University Lecturers’ Digital Competence and Training Quality in the Context of Generative Artificial Intelligence: An Integrative Review and Proposed Impact Framework

Abstract

Generative artificial intelligence (GenAI) is changing how university lecturers design learning environments, provide feedback, and assess students, while raising new risks related to bias, data security, and academic integrity. This study addresses four research questions concerning the components of lecturers’ digital competence, the mechanisms through which this competence influences training quality, the contextual conditions that moderate this influence, and a conceptual framework that integrates these relationships. Based on a directed integrative literature review, the study identifies five competence domains: digital pedagogy; technology and data literacy; GenAI competence; digital ethics and safety; and professional learning. These competences influence training quality through six pedagogical mechanisms: learning design, interactive engagement, personalization, feedback, authentic assessment, and scaffolding for self-regulated learning. The study proposes a four-layer impact framework and recommends that higher education institutions organize professional development around authentic teaching tasks, align institutional AI governance with pedagogical goals, and evaluate training quality using evidence of student learning, cognitive engagement, equity, and assessment validity.