Gator AI Summer Camp 2025

Introduction

This is a draft repository for the Gator AI Summer Camp 2025 program. The camp will teach students the basics of AI and how to apply AI to solve real-world problems.

Program Overview

• Week 1: Introduction to Python programming using game development
• Week 2: AI concepts and their application to real-world problems

Game Description

The project features a top-down adventure game, similar to The Legend of Zelda. Players will be able to:

• Move around the map
• Collect items
• Interact with NPCs

Code Standards

All code in this project should be:

• Well-documented with clear comments and docstrings
• Follow Python best practices for clean, maintainable code
• Organized logically to teach coding principles in order (variables → functions → classes → objects)

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Week 1: Introduction to Python Programming

Duration: ~4 hours per day
Schedule: 1 hour morning session + 2 hours afternoon + 1 hour evening with TAs

Day 1: Python Setup and Game Introduction

Objective: Set up programming environment and begin coding fundamentals.

Setup Tasks

• Install & Run IDE: Explain what IDEs are and set up development environment
• Install Python: Walk through Python installation (if not already installed)
• GitHub Setup: Sign-in or sign-up for GitHub account
• Download Project: Clone from Ian's GitHub Repository
• Hello World: Demonstrate running "Hello, World!" in a Python script

Core Concepts

• Basic Python Syntax
  • Simple print statements and comments
  • Indentation rules in Python
• Libraries and Importing: Explain how to import and use external libraries
• Game Environment Overview
  • Introduce existing game framework
  • Show game file locations and how to run the game
• Variables & Data Types (Basic)
  • Integers, floats, strings
  • Assign simple values (character names, positions, etc.)

Exercise: Customize a Splash Screen

• Modify "Hello, World!" splash screen to display welcome message
• Use variables to store player name or game title
• Deliverable: Push project to personal GitHub repository

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Day 2: Sprites and Game Logic

Objective: Explore game mechanics and make active changes to game code.

Core Concepts

• Loading Character Sprites
  • Import image files (PNGs, JPGs) into game code
  • Position sprites on screen
• Basic Conditionals
  • if statements for simple conditions
  • Examples: button presses, health checks
• Simple Functions
  • Create functions like move_character()
  • Demonstrate code organization benefits

Exercise: Adding Your First Character

• Add a single character sprite to the game
• Use Piskel to create unique sprites
• Experiment with character position and appearance
• Deliverable: Push project to personal GitHub repository

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Day 3: Dialogue Trees and Interactions

Objective: Create branching conversations and interactive dialogue systems.

Core Concepts

• Introduction to Dialogue Trees

  • Branching conversation options
  • Store dialogue in data structures (lists/dictionaries)

• Using Lists or Dictionaries (Basic)

  Example dictionary approach:

  dialogue = { 
    "greeting": "Hello, traveler!", 
    "option1": "Where am I?", 
    "option2": "Who are you?" 
  }

• Loops for Navigating Dialogue

  • for loops to cycle through dialogue options
  • Conditionals for conversation branching

Exercise: Implementing a Basic Conversation

• Give newly added sprite a short conversation
• Use input() for dialogue selection and response branching
• Deliverable: Push project to personal GitHub repository

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Day 4: Advanced Dialogue and Debugging

Objective: Enhance dialogue systems, add sprite interactions, and learn debugging techniques.

Core Concepts

• Refining Dialogue Trees
  • Multi-step dialogue structures
  • Explain this as the original "AI"
• Sprite Interaction
  • Tie sprite actions to dialogue choices
  • Change expressions based on conversation branches
  • Sprite movement (walking away, disappearing)
• Basic Debugging & Code Organization
  • Using print() statements to track variables
  • Common syntax errors and how to read error messages
  • Organizing dialogue code in separate files/functions

Exercise: Complex Dialogue

• Create multi-branch dialogue for character
• Optional: Group brainstorming for final projects
• Deliverable: Push project to personal GitHub repository

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Day 5: Project Finalization and Presentations

Objective: Polish game modifications and present final projects.

Activities

• Polish & Personalization
  • Add custom sprites, dialogue branches, animations
• Testing
  • Test all dialogue paths and sprite interactions
  • Use checklist approach: sprite display, dialogue functionality
• Show & Tell
  • Demonstrate modified games
  • Highlight added characters and dialogue systems
• Reflection & Next Steps
  • Group discussion: Python learnings and challenges
  • Review group projects (additional characters, scenes, menus)
• Deliverable: Push final project to personal GitHub repository

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Week 2: Introduction to Artificial Intelligence

Day 1: AI Concepts and Environment Setup

Objective: Learn AI fundamentals and prepare development environment.

Core Concepts

• Show Week Plan: Overview of full week schedule
• What Is AI/ML?
  • Differentiate AI, Machine Learning, and Deep Learning
  • Real-world applications (image recognition, chatbots, etc.)
• Facial Recognition Overview
  • Face detection vs. recognition vs. expression recognition
  • Introduction to libraries (OpenCV, face_recognition)
• Setting Up AI Environment
  • Install Python libraries (opencv-python, face_recognition)
  • Discuss CPU/GPU considerations or use Google Colab
• Data Gathering & Model Training Basics
  • Using images/webcam for training/fine-tuning
  • Dataset labeling, data requirements
  • Ethical considerations: privacy, consent

Hands-On Exercise

• Verify AI library installations (pip install opencv-python)
• Optional: Test script for face detection from webcam/image

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Day 2: Facial Recognition Model Training

Objective: Learn model training concepts and implement expression recognition.

Core Concepts

• Model Training Fundamentals
  • Training vs. validation vs. test data
  • Small dataset of facial expressions (happy, sad, neutral)
  • Students capture labeled images
• Implementing Training Pipeline
  • Code walkthrough for training/fine-tuning expression models
  • Focus on understanding how models learn from data
• Testing & Evaluating Models
  • Run model on test images
  • Print accuracy and confusion matrix
• Saving Models
  • Save trained model to file (.h5 or .pkl)
  • Loading models for game integration

Hands-On Exercise

• Follow guided notebook/script to train expression recognition model
• Test on personal images or sample photos
• Check and discuss accuracy results

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Day 3: Large Language Models and APIs

Objective: Understand LLMs and integrate them via API calls.

Core Concepts

• Overview of Large Language Models (LLMs)
  • What LLMs are and how they're trained
  • Natural language response capabilities
  • Common providers (OpenAI, Hugging Face, etc.)
• API Setup
  • Obtain API keys (instructor-guided)
  • Basic Python script for LLM endpoint requests
  • Using requests library or official client libraries
• Handling LLM Responses
  • Parse JSON/text responses
  • Understanding token usage and limits

Hands-On Exercise

• Write Python script to send prompts to LLM
• Print console responses
• Generate dialogue lines or short stories
• Use original dialogue as context pre-prompts

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Day 4: Game Integration - Facial Recognition + LLM

Objective: Combine facial recognition and LLM technologies in the game.

Core Concepts

• Loading Facial Recognition Model
  • Load saved model from Day 2 into game code
  • Integrate webcam/image capture for real-time analysis
  • Snapshot-based approach (keypress to check expression)
• API Calls from In-Game
  • Combine LLM API script with game logic
  • Dynamic prompts based on recognized expressions
  • Example: "User is smiling. NPC responds with friendly greeting."
• Designing Interaction Flow
  • Craft LLM prompts with contextual info
  • Map expressions to conversation branches (happy → friendly, sad → consoling)
• Error Handling & Debugging
  • Handle API downtime or recognition failures
  • Use print() statements for debugging

Hands-On Exercise

• Incorporate model + LLM calls into game dialogue function
• Group testing: one person as "player" (webcam), other monitors code

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Day 5: Final Demonstrations and Reflection

Objective: Complete AI-enhanced projects and present to group.

Activities

• Polish & Prepare
  • Refine dialogue flows and sprite reactions
  • Add more expression-to-prompt logic
• Presentation of AI-Enhanced NPCs
  • Demonstrate expression recognition (smile, frown, etc.)
  • Show LLM-generated dialogue responses
• Discussion & Troubleshooting
  • Group discussion: successes, challenges, real-world AI considerations
  • Topics: scale, bias, privacy, ethics
• Next Steps & Further Learning
  • Areas for exploration: reinforcement learning, advanced computer vision, complex game design
  • Work on group projects as time allows
• Final Reflection
  • Share one AI learning, one challenge, one future interest area
  • Optional: Feedback forms or understanding quizzes

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Resources

• Python Documentation
• Sprite Sheet Editor: Piskel
• OpenCV Documentation
• GitHub Desktop (for easier Git management)

Technical Requirements

Python Libraries

pip install pygame
pip install opencv-python
pip install requests
# Additional libraries as needed for specific AI frameworks

Hardware Recommendations

• Minimum: 4GB RAM, integrated graphics
• Recommended: 8GB+ RAM, dedicated graphics card for AI processing
• Webcam: Required for facial recognition exercises

Attribution

• Game art assets from OpenGameArt.org, licensed under CC0 1.0 Universal (CC0 1.0) Public Domain Dedication. Author: ArMM1998
• Educational framework inspired by game-based learning methodologies

Contributing

This is an educational repository. Contributions should focus on:

• Improving code clarity and documentation
• Adding beginner-friendly examples
• Enhancing learning progressions
• Fixing bugs or compatibility issues

License

This project is intended for educational use. Please respect the licensing terms of all included assets and libraries.