Generative AI for Learning: A Bibliometric Mapping of Global Scientific Publications
DOI:
https://doi.org/10.58812/wsis.v4i07.3018Keywords:
Generative Artificial Intelligence, Generative AI, ChatGPT, Artificial Intelligence in Education, Learning Technology, Bibliometric AnalysisAbstract
The rapid advancement of Generative Artificial Intelligence (GenAI) has significantly transformed educational practices by introducing new possibilities for personalized learning, intelligent tutoring systems, automated feedback, and AI-supported knowledge creation. This study aims to explore the global research landscape of Generative AI for Learning through a bibliometric analysis of scientific publications indexed in the Scopus database. A comprehensive literature search was conducted to identify relevant publications, followed by performance analysis and science mapping using VOSviewer. The analysis examined publication trends, highly cited literature, keyword co-occurrence, citation networks, author collaboration, institutional contributions, and international research patterns. The findings reveal that research on generative AI in learning has experienced substantial growth, particularly following the emergence of ChatGPT and large language models. The intellectual structure of the field is dominated by three interconnected themes: technological advancement of artificial intelligence, educational integration of AI-based learning systems, and human-centered considerations including AI literacy, critical thinking, ethics, and responsible adoption. Influential publications highlight both the opportunities and challenges of generative AI, including improvements in learning effectiveness, academic transformation, assessment challenges, and potential cognitive impacts. Furthermore, collaboration analysis indicates that the United States plays a central role in global research networks, while contributions from countries across Asia, Europe, and other regions continue to expand. This study provides a comprehensive understanding of the evolution, current trends, and future directions of Generative AI for Learning research, emphasizing the importance of interdisciplinary collaboration and responsible AI implementation to support sustainable educational innovation.
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