ACADEMIC PERFORMANCE ANALYZER USING AI

Abstract

The increasing demand for personalized education and timely academic intervention highlights the need for intelligent systems to monitor and predict student performance. Traditional evaluation methods are often periodic, reactive, and unable to provide continuous tracking, resulting in delayed identification of at-risk students. This review paper focuses on an AI-based student performance prediction system integrated with RFID hardware to improve accuracy, enhance efficiency, and enable real-time monitoring in educational environments. The system uses RFID technology to record student attendance and engagement data automatically, while machine learning algorithms analyze this data along with academic records to predict performance. The integration of Artificial Intelligence and IoT enables continuous data collection, real-time analysis, and remote monitoring by educators. The paper reviews recent advancements, design approaches, and implementation strategies, highlighting cost-effective and scalable solutions suitable for modern educational institutions and smart campus applications. By analyzing various methodologies, the study emphasizes the potential of AI-driven systems to improve academic outcomes, enable early intervention, and reduce manual effort. Overall, the review highlights the importance of adopting intelligent, AI-based technologies combined with RFID hardware to achieve efficient, data-driven, and proactive student performance management.