Introduction

The process of selecting professors is a crucial aspect of a student's academic journey. Traditionally, students rely on word-of-mouth recommendations, online reviews, or manually browsing faculty profiles to make decisions about course selection. However, these methods are often time-consuming, inconsistent, and sometimes unreliable due to personal biases.

To address these challenges, this article explores the implementation of an AI-driven Professor Recommendation System, leveraging LLMs (Large Language Models), Retrieval-Augmented Generation (RAG), and cloud-based vector search to provide personalized professor recommendations based on real-time queries and student feedback.

By integrating AI and cloud computing, this system enables an interactive, scalable, and intelligent approach to professor selection. It makes use of OpenAI for natural language processing, Pinecone for vector database management, and a robust Next.js-based frontend to ensure smooth and dynamic user interactions. The goal is to create a system that not only provides information about professors but also continuously improves based on student feedback.

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Overview

Key Features

The intelligent recommendation model continuously enhances its accuracy as more feedback is collected, ensuring data-driven and reliable suggestions for students.

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System Architecture

The architecture of this system follows a modular microservices approach, integrating multiple technologies to enable efficient data retrieval, AI-driven responses, and seamless user interactions.

Architecture Diagram

Technology Stack

The system is built using a combination of cloud computing, AI, and modern web technologies, ensuring a fast, scalable, and efficient platform.

Frontend

• Developed using React & Next.js for dynamic, interactive UI components.
• Styled using MUI and CSS, providing an intuitive and user-friendly interface.
• Features an AI-powered chatbot to process natural language queries.

Backend

• Built with Node.js & Express.js to handle API requests and server-side logic.
• Manages the communication between the frontend, AI model, and vector database.

AI & Vector Search

• OpenAI API generates embeddings for user queries and professor metadata.
• Pinecone stores these vectorized embeddings, enabling fast and accurate searches.
• Implements Retrieval-Augmented Generation (RAG) to enhance the results retrieved from the database.

Authentication & Security

• Clerk authentication ensures that only authorized UPES students can access the system.
• Prevents unauthorized access and protects user data through secure authentication protocols.

Deployment

• Hosted on Vercel, which allows automatic scaling, global accessibility, and continuous deployment.
• Uses cloud-based serverless functions for backend operations.
• Implements secure environment variables for API keys and database credentials.

By leveraging these technologies, the system ensures optimal performance, robust security, and seamless scalability.

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Implementation & Workflow

1️⃣ User Query Processing

1. A student enters a query (e.g., “Tell me about Dr. Avita Katal”).
2. The query is converted into embeddings using OpenAI.
3. A RAG-based search retrieves relevant professor metadata and student feedback from Pinecone.
4. The system generates an AI-driven response with detailed professor information.

2️⃣ Feedback Collection

1. Students submit ratings & comments via the Feedback Modal.
2. Feedback is stored as vector embeddings in Pinecone.
3. Future professor searches incorporate both metadata and student feedback to refine recommendations.
   

3️⃣ Admin Management

1. Administrators add, update, or delete professor records via the Admin Panel.
2. Feedback insights help enhance the recommendation system and improve decision-making.

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📌 Publication Links

• 🌐 Live Website: AI-RateMyProfessor (🔒 Access restricted to UPES students only)
• 🎥 Demo Video: Watch Here (📌 The sign-in using the email ID in the demo video is for demonstration purposes only, and some features might be missing as we are constantly updating the system.)
• 🖥️ Source Code: GitHub Repository

🔹 Since the platform is designed exclusively for UPES students, access is restricted to institutional email addresses.
🔹 If you'd like to test the system, reach out to us at 📩 admn.prof12@gmail.com for demo access.

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Results & Discussion

The system successfully integrates AI, vector databases, and cloud computing to create an efficient, scalable, and user-friendly professor recommendation system.

Key Outcomes

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Future Enhancements

• 📌 Sentiment Analysis: AI-powered analysis of student feedback trends to enhance recommendations.
• 📌 Multi-School Expansion: Extend the system to support multiple universities.
• 📌 Enhanced AI Features: Improve chatbot intelligence with more contextual understanding and advanced natural language processing.

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Conclusion

This article serves as a demonstration of how AI and cloud computing can revolutionize academic decision-making. By integrating LLMs, Pinecone vector search, and cloud-based deployment, the system provides a scalable and intelligent solution for professor recommendations.

Through the implementation of modern AI-driven techniques, cloud-based deployment, and an intuitive user interface, the AI-Driven Professor Recommendation System exemplifies how agentic AI can improve real-world applications. As the project continues to evolve, future iterations will focus on enhanced AI capabilities, greater personalization, and wider accessibility.

This initiative serves as a stepping stone towards smarter, data-driven educational tools, bridging the gap between students and their academic mentors.