The present article is dedicated to the analysis of the strategic dimensions inherent in the integration of artificial intelligence (AI) technologies within the educational milieu of technical universities. The focal point of this analysis encompasses models of interaction between the controlling and controlled subsystems, the development of pedagogical knowledge bases, and the utilisation of intelligent platforms for the optimisation of the learning environment. The text examines methods for transforming educational processes through adaptive algorithms, hybrid models, and AI tools that foster personalised learning and increase the efficiency of professional training. The study proposes a model of interaction between educational subsystems that employs artificial intelligence (AI). This model incorporates structured, static, dynamic and operational knowledge to facilitate decision-making processes. The integration of multiple AI platforms has been demonstrated to provide a number of advantages, including automation, adaptive learning and material visualisation. The findings illustrate a cyclical approach to process enhancement, emphasising the utilisation of hybrid models that integrate expert knowledge with algorithms. This approach has been demonstrated to facilitate the development of systemic thinking and the practical competencies of students specialising in technical disciplines. Subsequent research concentrates on the development of algorithms for a more profound analysis of the causal mechanisms of AI effectiveness in education. This analysis includes the empirical testing of hybrid models in real university environments. A promising avenue for future research lies in the study of the impact of intelligent systems on the psychological aspects of learning, such as motivation and cognitive load. Furthermore, there is potential for integration with new technologies, including machine learning, for the purpose of predicting academic trajectories.