Scholar Nest & Scholar Ship Agent: AI Powered Scholarship Eligibility Assessment System for Education Equity

Abstract

This paper presents the design, implementation and deployment of Scholar Nest, an AI powered web application that matches students in Karnataka with suitable scholarships. Scholar Nest combines a typed React frontend built with Vite, TypeScript, Tailwind CSS and shadcnui with a Supabase backend providing a PostgreSQL database, authentication, storage and row level security (RLS) policies. A separate Scholar Ship Agent, written in Python using Lang Chain and generative AI models, automates the discovery and extraction of scholarship information from official portals and documents. The system encodes student eligibility criteria, compares them to structured scholarship metadata and returns ranked results in a responsive dashboard. Implementation details, data model, agent pipeline, performance considerations, accessibility measures, security and deployment practices are documented in depth. The project demonstrates how open source technologies can be combined to promote education equity by reducing search time and improving access to financial aid opportunities.