From Innovation Signaling to Governance Readiness: Generative Artificial Intelligence Integration, Policies, Opportunities, and Challenges in Two Rwandan Universities

Background: Generative artificial intelligence is entering higher education faster than many universities have been able to govern it, particularly in African contexts where policy ambition, institutional capacity, digital infrastructure, and pedagogical practice do not always advance at the same pace. Purpose: This study examines how generative artificial intelligence integration is publicly documented, governed, and framed at two universities in Rwanda: the African Leadership University and the Adventist University of Central Africa. Design: Guided by an integrated framework combining institutional readiness, Diffusion of Innovations, and a rights-based governance lens, the study adopts an interpretivist comparative multiple-case design based on document analysis and secondary analysis. The corpus comprises publicly retrievable institutional webpages, policy documents, academic regulations, handbooks, e-learning materials, research manuals, national policy texts, and recent peer-reviewed scholarship published or available between 2021 and April 2026. Findings: Public evidence indicates visible AI engagement at both universities, but in materially different forms. ALU appears more innovation-signalling, foregrounding AI research, student bootcamps, and academic-support programming. AUCA appears more governance-dense, with stronger public visibility of academic regulations, academic-integrity language, ICT and online-learning policies, plagiarism infrastructure, and AI-and-big-data institutional positioning. However, neither institution publicly presents a fully specified generative AI acceptable-use regime aligned with Rwanda’s evolving national and sectoral AI governance expectations. The findings therefore suggest that visible experimentation is advancing faster than visible rule specificity. Originality/value: The study contributes rare comparative African evidence on university AI governance and introduces a useful analytical distinction between innovation signalling and governance readiness. Practical implications: The central challenge is no longer whether universities will adopt AI, but whether they can align policy clarity, academic-integrity architecture, digital capacity, and educational purpose in institutionally credible ways. The study also identifies concrete priorities for later primary research on implementation, stakeholder interpretation, and assessment design.