BIBLIOMETRIC ANALYSIS OF GENERATIVE ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN EDUCATION

Abstract

The rapid spread of generative artificial intelligence in education intensifies the requirements for its responsible use, the maintenance of academic integrity, and the modernization of learning design. The aim of the study is to identify the dynamics of GenAI-related publications in the Web of Science database from 2019 to 2024, as well as influential authors, organizations, countries, and priority thematic clusters. The significance of the study lies in systematizing this development and providing an evidence-based foundation for teacher training and curriculum renewal in Kazakhstan. Bibliometric analysis was used as the method. The data were selected using the TS search expression. Keyword co-occurrence, co-citation, and co-authorship networks were visualized in VOSviewer 1.6.20 using the fractional counting method with a frequency threshold of ≥5. The analysis identified three thematic clusters: perception and trust; methodological and regulatory frameworks and learning design; and application and effectiveness. In addition, since 2023 the research agenda has shifted toward large language models (LLMs), ChatGPT, and issues of academic integrity. In the cooperation network, the United States, China, and the United Kingdom acted as key hubs, while Singapore, Canada, and Germany served as connecting bridges; at the regional level, Nazarbayev University stood out as a point of interaction with global consortia. The final conclusion is that GenAI research is evolving from a technocentric interest toward institutionalized pedagogical solutions. The value of the study lies in formulating concrete recommendations for integrating AI literacy and ethics into curricula and expanding international partnerships.