AI-Powered Learning Management System

Traditional learning management systems face significant challenges in providing personalized, context-aware academic support. Students and faculty often struggle with accessing relevant institutional information, finding specific course details, and getting immediate responses to academic queries.

Key challenges in existing educational systems include:

• Information Fragmentation: Academic resources scattered across multiple platforms and documents
• Limited Accessibility: Lack of 24/7 support for student queries and academic guidance
• Generic Responses: One-size-fits-all solutions that don't understand institutional context
• Manual Knowledge Retrieval: Time-consuming searches through policies, course catalogs, and guidelines
• Language Barriers: Limited support for natural language interaction and voice-based queries

We developed a sophisticated AI-powered LMS assistant that combines fine-tuned large language models with retrieval-augmented generation (RAG) to deliver accurate, context-aware academic support tailored specifically for RV College of Engineering.

Core Features:

Technical Stack:

Data Sources:

• RVCE-Specific Dataset: Course details, faculty information, academic policies, student guidelines
• Harvard Public Domain Corpus: General academic knowledge enhancement
• Structured Documents: Machine-readable institutional resources with vector embeddings

The LMS assistant follows a modular architecture designed for scalability, efficiency, and privacy-focused deployment.

Key Components:

• Frontend Interface: React.js with Tailwind CSS for responsive UI supporting text and voice interaction
• Backend API: Node.js server handling query processing and AI model communication
• AI Processing Unit: Fine-tuned LLaMA 3.1-8B model with integrated RAG system
• Vector Database: FAISS-based storage for academic document embeddings
• Local Deployment: Ollama framework for privacy-focused, offline operation

Through comprehensive evaluation and optimization, the AI-powered LMS assistant demonstrates exceptional performance in accuracy, speed, and user satisfaction for academic queries.

Model Performance Metrics:

Optimization Achievements:

• QLoRA Fine-tuning: Efficient model adaptation with minimal computational overhead
• 4-bit Quantization: Reduced memory consumption while maintaining accuracy
• Vector Compression: Optimized embedding storage for faster retrieval
• Voice Integration: Seamless speech-to-text and text-to-speech functionality
• Local Execution: Privacy-focused deployment without cloud dependency

Competitive Advantages:

• Domain Specialization: Outperforms general AI models like GPT-3.5 in RVCE-specific queries
• Privacy Protection: Local execution ensures sensitive academic data remains secure
• Real-time Knowledge: RAG system provides up-to-date information retrieval
• Multimodal Support: Text and voice interaction capabilities for accessibility

The project implements cutting-edge AI techniques combining fine-tuning, retrieval-augmented generation, and efficient deployment strategies for optimal performance.

Fine-tuning Process:

• Dataset Preparation: Academic content formatted into question-answer pairs and context-based queries
• QLoRA Optimization: Quantized Low-Rank Adaptation for efficient parameter updates
• Hyperparameter Tuning: Optimized batch size, learning rate, and weight decay to prevent overfitting
• Tokenization: Byte-Pair Encoding (BPE) for optimal language model input processing

RAG Implementation:

• Embedding Generation: Academic documents converted to vector embeddings using Ollama models
• Vector Database: FAISS indexing for fast similarity search and retrieval
• Context Integration: Retrieved documents combined with queries for enhanced response generation
• Real-time Processing: Dynamic knowledge retrieval for current and accurate information

System Requirements:

The current system provides a solid foundation for advanced educational AI applications with several planned improvements and expansions.

Planned Improvements:

• Dataset Expansion: Additional course materials, research papers, and institutional resources
• Enhanced Voice Processing: Improved speech-to-text models with better accent recognition
• Cloud Deployment: Scalable cloud-based version for broader institutional access
• Multi-language Support: Expansion to support regional languages for diverse student populations
• Advanced Analytics: Usage patterns analysis and personalized learning recommendations