Adaptive learning using artificial intelligence: theoretical principles of implementation in vocational college

Abstract

The article presents a theoretical and methodological analysis of adaptive learning as an innovative educational approach that enables the personalization of the learning process in accordance with the individual characteristics of vocational pre-tertiary education students. The concept of “adaptive learning” is clarified in the context of digital transformation of education, and its key features are outlined: individualization, flexibility, automation, and contextual sensitivity.
The study analyzes current models of implementing adaptive approaches using digital educational platforms such as Moodle, TalentLMS, and Docebo, which incorporate elements of predictive analytics, gamification, and automated feedback. The international and national experience of introducing adaptive learning in professional training is generalized, with a particular focus on resource-limited and remote learning conditions.
Special attention is paid to the role of artificial intelligence technologies as a foundation for the adaptivity of digital learning environments. Examples are provided of how AI is used for learning outcomes monitoring, constructing personalized learning paths, adjusting educational content to learners’ proficiency levels, and delivering automated feedback. The main risks of implementing intelligent educational technologies are identified, including algorithmic opacity, potential bias, violations of ethical standards, and threats to personal data security. The article highlights the challenges faced by vocational colleges during wartime, particularly the need to adapt educational strategies to dynamically changing conditions.
The feasibility of integrating adaptive learning enhanced by artificial intelligence in vocational education institutions is substantiated, emphasizing its potential to improve learning efficiency, expand access to quality resources, and personalize instructional content. The study outlines prospects for further research, including the optimization of personalization algorithms, development of user interfaces for adaptive platforms, and expansion of inclusive capabilities in digital education.