many did it get wrong?
40. 40.📋 Planning an AI project: which comes first — correct bad labels or train the model? (a) correct bad labels (b) train the model

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