CAI-5724 Demo Workspace

An instructional sandbox demonstrating how to use GitHub Copilot across multiple languages & contexts: Python (notebook), Lua (LÖVE2D game), and guidance for scaffolding a small React medical app.

📁 What's in This Repo

File	Purpose
copilot_lesson.ipynb	Interactive lesson: concepts, ethics, shortcuts, and hands‑on Copilot exercises (BMI calculator, class design, LÖVE2D + React prompts).
main.lua	Entry point for the LÖVE2D mini game exercise (you'll iteratively build or replace contents using Copilot Chat).
README.md	You are here—overview, setup, and usage instructions.

🧠 Learning Goals

Using this workspace you will:

1. Understand what GitHub Copilot & Copilot Chat can (and cannot) do.
2. Practice comment‑to‑code and inline chat driven development in Python.
3. Build / iterate on a simple LÖVE2D (Lua) game loop with AI assistance.
4. Plan a basic React “medical records” interface (prompts & workflow guidance).
5. Reflect on ethical, licensing, and integrity considerations when using AI.

📝 Notebook Lesson

Open copilot_lesson.ipynb inside VS Code / Codespaces. It walks you through:

• Copilot basics & activation
• Keyboard shortcuts & chat slash commands (/explain, /fix, /tests, /doc, /optimize)
• Responsible use & academic integrity reminders
• Guided Python exercises (BMI, dosage function, patient record class)
• Prompt patterns for iterative refinement
• Exercises for Lua (game) and React (medical app UI)

Tip: Use inline chat (Ctrl+I) right inside a code cell or file to request specific functionality without leaving your context.

🏃 Running the LÖVE2D Demo (Lua)

This project uses LÖVE—a lightweight 2D game framework.

Inside the dev container, run:

DISPLAY=:0 love .

Why DISPLAY=:0? The container needs an X display target. In Codespaces with a GUI / forwarded display extension this directs LÖVE's window to the expected display. If you're running locally outside the container you can usually just run:

love .

Iterative Workflow

1. Open main.lua.
2. Clear or simplify existing content.
3. Ask Copilot Chat: “Create a LÖVE2D game with a bouncing ball that changes color on edge collisions.”
4. Run the command above to test.
5. Iterate: add player control, scoring, particles, etc., by prompting Chat (e.g., “Add arrow key control” / “Add a trail effect”).

🧪 Python Exercises

In the notebook you’ll:

• Implement calculate_bmi and a dosage calculator.
• Write tests (prompt Copilot: “/tests generate pytest tests for this function”).
• Design a PatientRecords class (ask for validation rules, stats, or docstrings).

Suggested Inline Chat Prompts

Try these while your cursor is in a code cell:

• “Add input validation & raise ValueError for invalid height.”
• “Refactor to a dataclass.”
• “Generate edge case tests.”

🌐 React Medical App (Concept Phase Here)

The notebook guides you to start a new React app (outside this minimal repo). Suggested flow:

1. Ask: “Scaffold a React app for a simple patient dashboard (no backend).”
2. Follow up: “Add a component to filter patients by condition and show basic stats.”
3. Ask for accessibility improvements: “Ensure ARIA roles and keyboard navigation.”

🤖 Effective Copilot Usage

Scenario	Strategy
Unsure of API	Write a high‑level comment, pause for suggestion.
Need refactor	Select code → /optimize or ask “Simplify and explain.”
Understanding unfamiliar code	Highlight → /explain
Generate tests	Select function → /tests
Fix an error	Select block / file → /fix
Multi‑file change reasoning	Use @workspace + describe the goal.

🔐 Ethics & Integrity Highlights

• Review every suggestion—don’t blindly accept.
• Avoid pasting sensitive or proprietary data into prompts.
• Cite Copilot usage if required by course policy.
• Run security & logic reviews (especially for medical / regulated contexts).

🧩 Project Structure (Current Minimal Form)

.
├── copilot_lesson.ipynb   # Guided lesson & exercises
├── main.lua               # Lua game entry point (build via Copilot)
└── README.md              # Documentation

✅ Suggested Next Enhancements

• Add a requirements.txt if you formalize Python dependencies.
• Create a tests/ folder and persist generated pytest tests.
• Add a starter main.lua template with comments for each LÖVE callback.
• Scaffold a separate React subfolder (/react-app) once ready.

❓ Troubleshooting

Issue	Check
No Copilot suggestions	Is extension enabled / signed in? Rate limit?
LÖVE window not appearing	DISPLAY variable / GUI forwarding support
Chat can’t reference files	Ensure they are saved & opened; use @workspace.
Repetitive suggestions	Rephrase prompt: include intent + constraints.

📜 License / Attribution

No explicit license included yet—add one (e.g., MIT) before distributing. Remember: you are responsible for reviewing and validating all AI‑assisted code.

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Happy experimenting! Try stretching each exercise: add scoring to the game, persist patient data, or benchmark alternative implementations suggested by Copilot.