SCHOOL MANAGEMENT SYSTEM 4: AI-DRIVEN MANAGEMENT INFORMATION SYSTEM WITH INTELLIGENT STUDENT PAYMENT TRACKING AND HISTORY ANALYSIS

Abstract

This study proposes an AI-powered platform designed to automate and streamline administrative and financial operations in schools, with a particular focus on student payment tracking. The system enhances efficiency, accuracy, and transparency by leveraging AI to monitor payments in real time, generate analyzable payment history reports, identify overdue accounts, and forecast potential financial risks. By addressing the limitations of traditional, manual data handling, the platform offers a modern solution to the challenges of school financial management. This study presents the development and implementation of an AI-powered Management Information System (MIS) for schools, following the Agile Scrum methodology. The development process began with gathering stakeholder requirements and analyzing existing systems to identify gaps and opportunities. The system was built using a scalable microservices architecture, leveraging technologies such as React.js, Node.js, Laravel, and MySQL. Deployment was streamlined through DevOps practices, while rigorous testing and continuous stakeholder feedback ensured the system aligned with the operational needs of educational institutions. The AI-driven Management Information System (MIS) successfully met its objectives by automating student payment tracking and generating real-time transaction histories. Feedback from stakeholders highlighted significant improvements in administrative efficiency, with users commending the system's intuitive dashboard and timely updates. Manual errors were notably reduced, and the system delivered actionable insights into payment patterns and overdue accounts. Performance testing confirmed stable and secure operations under standard load conditions. Key outcomes included faster reconciliation, improved financial transparency, and streamlined reporting, all of which directly support data-driven financial decision-making in educational institutions. This emphasizes how the AI-powered MIS effectively addressed critical reporting limitations by providing real-time tracking and a consolidated view across all departments, capabilities that were absent in previous systems. The integration of predictive analytics and automation enhanced payment management and improved overall financial transparency. Built on a scalable microservices architecture, the system supports future expansion, while DevOps practices ensured rapid and reliable deployment. Challenges such as AI model integration and data security were resolved through iterative development and close stakeholder collaboration. The study concludes that AI can significantly enhance school operations, and recommends future advancements—such as mobile app integration and more sophisticated predictive modeling—to further strengthen the synergy between educational institutions and artificial intelligence.