Models for the Integration of Artificial Intelligence Technologies into Adaptive Chemistry Learning Systems

Abstract

The article examines approaches to the implementation of intelligent algorithms in digital educational environments oriented toward the personalization of the educational process. The aim of the article is to carry out a comprehensive theoretical and methodological analysis and systematization of contemporary models for the integration of artificial intelligence technologies into adaptive learning systems in chemistry, and also to assess their influence on the personalization of educational trajectories, on the cognitive effectiveness of learning, and on the optimization of the didactic process. The study employs the methods of systems analysis and the comparative analysis of scholarly sources, the content analysis of contemporary educational platforms, the method of the pedagogical modeling of adaptive learning environments, and the generalization of empirical experience in the introduction of intelligent learning systems into the teaching of chemistry. Elements of conceptual design and of structural-functional analysis are additionally employed. As a result of the study, the principal models for the integration of artificial intelligence into adaptive learning systems are identified, in particular machine-learning-based models for the personalization of educational trajectories, intelligent recommendation systems, the automated assessment of knowledge, and adaptive feedback. It is established that the use of such models contributes to enhancing the motivation of learners, to improving the quality of the assimilation of complex chemical concepts, and to the optimization of the educational process. The conclusions note that the integration of artificial intelligence technologies into adaptive systems of chemistry instruction constitutes a promising direction in the development of education. It ensures the individualization of instruction, enhances the effectiveness of the educational process, and provides new opportunities for the development of digital educational environments. It is expedient to direct further research toward refining the algorithms of adaptation and toward assessing their effectiveness under real instructional conditions.