TrackWise - AI-Powered Course Recommendation System

TrackWise is an intelligent academic advising system that uses rule-based inference engines to provide personalized course recommendations for students in the Artificial Intelligence Science track.

🚀 Features

🤖 AI-Powered Inference Engine

• Rule-Based Reasoning: Uses Experta library for intelligent decision making
• Credit Limit Management: Automatically calculates credit limits based on CGPA
• Prerequisite Validation: Ensures all prerequisites are met before recommendations
• Co-requisite Handling: Manages co-requisite requirements
• Failed Course Priority: Prioritizes retaking failed courses when prerequisites are met
• Semester Availability: Only recommends courses offered in the current semester
• Track Alignment: Ensures recommendations align with AI Science track requirements

📊 Smart Recommendations

• Personalized Suggestions: Based on student's academic history and performance
• Credit Optimization: Maximizes credit hours within CGPA-based limits
• Priority System: Failed courses get higher priority for retaking
• Detailed Information: Shows prerequisites, credit hours, and semester availability

🎨 Modern Web Interface

• Beautiful UI: Modern, responsive design with gradient backgrounds
• Interactive Forms: Multi-select dropdowns with search functionality
• Real-time Feedback: Instant recommendations with detailed explanations
• Progress Tracking: Visual credit progress bars and summaries

📋 System Requirements

Credit Limits Based on CGPA:

• CGPA ≥ 3.5: 21 credits maximum
• CGPA ≥ 3.0: 18 credits maximum
• CGPA ≥ 2.5: 15 credits maximum
• CGPA < 2.5: 12 credits maximum

Recommendation Rules:

1. Prerequisites: All prerequisite courses must be passed
2. Co-requisites: Co-requisite courses must be passed or taken concurrently
3. Semester Availability: Only courses offered in the selected semester
4. Failed Course Priority: Failed courses are recommended first if prerequisites are met
5. Credit Limits: Total recommended credits cannot exceed CGPA-based limit

🛠️ Installation

1. Clone the repository:

   git clone <repository-url>
   cd TrackWise

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   cd UI
   python app.py

4. Access the application: Open your browser and go to http://localhost:5000

📁 Project Structure

TrackWise/
├── inference_engine/           # AI Inference Engine
│   ├── course_recommender.py   # Main inference logic
│   ├── example.py             # Standalone example
│   └── README.md              # Inference engine documentation
├── UI/                        # Flask Web Application
│   ├── app.py                 # Main Flask app
│   ├── inference_engine.py    # Flask blueprint for AI recommendations
│   ├── Client.py              # User authentication and routing
│   ├── KBSystem.py            # Legacy knowledge-based system
│   ├── KBEditor.py            # Knowledge base editor
│   ├── DB.py                  # Database operations
│   ├── templates/             # HTML templates
│   │   ├── index.html         # Login/signup page
│   │   ├── student.html       # Student dashboard
│   │   ├── course_recommendations.html  # AI recommendations page
│   │   └── admin.html         # Admin dashboard
│   ├── static/                # CSS, JS, and other static files
│   └── Data.csv              # Course database
├── Data.csv                   # Main course database
├── requirements.txt           # Python dependencies
└── README.md                 # This file

🎯 How to Use

For Students:

1. Sign Up/Login: Create an account or login as a student
2. Access Dashboard: Choose between AI-powered or legacy recommendations
3. AI Recommendations (Recommended):
   • Select your current semester (Fall/Spring)
   • Enter your CGPA
   • Select passed courses from the dropdown
   • Select failed courses (if any)
   • Click "Get AI Recommendations"
4. View Results:
   • See prioritized failed courses to retake
   • View new course recommendations
   • Check credit summary and progress
   • Review prerequisites for each course

For Administrators:

1. Login as Admin: Use admin credentials
2. Manage Knowledge Base: Add/edit/delete courses and rules
3. Monitor System: View system usage and performance

🧠 Inference Engine Details

The inference engine uses the Experta library to implement forward-chaining rule-based reasoning:

Core Classes:

• Course: Represents course information and status
• Student: Represents student academic information
• EligibleCourse: Represents courses eligible for recommendation
• CourseRecommender: Main inference engine with rules

Key Rules:

1. Credit Limit Rule: Sets maximum credits based on CGPA
2. Course Eligibility Rule: Evaluates prerequisites, co-requisites, and semester availability
3. Failed Course Priority Rule: Prioritizes failed courses
4. Regular Course Rule: Recommends other eligible courses

Example Usage:

from inference_engine.course_recommender import get_course_recommendations

recommendations = get_course_recommendations(
    student_cgpa=3.2,
    passed_courses=['CSE014', 'CSE015', 'MAT111', 'MAT112'],
    failed_courses=['CSE111'],
    current_semester='Spring',
    csv_path='Data.csv'
)

🔧 API Endpoints

Web Routes:

• GET /: Login/signup page
• GET /student_dashboard: Student dashboard
• GET /course_recommendations: AI recommendation form
• POST /course_recommendations: Process recommendations

API Routes:

• POST /api/recommendations: JSON API for getting recommendations

📊 Sample Data

The system includes comprehensive course data for the AI Science track including:

• Core CS courses (Programming, Data Structures, Algorithms)
• Mathematics courses (Calculus, Linear Algebra, Statistics)
• AI-specific courses (Machine Learning, Neural Networks, NLP)
• Elective courses and general education requirements

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

👥 Team

• Muhammed Mustafa
• Ramez Asaad
• Noureen Muhammed
• Rodina Mohamed
• Aya Mamdouh

📄 License

This project is developed as part of the AIE212 Knowledge-Based Systems course.

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TrackWise - Your intelligent academic companion! 🎓✨