ARCHIVES
VOL. 10, ISSUE 3 (2025)
AI-based automated scholarship and financial aid allocation systems
Authors
Mahendra Vishwanath Thakare, Sanjivani Rajendra Phatangare
Abstract
AI-based automated scholarship and financial
aid allocation systems leverage machine learning and data-driven algorithms to
enhance the efficiency, fairness, and transparency of awarding financial assistance
to students. These systems utilize predictive analytics to assess applicants
based on academic performance, financial need, extracurricular activities, and
demographic factors, ensuring an unbiased and merit-based selection process. By
integrating natural language processing and deep learning models, AI can
analyze vast amounts of data from applications, recommendation letters, and
institutional records to make informed decisions while minimizing human bias.
Furthermore, blockchain technology can enhance security and transparency by
maintaining immutable records of financial aid distribution. Such automated
systems not only reduce administrative burdens but also improve accessibility
by identifying deserving candidates who may otherwise be overlooked due to
manual processing limitations. Despite the benefits, challenges such as data
privacy concerns, algorithmic fairness, and ethical considerations must be
addressed to prevent biases and ensure equitable distribution. By continuously
refining AI models with ethical AI frameworks and diverse datasets,
institutions can develop more inclusive and accurate financial aid allocation
systems, ultimately fostering equal educational opportunities for all.
Download
Pages:100-103
How to cite this article:
Mahendra Vishwanath Thakare, Sanjivani Rajendra Phatangare "AI-based automated scholarship and financial aid allocation systems". International Journal of Advanced Scientific Research, Vol 10, Issue 3, 2025, Pages 100-103
Download Author Certificate
Please enter the email address corresponding to this article submission to download your certificate.