SCHOOL MANAGEMENT SYSTEM: STUDENT INFORMATION SYSTEM WITH DATA VISUALIZATION AND PREDICTIVE ANALYTICS USING OPENAI.

Abstract

Traditional Student Information Systems (SIS) often fall short in delivering meaningful insights and predictive analytics, as they primarily focus on basic data storage and retrieval. This capstone project addresses these limitations by developing a comprehensive School Management System (SMS) that integrates an SIS with advanced data visualization and predictive analytics powered by OpenAI. The goal is to modernize and streamline school processes, improve administrative efficiency, and support student success through data-driven decision-making. The system was developed using the Agile Scrum methodology, dividing the project into smaller sprints with clearly defined roles and responsibilities. Scrum artifacts such as the product backlog and sprint backlog were used to manage features and tasks efficiently. The system architecture follows a modular design, incorporating components for user management, student profiles, academic records, and reporting. The technology stack includes HTML, CSS/Bootstrap, PHP/Laravel, MySQL, and JavaScript, with OpenAI integrated for predictive analytics capabilities. Project requirements were gathered through surveys and stakeholder interviews. A comprehensive testing strategy was implemented—comprising integration, end-to-end, usability, and security testing—to ensure seamless module interaction and system reliability. The implemented School Management System (SMS) offers a centralized database for student information management, real-time data visualization for enhanced performance tracking, and AI-powered predictive analytics for early identification of at-risk students. It provides comprehensive reporting tools for administrators and educators, streamlining both academic and administrative tasks. User feedback highlighted improved navigation and a better overall experience, particularly with the redesigned dashboard and reporting modules. The successful development and implementation of the School Management System (SMS) highlights the significant potential of integrating AI and data visualization into educational management systems. The project achieved its core objectives: optimizing student information management, automating academic performance tracking, and enabling data-driven decision-making. Key lessons learned include the value of user-centered design, effective communication, and comprehensive testing. Future enhancements may focus on expanding AI capabilities, reinforcing security protocols, and improving system scalability.