AI-BASED PEDAGOGICAL MODELLING IN DIGITAL EDUCATION: CONCEPTUAL FOUNDATIONS, DESIGN, AND IMPLICATIONS

Abstract

The rapid digital transformation of education has intensified the need for innovative pedagogical frameworks capable of addressing diverse learner needs and dynamic learning environments. This article examines the conceptual foundations and practical implementation of AI-based pedagogical modelling in digital education. It explores the design of adaptive learning systems, AI-driven instructional design, and the role of learning analytics in shaping data-informed educational practices. Particular attention is given to inclusive education, where artificial intelligence enables personalized and accessible learning experiences for students with diverse educational needs. The study also analyzes AI-based assessment models and highlights key ethical and methodological challenges, including data privacy, algorithmic bias, and the evolving role of educators. The findings suggest that AI-based pedagogical modelling has significant potential to enhance learning outcomes, provided it is implemented within a human-centered and ethically grounded framework.