Generative AI in higher education: A global perspective of institutional adoption policies and guidelines

Abstract

Integrating generative AI (GAI) into higher education is crucial for preparing a future generation of GAI-literate students. However, a comprehensive understanding of global institutional adoption policies remains absent, with most prior studies focusing on the Global North and lacking a theoretical lens. This study utilizes the Diffusion of Innovations Theory to examine GAI adoption strategies in higher education across 40 universities from six global regions. It explores the characteristics of GAI innovation, including compatibility, trialability, and observability, and analyses the communication channels and roles and responsibilities outlined in university policies and guidelines. The findings reveal that universities are proactively addressing GAI integration by emphasising academic integrity, enhancing teaching and learning practices, and promoting equity. Key policy measures include the development of guidelines for ethical GAI use, the design of authentic assessments to mitigate misuse, and the provision of training programs for faculty and students to foster GAI literacy. Despite these efforts, gaps remain in comprehensive policy frameworks, particularly in addressing data privacy concerns and ensuring equitable access to GAI tools. The study underscores the importance of clear communication channels, stakeholder collaboration, and ongoing evaluation to support effective GAI adoption. These insights provide actionable insights for policymakers to craft inclusive, transparent, and adaptive strategies for integrating GAI into higher education.