AI Usage Guidelines for In-Class Labs

🎯 Core Principle: AI as Learning Partner, Not Solution Provider

Before Using AI:

1. Attempt First Policy

• Try each exercise independently for at least a few minutes
• Write pseudocode or outline your approach before asking for code
• Identify specific concepts you’re struggling with rather than asking for complete solutions

2. Set Learning Intentions

• Define what you want to understand, not just what you need to accomplish
• Ask yourself: “What programming concept is this exercise teaching me?”

During AI Interaction:

• Avoid attaching or copying the entire Lab to AI.
• Express prompts in your own words rather than copying exercise questions.

3. Question-Driven Learning

• Good AI prompts:
  • “Why does this expression need parentheses?”
  • “What’s the difference between int() and float() conversion?”
  • “Explain why this error message appeared”
  • “Show me two different ways to format this output”
• Avoid these prompts:
  • “Write code for exercise 2.1”
  • “Give me the answer to this lab”
  • “Fix all my errors”

4. Incremental Help

• Ask for help with one concept at a time
• Request explanations before requesting code
• Ask for hints rather than complete solutions

5. Verification Focus

• Use AI to explain why test cases like 32°F → 0°C are important
• Ask AI to suggest additional test cases you should try
• Request explanations of what could go wrong with your code

After AI Assistance:

6. Active Learning Verification

• Explain the AI’s solution back in your own words
• Modify the code with different values to ensure you understand it
• Create a similar problem and solve it without AI help

7. Pattern Recognition

• Identify what programming patterns you learned
• Note which concepts you can now apply to other problems
• Document your understanding in comments

Self Reflection Questions:

After each exercise consider how you would respond to the following questions:

• “Explain your problem-solving process”
• “What did you learn from the AI interaction?”
• “How would you solve a similar problem without AI?”
• “What questions did you ask AI and why?”
• “What concept did this exercise teach you?”
• “What would you do differently if solving this again?”
• “How did AI help/hinder your learning process?”

These are questions that may be discussed in class.

Follow-up Challenges:

After completing each exercise with AI help:

1. Variation Challenge: Solve a similar problem with different requirements
2. Teaching Challenge: Explain the concept to a classmate
3. Debugging Challenge: Intentionally introduce an error and fix it
4. Efficiency Challenge: Find an alternative approach

Red Flags - Stop and Reassess:

• ❌ You can’t explain any line of your code
• ❌ You copied AI output without testing different inputs
• ❌ You didn’t attempt the problem before asking AI
• ❌ You can’t solve a similar problem without AI assistance
• ❌ You asked for complete solutions rather than concept explanations

This approach transforms AI from a “lab completion tool” into a “learning amplifier” that helps you understand concepts more deeply while maintaining the educational value of hands-on practice.