Mapping the Emergence: A Bibliometric Analysis of the Convergence Gap in GeoAI Teacher Education Research

Abstract

The integration of Geospatial Artificial Intelligence (GeoAI) into geography education represents a critical frontier for twenty-first-century pedagogy, yet teacher preparation in this domain remains severely underexplored. This study employs bibliometric analysis to examine the convergence gap between established research fields and the emerging area of GeoAI teacher education. Through systematic analysis of 1471 publications from the Dimensions database spanning January 2010 through March 2025, we investigated five interconnected datasets using advanced network analysis techniques. Drawing on convergence science theory and adapting interdisciplinary measurement approaches, our findings reveal an extreme convergence gap (Gap Index = 0.941, where values approaching 1.0 indicate greater disconnection between fields) between well-established foundational domains—geography teacher education (470 papers), AI teacher education (382 papers), and spatial thinking education (312 papers)—and their intersection. Only 25 papers directly address AI integration in geography education, representing 1.7% of the total corpus. The research exhibits pronounced temporal concentration with a Field Emergence Score of 70.6 (indicating that over 70% of publications appeared within the most recent three years), significant geographic bias toward the Global North (Herfindahl Index = 0.208), and limited theoretical framework development. Co-authorship and keyword co-occurrence networks demonstrate fragmented research communities with structural holes representing opportunities for knowledge brokerage across domains. We propose a prioritized research agenda addressing theoretical development, empirical validation, and geographic equity, documenting the real-time emergence of a critical educational research domain while identifying strategic intervention points for accelerating field convergence.