The originality of this paper lies in addressing a gap in research concerning the growing use of AI in higher education (HE) and the lack of sector-specific policies. AI policy in HE is an emerging area with limited scholarship, yet existing studies warrant systematic synthesis. Using a PRISMA-informed systematic review approach, the study screened peer-reviewed English-language journal articles published between 2015 and early 2025; 11 studies met the inclusion criteria, indicating a limited evidence base on AI policy in higher education. The small final corpus indicates that peer-reviewed research on AI policy in higher education remains emergent. This paper advances theoretical understanding of the pace of policy learning, the actors involved, and persisting gaps. It also consolidates available policy recommendations, notes their limitations, and underscores the need for collective action to accelerate effective AI policy responses across HE. In doing so, it informs policy-making and supports efforts to align institutional responses to AI with SDG4 on inclusive and equitable quality education.
Kushnir et al. (Tue,) studied this question.