Artificial Intelligence Competence among Higher Education Faculty: A Critical Review through the Lens of the DigCompEdu Framework

Abstract

The rapid diffusion of artificial intelligence (AI) tools across teaching, assessment and research has placed renewed pressure on universities to define what it means for academic staff to be digitally and technologically competent. The European Framework for the Digital Competence of Educators (DigCompEdu), originally conceived to describe generic digital competence, has increasingly been invoked, adapted and contested as a reference point for understanding faculty readiness to use, teach with, and critically evaluate AI. This article offers a critical narrative review of the empirical and conceptual literature examining AI competence among higher education faculty, situated explicitly within the DigCompEdu architecture and its emerging extensions. It traces the historical development of DigCompEdu and related frameworks such as Technological Pedagogical Content Knowledge (TPACK) and its AI-oriented derivatives, synthesises empirical evidence on faculty self-reported competence, usage patterns, attitudes and professional development needs, and identifies the institutional, disciplinary and ethical factors that shape competence development. The review further examines professional development strategies reported in the literature and concludes with a critical assessment of the conceptual and methodological gaps separating policy aspirations from classroom realities, including unresolved questions of equity, algorithmic literacy and academic integrity. The synthesis suggests that, while DigCompEdu remains a durable and adaptable scaffold, its AI-related extensions are conceptually fragmented, empirically uneven across world regions, and insufficiently attentive to questions of educator agency and structural inequality. The article closes by outlining implications for policy, institutional practice and future research, alongside an honest account of the review's limitations.