A Study on the Design and Development of AI Driven Student Performance and Recommendation Dashboard

Abstract

This research proposes an intelligent system for analysing student academic performance and providing personalized recommendations using Artificial Intelligence and Data Mining techniques. Educational institutions generate large volumes of student data including attendance records, examination scores, and assignment results. Analysing this data manually is difficult and time-consuming. The proposed system uses machine learning algorithms to evaluate student performance and identify patterns in academic behaviour. The system processes academic records and predicts student performance using data mining techniques. A dashboard interface is designed to visualize performance trends, subject-wise scores, and attendance patterns. Based on the analysis results, the system generates personalized recommendations such as study strategies and subject improvement suggestions. The system helps teachers monitor student progress while enabling students to understand their academic strengths and weaknesses. The proposed solution demonstrates how Artificial Intelligence can enhance educational analytics and support data-driven decision making in modern education systems. The study highlights the importance of developing intelligent dashboards that assist teachers, administrators, and students in making data-driven decisions. The dashboard visualizes performance trends, attendance analysis, subject-wise performance, and engagement metrics in real time.  The study concludes that implementing AI-driven student performance dashboards in higher education institutions can significantly improve learning outcomes, optimize resource utilization, and support personalized education strategies.