A Comprehensive Approach to Personalized Scholarship Matching through Machine Learning

Abstract: The increasing disparity in educational opportunities has sparked an urgent need for systems that can bridge the gap by ensuring that financial aid reaches the most deserving candidates. This paper introduces an AI-powered scholarship eligibility checker designed to automate and refine the process of scholarship evaluation. Leveraging state-of-the-art artificial intelligence techniques, machine learning algorithms, and robust data analytics, the system is engineered to identify qualified applicants accurately while mitigating human bias. By integrating comprehensive datasets including academic records, socioeconomic indicators, and historical scholarship data, this tool aims to not only expedite the evaluation process but also to enhance transparency in scholarship distribution. The survey explores the system’s architecture, the methodological framework, integration challenges, ethical considerations, and the potential impact on education equity.

Keywords: Artificial Intelligence, Scholarship Eligibility, Education Equity, Machine Learning, Data Analytics, Decision Support Systems.