AI Architecture for Educational Transformation in Higher Education Institutions

Abstract

Background: The rapid integration of Artificial Intelligence (AI) into Higher Education Institutions (HEIs) is reshaping educational paradigms through AI architecture—structured systems that redefine education stakeholders, behavioural roles, and leverage predictive analytics.

Objective: This paper aims to explore the current state, challenges, and transformative potential of AI architecture in HEIs, with a focus on teaching methodologies, student-centric learning paradigms, and administrative efficiency supported by Learning Management Systems (LMS).

Methods: A mixed-methods approach was employed, analyzing data from diverse stakeholders across multiple universities and examining different approaches to online syllabus implementation, supplemented by a synthesis of global literature.

Results: Findings indicate significant benefits, including the evolution of educational paradigms with AI and supporting technologies. This evolution facilitates transformation towards student-centric learning and operational efficiency, accompanied by shifts in the roles of teachers, students, infrastructure, and syllabi.

Conclusion: The study proposes a four-phase transformation framework that highlights the development of AI-driven social learning ecosystems and new AI infrastructure, prioritizing these over traditional physical infrastructure. Sustainable implementation recommendations are also provided.