A Bibliometric Analysis of Digital Teacher Competency Research Trends in the Era of Artificial Intelligence-Based Learning: A Global Study

Abstract

The integration of artificial intelligence (AI) into education requires a reconceptualization of digital teacher competencies. This study presents a qualitative bibliometric analysis of global research trends in digital teacher competency in the era of AI-based learning. Using the Scopus database, 876 documents published between 2014 and 2024 were analyzed through descriptive bibliometrics, keyword co-occurrence mapping, and thematic cluster interpretation using VOSviewer. The qualitative method employed an interpretive approach to identify the emergent thematic structures, evolutionary pathways, and geographic distribution patterns. The findings reveal exponential growth in publications since 2020, driven by the rise of generative AI and adaptive learning systems. Four dominant thematic clusters emerged: (1) foundational digital competence frameworks and teacher professional development, (2) AI-driven adaptive and personalized learning environments, (3) ethical dimensions and AI literacy, and (4) data-informed pedagogical decision-making. The United States, China, and the United Kingdom lead scientific production, while Indonesia appears as the most productive Southeast Asian country but with a limited citation impact. The temporal evolution demonstrates a clear shift from generic digital literacy to AI-specific competencies, such as prompt engineering, algorithmic evaluation, and critical AI pedagogy. The discussion highlights the urgent need to embed AI-related constructs into existing teacher competence frameworks and foster international research collaboration. This study provides a comprehensive research map for policymakers, teacher educators, and researchers aiming to design future-ready professional development models