AI Testing and Evaluation Checklist

Lesson 12: Build Your Own AI - Hands-On Project

Evolve AI Institute | Free AI Education for All

Testing Tip: Good AI developers test early and often! Don't wait until your project is "perfect" to start testing. Testing helps you identify problems early and guides your improvements. Remember: iteration is the key to success!

Pre-Testing Checklist

Before you begin testing, make sure you have:

🔄 Iteration 1: Initial Testing

Testing Phase

Date of Testing:

Conduct the following tests and record your results:

Test Type Results / Observations
Similar to Training Data
Test with samples very similar to your training data
Different Conditions
Test with different lighting, angles, or backgrounds
Edge Cases
Test ambiguous or borderline examples
Unknown Items
Test with items not in any category
Peer Testing
Have a classmate test your AI

Correct Predictions

____

out of ____ tests

Accuracy Rate

____%

calculated percentage

Confidence Level

____

Low / Medium / High

Evaluation Phase

What worked well? What are the strengths of your AI?
What didn't work well? What problems did you identify?
Which categories were confused most often? Why do you think this happened?

Improvement Plan

What specific changes will you make to improve your AI?
🔄 Iteration 2: Refinement Testing

Testing Phase

Date of Testing:
What changes did you implement from Iteration 1?
Test Type Results / Observations
Retest Previous Problem Areas
Focus on what didn't work in Iteration 1
New Test Cases
Try tests you didn't do before
Real-World Scenarios
Test in actual use conditions
Stress Testing
Test difficult or unusual cases

Correct Predictions

____

out of ____ tests

Accuracy Rate

____%

calculated percentage

Improvement

+____%

compared to Iteration 1

Evaluation Phase

Did your changes improve the AI's performance? How?
Are there still problems that need to be addressed? What are they?

Improvement Plan

What additional improvements will you make?
🔄 Iteration 3: Final Testing (Optional)

Testing Phase

Date of Testing:
Final changes implemented:
Test Type Results / Observations
Comprehensive Testing
Run all previous test types
User Acceptance Testing
Have multiple people test
Documentation
Record any remaining issues

Final Accuracy

____%

overall performance

Total Improvement

+____%

from Iteration 1

User Satisfaction

____

Low / Med / High

Final Reflection

Overall, how would you rate your AI project's success? (Circle one)
Not Successful     1   2   3   4   5     Very Successful
What did you learn about the AI development process?
What was the most challenging part of this project?
What was the most rewarding part of this project?
If you had more time, what would you improve or add to your AI?
How could this AI be used in the real world? Who would benefit from it?

Key Learnings About AI

Based on your experience, rate your understanding of these AI concepts:

1 = Don't understand, 2 = Somewhat understand, 3 = Understand, 4 = Understand well, 5 = Could teach others

Concept Rating (1-5)
How training data affects AI performance
The importance of diverse and balanced datasets
How AI models learn patterns from examples
The iterative nature of AI development
How to test and evaluate AI performance
Limitations and challenges of AI systems
Congratulations on completing your AI project! You've experienced the complete AI development lifecycle from planning through testing and iteration. This hands-on experience has given you valuable insights into how AI really works - not just using it, but creating it. Keep building, keep learning, and keep innovating!

Teacher Evaluation

Teacher comments on testing process and iteration:
Criteria Points Earned
Thoroughness of testing (multiple test types completed) _____ / 20
Documentation quality (clear, detailed observations) _____ / 20
Iteration and improvement (evidence of refinement) _____ / 25
Analysis and reflection (thoughtful evaluation) _____ / 20
Final AI performance (accuracy and functionality) _____ / 15
Total _____ / 100