Digital Transformation in Higher Education in the AI Era: A Systematic Review of Definitions and Emerging Trends

Digital transformation (DT) remains inconsistently defined in higher education, particularly as artificial intelligence (AI) introduces new institutional capabilities and responsibilities. This systematic review synthesizes 40 retrievable peer‐reviewed studies published between 2020 and 2025 to examine how DT is defined, which institutional attributes recur, and how AI is positioned within transformation. Eight attributes were identified. Strategy, leadership, and governance were the most consistently represented, followed by teaching, learning, and student experience, and technology, infrastructure, and platforms. Process redesign and data, analytics, and evidence appeared in a majority of studies, although less consistently than strategy, teaching and learning, and technology. AI was positioned through four overlapping roles: an advanced technology layer, an institution‐wide strategic capability, a teaching‐learning engine, and a governance and risk amplifier. The 2025 literature was not more AI‐saturated overall, but studies addressing AI placed substantially greater emphasis on generative AI, human readiness, and governance‐related concerns. The findings characterize DT as multidimensional socio‐technical change rather than technology adoption alone. The review proposes AI‐coupled institutional innovation, operating through dependency and consequence coupling, to explain how AI both relies on institutional conditions and generates implications that extend across pedagogical, operational, organizational, and governance domains.