ACADEMIC PERFORMANCE ANALYZER USING AI

Abstract

The growing need for personalized education and timely academic support has increased the importance of intelligent systems that can monitor and predict student performance. Conventional evaluation methods are typically periodic and reactive, which often leads to delays in identifying students who require additional support. This review paper presents an AI-based student performance prediction system integrated with RFID hardware to enhance accuracy, improve efficiency, and support real-time monitoring in educational environments. The proposed system utilizes RFID technology to automatically capture student attendance and engagement data, which is then combined with academic records for analysis using machine learning algorithms. The integration of Artificial Intelligence with IoT facilitates continuous data collection, real-time analysis, and remote access for educators. This paper examines recent developments, system architectures, and implementation strategies, with a focus on cost-effective and scalable solutions suitable for modern institutions and smart campus environments. Through the evaluation of different methodologies, the study demonstrates how AI-driven systems can support better academic outcomes, enable early intervention, and minimize manual effort. In conclusion, the review emphasizes the role of intelligent AI-based systems combined with RFID technology in creating efficient, data-driven, and proactive approaches to student performance management.