AI Learning Assistant

🎓 Advanced AI Learning Assistant

A sophisticated educational AI system built with the Model Context Protocol (MCP) and OpenAI's Agent SDK

🚀 Key Features

1. 🧠 Socratic Dialogue Engine

Guides students through discovery learning using probing questions instead of direct answers.

2. 🗺️ Intelligent Learning Path Generator

Creates personalized, week-by-week curricula with:

• Hands-on projects at each milestone
• Industry-relevant resources
• Built-in assessments
• Realistic timeframes

3. 🔗 Concept Prerequisite Mapper

Generates knowledge dependency graphs showing what concepts are needed before others.

4. 📊 Knowledge Gap Analyzer

Analyzes student explanations to provide:

• Specific gap identification
• Misconception correction
• Personalized remediation strategies

5. 🔍 Professional Code Reviewer

Enterprise-grade analysis covering:

• Bug detection and edge cases
• Style and readability (PEP 8)
• Performance optimizations
• Security vulnerabilities
• Best practices and design patterns

6. 🎯 Adaptive Practice Problem Generator

Creates varied problems with:

• Multiple difficulty levels
• Hints and solutions
• Common mistakes to avoid
• Follow-up challenges

7. 📚 Research Paper Analyzer

Multi-level summaries including:

• ELI5 explanations
• Technical breakdowns
• Critical analysis
• Practical implications

8. 📅 Smart Study Scheduler

Optimizes learning with:

• Spaced repetition
• Active recall techniques
• Realistic time allocation
• Progress checkpoints

9. 📰 News Newsletter Generator

Aggregates and curates recent news with:

• Article summaries
• Trend analysis
• Multiple output styles
• Original source links

🏗️ Architecture

┌──────────────┐         MCP over STDIO        ┌───────────────┐
│              │◄─────────────────────────────►│               │
│   agent.py   │                                │ mcp_server.py │
│   (Client)   │   Tool Calls & JSON Results   │   (Server)    │
│              │◄───────────────────────────────┤               │
└──────────────┘                                └───────┬───────┘
                                                        │
     Agent SDK                                          │
     - Tool Discovery                           OpenAI API
     - Context Management                       - GPT-4o-mini
     - Timeout Management                       - Structured Output
     - Conversation Flow                        - JSON Mode

📋 Prerequisites

• Python 3.10 or higher
• OpenAI API key
• (Optional) NewsAPI key for newsletter feature

🚀 Installation

1. Clone the Repository

git clone https://github.com/yourusername/advanced-ai-learning-assistant.git
cd advanced-ai-learning-assistant

2. Create Virtual Environment

# Windows
python -m venv venv
venv\Scripts\activate

# Mac/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file in the project root:

# Required
OPENAI_API_KEY=sk-your-openai-key-here

# Optional (for news newsletter feature)
NEWSAPI_KEY=your-newsapi-key-here

Get API Keys:

• OpenAI: https://platform.openai.com/api-keys
• NewsAPI: https://newsapi.org/register (free tier: 100 requests/day)

💻 Usage

Start the Assistant

python agent.py

Example Interactions

1. Create a Learning Path:

💬 You: Create a 12-week learning path for machine learning

🤖 Assistant: [Comprehensive JSON with weekly curriculum, projects, resources]

2. Map Prerequisites:

💬 You: What prerequisites do I need for understanding transformers?

🤖 Assistant: [Dependency graph showing required concepts]

3. Review Code:

💬 You: Review this Python code:
def fib(n):
    if n <= 1: return n
    return fib(n-1) + fib(n-2)

🤖 Assistant: [Detailed analysis with performance issues, suggestions, examples]

4. Generate Newsletter:

💬 You: Create a newsletter with top 10 AI news from this week

🤖 Assistant: [Curated newsletter with summaries and links]

5. Assess Knowledge:

💬 You: Assess my understanding of neural networks: [your explanation]

🤖 Assistant: [Personalized feedback with gaps and remediation]

Special Commands

• help - Show examples and available tools
• clear - Reset conversation context
• exit / quit / bye - Exit the program

📁 Project Structure

advanced-ai-learning-assistant/
│
├── agent.py                 # Main agent client with chat interface
├── mcp_server.py           # MCP server with 9 advanced tools
├── .env                    # Environment variables (create this)
├── .env.example           # Template for environment variables
├── requirements.txt       # Python dependencies
└── README.md              # This file

🔧 Configuration

Adjust Timeouts

In agent.py:

mcp_server = MCPServerStdio(
    name="Advanced AI Tutor",
    params={...},
    timeout=300,  # Server process timeout
    client_session_timeout_seconds=300  # Session timeout
)

Change Model

In mcp_server.py:

MODEL_NAME = "gpt-4o"        # Best quality
MODEL_NAME = "gpt-4o-mini"   # Fast & economical (default)
MODEL_NAME = "gpt-4-turbo"   # High quality

Customize Tool Behavior

Edit functions in mcp_server.py:

• Modify prompts in each *_fn() function
• Adjust parameters (difficulty levels, timeframes, etc.)
• Add new tools following the existing pattern

🧪 Testing

Test NewsAPI Connection

python test_newsapi.py

Test MCP Server Standalone

python mcp_server.py

Run Agent in Debug Mode

# Windows
set DEBUG=true
python agent.py

# Mac/Linux
DEBUG=true python agent.py

🎯 Future Enhancements

• [ ] Vector database integration for RAG
• [ ] Multi-agent collaboration
• [ ] Web interface (Gradio/Streamlit)
• [ ] File upload support (PDFs, code files)
• [ ] Conversation history persistence
• [ ] Analytics dashboard
• [ ] Multi-language support
• [ ] LMS platform integration

📚 Resources & References

• Model Context Protocol Docs
• OpenAI Agents SDK
• OpenAI API Reference