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@article{178672,
        author = {Anuj Nagare and Apeksha Wadghule and Gayatri Mule and Shreya Tagalpallewar and Prof. S. L. Dawkhar},
        title = {AI-Driven Scholarship Finder},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5500-5505},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178672},
        abstract = {In an increasingly competitive academic landscape, scholarship management systems play a crucial role in ensuring equitable access to educational opportunities. This paper presents an AI-driven scholarship finder that streamlines the scholarship recommendation process by analyzing student profiles and verifying key eligibility criteria such as income tax records, caste certificates, and academic performance. By leveraging machine learning algorithms, the system matches students with relevant scholarships, enhancing transparency and efficiency for applicants, institutions, and funding bodies. Through the integration of automated data verification and intelligent recommendation mechanisms, this work aims to improve the accessibility and effectiveness of scholarship distribution, contributing to a more inclusive education ecosystem.},
        keywords = {AI-Driven Scholarship Finder, Data Verification, Financial Aid, Machine Learning, Scholarship Recommendation, Student Profiles.},
        month = {May},
        }