A MODEL FOR FORMING ADAPTIVE LEARNING TRAJECTORIES IN DISTANCE EDUCATION BASED ON ARTIFICIAL INTELLIGENCE (INTERNATIONAL CONFERENCE ON INTERDISCIPLINARY SCIENCE, 2026)
In the context of digital transformation, the widespread integration of distance learning technologies in higher education has led to the emergence of new approaches to organizing the pedagogical process. Although distance learning platforms have significantly expanded access to educational resources and learning opportunities, many existing systems still lack mechanisms for effectively organizing instruction that accounts for the individual characteristics of students. Since learners differ in terms of prior knowledge, learning pace, interests, and academic performance, applying the same learning materials and instructional strategies to all students often reduces the overall effectiveness of the educational process.This study proposes a pedagogical model for forming adaptive learning trajectories in distance education systems based on artificial intelligence technologies. The proposed model enables the generation of individualized learning pathways by analyzing students’ learning activities, performance indicators, and knowledge levels. Through the use of intelligent data analysis and learning analytics, the model dynamically adapts educational content and instructional strategies to the needs of each learner. The results of the study demonstrate that the implementation of adaptive learning technologies in distance education environments can significantly improve learning efficiency, enhance student engagement, and support personalized learning processes in higher education.