AI Dependency in Education: A Bibliometric Analysis and Psychological Research Directions - Jonatan Hasudungan Sihombing; Idat Muqodas; Mubiar Agustin (FKIP - Universitas Riau, 2026)
The proliferation of artificial intelligence (AI) tools in higher education has raised growing concerns about students' excessive reliance on AI in completing academic tasks, a phenomenon referred to as AI dependency. No comprehensive bibliometric synthesis on this topic currently exists. This study aimed to map the research landscape of AI dependency in education by identifying publication trends, intellectual structure, and emerging thematic directions. A bibliometric analysis was conducted on 88 articles published between 2023 and 2025, indexed in the Scopus database. Descriptive analysis, keyword co-occurrence networks, co-authorship networks, and thematic mapping were performed using Bibliometrix and VOSviewer. Findings reveal an exponential annual growth rate of 724.62%, with China, France, and Southeast Asian countries dominating research output. Four thematic clusters were identified: Generative AI and Educational Ethics, AI Dependency and Cognitive Impact, Human Factors and Psychology, and Educational Research Methods. Thematic mapping indicates that AI dependency occupies an emerging theme position with near-zero centrality, suggesting the construct remains in early conceptual consolidation. The co-occurrence of self-efficacy and psychological variables points to possible directions for developing standardised theoretical frameworks and evidence-based interventions to support responsible AI use in education.