Generative Artificial Intelligence (GenAI) has gained notable attention in educational literature, with supporters and critics expressing varying opinions. Despite its popularity, only a few reviews are available on the subject, with limitations such as small sample sizes and limited scope. This study aims to clarify the major themes influencing the discussion on GenAI in educational contexts. It employs a strong Business Intelligence paradigm and uses bibliometric analysis and topic modeling focusing on the R program's structural topic model (STM) Package, VOSviewer, and bibliometric software. The results highlight the esteem of GenAI in education and evidence of international collaboration in the research process dedicated to enhancing the rapidly evolving field of GenAI. The scientometric indexes indicate that the diversity of journals has the significant impact on GenAI in education. While Lotka's Law suggests that the field is still in its early stages, the collaborative network demonstrates strong connections among researchers, a positive indicator of future progress. Moreover, the STM method has identified nine pivotal topics grouped into three categories relating to GenAI in education. By shedding light on these emerging themes, this study provides educators and researchers with valuable insights into the future of GenAI in education.
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