This article is devoted to a comprehensive analysis of the methodological foundations of the use of artificial intelligence (AI) in higher education to build adaptive and deeply personalized educational trajectories. The paper highlights the strategic importance of moving from unified approaches to models that are able to flexibly respond to the individual needs, pace, and learning style of each student. Special attention is paid to the potential of Machine Learning and Big Data analysis technologies in knowledge diagnostics, predictive analytics and effective automation of routine tasks of the teaching staff. The role of AI in the successful integration of multimodal pedagogy is considered separately. A pilot study conducted at TIIAME National Research University demonstrated that AI-based adaptive learning significantly improves students’ academic performance, engagement, and satisfaction compared to traditional LMS-based instruction. These results confirm that even partial personalization through Intelligent Tutoring Systems can enhance learning efficiency and support the development of key 21st-century competencies. The key research results are presented, as well as the ethical and practical challenges associated with responsible AI implementation. The conclusions drawn can serve as a basis for the development of new educational programs and strategic planning for the digital transformation of universities.