The transformation of higher education requires not only new teaching methods and curricular adaptations, but also structural and organizational changes at the institutional level. As education systems move toward more flexible and adaptive models, universities must respond to the diverse needs of students and the labor market. This transition necessitates the emergence of new departments and services, which can be supported and optimized by Artificial Intelligence (AI). AI-driven educational systems offer personalized learning pathways, automated feedback mechanisms, and real-time curriculum adjustments, fostering a more responsive and dynamic learning environment.

This study explores how AI-based Learning Management Systems (LMS) and learning analytics contribute to institutional change by enabling continuous student performance monitoring, real-time feedback loops, and adaptive curriculum updates. The research presents a case study of an institutional experiment utilizing AI-driven adaptive learning strategies to align academic training with industry expectations. The methodology was based on action research and iterative curriculum development, allowing for the continuous refinement of teaching materials and pedagogical approaches.

The results demonstrated that AI-assisted learning increased student engagement, motivation, and long-term institutional commitment. Students benefited from individualized learning experiences, leading to improved academic performance and reduced dropout rates. Furthermore, the integration of AI-supported analytics enabled universities to identify learning difficulties early, facilitating timely interventions. However, the study also highlights challenges such as faculty resistance, digital infrastructure limitations, and ethical concerns regarding data privacy.

Beyond its impact on students, AI also plays a transformative role in university administration and workforce development. The research identifies emerging institutional roles such as AI-based education management units, digital curriculum development teams, and AI-driven student advising systems. These units ensure that adaptive learning strategies are effectively integrated into institutional policies, helping universities maintain their long-term competitiveness and relevance.

While AI offers significant potential in higher education, its successful implementation depends on balancing automation with human interaction. Universities must ensure that AI technologies enhance rather than replace mentorship, critical thinking, and collaborative learning. The study concludes with policy recommendations for university leaders, curriculum designers, and educators on integrating AI into higher education governance. Strategies for faculty training, ethical AI adoption, and personalized learning environments are outlined to ensure a sustainable, technology-enhanced educational future.

Keywords: Artificial Intelligence, Adaptive Learning, Higher Education, Learning Analytics, Institutional Transformation, Digital Pedagogy, Personalized Education, AI in Teaching.