SCIENTIFIC AND METHODOLOGICAL FOUNDATIONS OF ARTIFICIAL INTELLIGENCE-BASED PEDAGOGICAL DECISION-MAKING SYSTEMS

Abstract

This scientific-methodological work examines the theoretical foundations of AI-based pedagogical decision-making systems and analyzes contemporary approaches to data-driven instructional management. The study substantiates the effectiveness of intelligent algorithms designed to process large-scale educational data, identify personalized learning trajectories, automate assessment procedures, and optimize pedagogical interventions. Predictive capabilities of artificial intelligence are highlighted as essential tools for improving educational quality, forecasting learner needs, and supporting educators through intelligent digital assistant systems. The research also addresses ethical considerations, transparency requirements, data security, and governance mechanisms that influence the reliability of pedagogical decisions. Furthermore, the paper proposes scientifically grounded recommendations for integrating artificial intelligence into educational environments to enhance decision-making accuracy and instructional effectiveness.