Geospatial Education at the GIS–GeoAI Interface: A PRISMA-Guided Bibliometric Mapping of a Scopus Corpus, 2002–2025
DOI:
https://doi.org/10.24114/jg.v18i2.75596Keywords:
Geospatial Information, Geography Education, Geographic Information Systems, Geospatial Artificial IntelligenceAbstract
Geospatial information is used in education as disciplinary content and as a learning technology, while computational geospatial research increasingly employs artificial intelligence, machine learning, deep learning, and remote sensing. This study maps the publication performance and thematic structure of a broad Scopus corpus at the interface of geospatial technology and education from 2002 to 2025. A PRISMA-guided review and bibliometric analysis were conducted on 125 documents from 68 sources using bibliometrix and Biblioshiny in R. The corpus showed an annual publication growth rate of 10.99%, an average of 11.93 citations per document, and 2.81 authors per document. The Journal of Geography was the most productive source, while De Lázaro-Torres and De Miguel González were the most productive authors. Co-word analysis identified an educational GIS cluster, a geospatial technology and learning cluster, and a computational geospatial cluster containing GeoAI-related terms. The increased visibility of the computational cluster in 2022–2025 indicates an emerging thematic orientation, not evidence that GeoAI has replaced GIS-based education or already dominates educational practice. Because the search expression included the broad term learning and the corpus retained several technical geospatial records, the findings should be interpreted as an exploratory map of the education–technology interface. The proposed instructional sequence is therefore a theory-informed research proposition that requires empirical validation
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