DEVELOPMENT OF AN AI-POWERED STUDENT PERFORMANCE ANALYSIS SYSTEM USING PHP

Аннотация

The growing adoption of web-based educational platforms has generated large volumes of student-related data, creating new opportunities for analyzing academic performance. However, many PHP-based educational systems still rely on basic statistical summaries and manual evaluation methods, which limit their ability to provide timely and actionable insights. As a result, early identification of learning difficulties and performance risks remains a challenge in many educational environments. This article presents the development of an AI-powered student performance analysis system implemented within a PHP-based educational platform. The proposed system leverages machine learning techniques to analyze student assessment results, activity logs, and engagement indicators in order to identify performance patterns and predict potential academic risks. Artificial intelligence components are integrated into the PHP architecture through a modular design, enabling automated performance analysis without requiring significant changes to existing systems. The results demonstrate that AI-driven performance analysis enhances the accuracy and responsiveness of student evaluation processes. The system enables early detection of low-performing students, supports data-driven academic monitoring, and provides a foundation for informed instructional decision-making. This research contributes to educational technology by illustrating how artificial intelligence can be effectively combined with PHP-based systems to improve student performance analysis and academic support.