Deep machine learning for innovative educational management under crisis conditions

Abstract

Modern education faces uncertainty driven by crises, pandemics, and rapid technological shifts. These challenges demand a transition from traditional reactive management to adaptive, data-driven strategies. Deep machine learning (DML), utilizing multi-layer neural networks, provides tools for predictive analytics, risk assessment, and intelligent decision-making. In educational management, DML supports crisis forecasting, adaptive planning, personalized learning, resource optimization, and quality control. Its key strength lies in adaptability and continuous learning, ensuring accurate predictions and quick responses to instability. Integrating DML enables resilience and innovation, shifting education toward proactive, intelligent management. Future developments include hybrid models combining deep learning with symbolic AI for enhanced interpretability and strategic effectiveness during complex crises.