Understanding the Adoption of Artificial Intelligence in Higher Education: a Topic Modeling Approach

As Higher Education Institutions (HEIs) undergo rapid digital transformation, Artificial Intelligence (AI) has emerged as a key enabler in reshaping both academic and administrative practices. However, the complexity of AI adoption in HEIs demands deeper insight into prevailing approaches and strategic trends. This study employs Latent Dirichlet Allocation (LDA) to perform topic modeling on a curated corpus of 453 peer-reviewed articles and conference papers, aiming to identify the dominant themes surrounding AI adoption in higher education. Thirteen distinct topics were uncovered, highlighting key dimensions such as adoption drivers, management issues, stakeholder perspectives and ethical concerns. The results provide a structured overview of major discourses, such as the existence of stakeholders with diverse requirements and perceptions, and reliance on existing adoption frameworks. These should inform institutional decision-making and future research directions.