Generative artificial intelligence in education: A topic‑based bibliometric analysis

Information and communication technologies have transformed education, driving it towards intelligent teaching and learning. With the rise of generative artificial intelligence (AI), represented by tools such as ChatGPT, there is also a growing body of literature on generative AI in education. In this study, we searched the Scopus, ERIC, and Web of Science databases as well as the proceedings of the International Conference on Artificial Intelligence in Education and the International Conference on Educational Data Mining. These searches yielded 1,158 papers that we subjected to topic modelling analysis, the Mann-Kendall trend test, and keyword analysis to comprehensively review the evolving trends and dynamics of generative AI in education, active publication sources and authors, major research topics in generative AI education, and potential directions for cross-thematic research. We analysed the trends of keywords and relevance during different sub-periods. Topic modelling enabled us to classify all abstracts of the reviewed papers into 12 topics and identify the tendency of each topic. The topics included predicting student performance, learning tutoring systems, generative AI and AI literacy, writing/essay automated grading, chatbot-based learning/assessment, gamified/game-based learning & AI learning environments, emotional engagement, automatic feedback, question generation, generative AI use in peer assessment, personalised recommendation system, and simulation-based study environment. Finally, a heat map and a hierarchy cluster provided information about the correlations between topics and the potential integration of different research directions for generative AI in education, offering a reference for future research.