The evolution of pedagogical design in the context of the development of adaptive learning systems based on machine learning

Abstract

This study examines transformational processes in pedagogical design influenced by the development of adaptive learning systems based on machine learning algorithms. In the last decade, there has been a significant shift from static, one-dimensional educational models to dynamic, personalized systems that can adapt to the individual characteristics of students. The study analyzes the architectural components of modern adaptive systems, including models for building student profiles, content adaptation mechanisms, and methods for optimizing educational trajectories. Based on a comprehensive analysis of empirical data collected in the period 2020-2023, key trends in the integration of artificial intelligence and pedagogical design have been identified, as well as principles for designing effective adaptive educational environments. The results of the study demonstrate that machine learning systems contribute to an increase in academic performance by an average of 14,7%, increased student engagement by 23,5%, and reduced learning time by 31,2% compared to traditional educational formats. Significant correlations have been identified between the architectural features of adaptive systems and the effectiveness of educational outcomes, which makes it possible to formulate a comprehensive methodology for designing a new-generation pedagogical environment focused on individualizing learning while maintaining scalability.