Abstract

Examining the scope of research on AI teaching and learning in adult education is important for keeping up with current trends. It also helps find gaps that need more research in this area. This study conducts a comprehensive analysis of citation metrics in AI teaching and adult learning up to August 20, 2023, utilizing a Bibliometric analysis approach. This paper examines 435 selected papers that assess key citation metrics (total citations, citations per year, per paper, and per author) to gauge research impact. The main result shows that a significant increase in research output since 2009, with 2022 being the year of highest publication volume. The findings reveal robust scholarly engagement, articles and conference papers dominate this field, comprising 89.66% of the corpus, with peer-reviewed articles and conference papers taking precedence. English is the predominant language of publication (98.39%), while other languages, such as Chinese, Spanish, and Portuguese, are used to a lesser extent. Social sciences (51.03%) are the primary focus of this research, followed by computer science (45.75%), engineering (26.44%), and business-related fields (9.89%). This study's implications are twofold. Theoretically, it underscores the ongoing significance of AI-enhanced adult education, encouraging exploration of evolving theoretical frameworks. Managerially, it advises practitioners and policymakers to draw insights from highly cited articles when making decisions about program development and implementation. Future research could be updated with more recent data to incorporate changing citation trends investigating highly cited articles' content and impact may reveal their influence. In summary, this analysis provides valuable insights into the scholarly influence of AI in adult education, offering a solid foundation for further exploration of theoretical and practical aspects within this dynamic field.