Applying Biometric Technologies for Personalized Learning in Education Management Systems

In recent years, biometric technologies have gained significant attention in the field of education, offering new possibilities for personalized learning experiences. This study explores the application of biometric technologies in education management systems to enhance adaptive learning and improve student engagement. The research proposes a model that utilizes biometric data, including facial recognition, eye-tracking, and physiological signals (heart rate, EEG), to assess students' cognitive states in real-time. By analyzing these biometric indicators, the system dynamically adjusts learning materials and teaching strategies to match individual learning needs. The proposed model is implemented using artificial intelligence algorithms to process biometric inputs and personalize the learning environment accordingly. A controlled experiment was conducted with university students to evaluate the system's effectiveness. The results indicate that biometric-driven personalized learning improves knowledge retention and student engagement compared to traditional learning approaches. Additionally, the system demonstrates the potential to identify learners' stress levels and attention spans, enabling more effective educational interventions. This study highlights the potential of biometric-based adaptive learning systems in modern education and suggests future directions for integrating biometric technology into large-scale learning platforms. The findings provide valuable insights for researchers and education policymakers aiming to enhance the efficiency of learning management systems through biometric data integration.