Empowering Rural Minds: Leveraging Predictive Analytics to Bridge Urban-Rural Educational Disparities Through Financial Assistance

Access to quality education is a critical factor in individual empowerment, economic development, and societal progress. However, students from rural backgrounds often face significant obstacles when it comes to pursuing technical education, primarily due to financial barriers. Financial barriers act as a deterrent, restricting the participation of rural students in technical education programmes. This paper presents an innovative approach to address this issue by leveraging the power of artificial intelligence (AI) and predictive analytics to provide equitable financial assistance and scholarships for technical education. By adopting a comprehensive strategy, this paper addresses the biases, transparency shortcomings, and contextual factors that have been prevalent in previous algorithm methods. The strategy employed in this study involves a multi-faceted approach, integrating various methodologies to achieve optimal outcomes. The key steps encompassed in the methodology include algorithmic fairness, model training and selection, transparency and interpretability, human-in-the-loop review, contextualised assessment, continuous learning, and feedback loops. Adversarial Debiasing is utilised to mitigate biases and ensure a fair decision-making process. To enhance transparency and interpretability, rule-based systems and LIME (Local Interpretable Model-Agnostic Explanations) are employed. Human-in-the-loop review is an integral part of the methodology, ensuring that AI-driven decisions are validated and refined by human experts. The expected outcome of this research is an AI-enabled financial assistance system that democratises technical education by making it more accessible to students from rural backgrounds. The implementation of this model can have a transformative impact on education equality, empowering rural minds to pursue their academic dreams.