🎓 XJTLU Academic Navigator

An MCP (Model-Context-Protocol) based AI assistant for XJTLU Economics students. This project demonstrates how commercial AI systems can integrate fragmented academic data sources without requiring official API access.

🔍 Project Vision

Current academic systems at XJTLU (Learning Mall, e-Bridge) operate in isolation, forcing students to manually synthesize information. Our solution provides:

• ✨ Course explanations contextualized to career goals (e.g., HKU MFWM preparation)
• 📅 Personalized semester planning based on academic background and credit constraints
• 🎯 Career pathway analysis mapping XJTLU Economics courses to target graduate programs
• 🔍 Prerequisite validation and workload assessment for optimal course selection

🏗️ MCP Architecture

Our system implements a strict Model-Context-Protocol separation:

| Component | Role | Implementation | |-----------|------|----------------| | Model | AI reasoning engine | DeepSeek API integration with professional fallbacks | | Context | Academic knowledge base | Mock data derived from official XJTLU Economics programme specification | | Protocol | Standardized communication | JSON message format with intent routing and validation rules |

Key Architectural Components:

• Dispatcher: Intent recognition and request routing
• Orchestrator: Multi-agent coordination for complex queries
• Course Service: Semantic search over curriculum data
• Planning Service: Rule-based semester planning with AI enhancement
• AI Service: Unified interface for LLM interactions with graceful degradation

🛡️ Compliance & Ethics

This project strictly adheres to ethical and compliance standards:

• ✅ No access to real student data - all data is synthetic/mock
• ✅ No scraping of XJTLU systems - course data derived from publicly available programme specifications
• ✅ Transparent AI sourcing - all AI-generated content is clearly marked
• ✅ Privacy by design - no user data persistence in demo version
• ✅ Academic integrity - all course descriptions accurately reflect official XJTLU curriculum

🚀 Getting Started

Prerequisites

• Python 3.9+
• Node.js (optional, for frontend enhancements)
• DeepSeek API Key (optional - mock mode available)

Installation

# Clone repository
git clone https://github.com/yourusername/xjtlu-academic-navigator.git
cd xjtlu-academic-navigator

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# venv\Scripts\activate   # Windows

# Install dependencies
pip install -r requirements.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your DeepSeek API key (or keep USE_MOCK_AI=true)