First-mover advantage in AI adoption in higher education: a critical discourse analysis of social policy, inequality, and institutional governance

Purpose This study investigates the strategic importance of first-mover advantage (FMA) in adopting Artificial Intelligence (AI) within higher education institutions (HEIs), exploring its effects on governance, institutional inequality, and access to technological resources. Design/methodology/approach A Critical Discourse Analysis (CDA) approach, guided by Fairclough’s three-dimensional model and the PRISMA 2020 guidelines, is used to analyse 54 academic sources published between 2000 and 2024. The study examines how early AI adoption is framed across academic, policy, and institutional discourses, and how these narratives shape perceptions of innovation, competitiveness, and leadership. Findings The study shows that early adoption of AI offers higher education institutions benefits in innovation, operational efficiency, and reputation. AI tools, such as personalised learning systems and chatbots, improve teaching, student engagement, and learning outcomes. However, successful implementation relies on institutional readiness, such as available resources, faculty expertise, and technological infrastructure, which highlights disparities among different institutions. Practical implications The study offers policy and pedagogical insights to optimise AI adoption, emphasising resource allocation, faculty capacity building, and ethical considerations. It underscores the need for continuous training to maximise the benefits of AI in education. Originality/value This research broadens FMA’s scope into higher education by employing CDA to explore how AI adoption stories are formed and justified. It introduces a conceptual framework that combines institutional readiness, leadership, pedagogy, and ethics to direct future research and practical applications.