Abstract
This study conducted thematic mining and evolutionary analysis of research outcomes on the integration of artificial intelligence into higher education. A total of 418 core journal articles published between 2018 and 2023 from the CNKI and Web of Science databases were selected. The LDA+Word2vec topic model was used for thematic mining, and cosine similarity was calculated to explore the similarity between topics. Finally, a Sankey diagram was created to illustrate the evolutionary relationships between different themes. The results indicate that research on the integration of artificial intelligence into higher education can be divided into three time periods with six themes. These themes include intelligent medical education talent cultivation, research on intelligent online education platforms/systems, intelligent disciplinary major construction, transformation of personalized teaching modes through human-machine collaboration, digitized higher education talent cultivation, the application and impact of generative artificial intelligence, as well as two thematic evolutionary paths: intelligent medical education talent cultivation and research on intelligent online education platforms—intelligent disciplinary major construction—digitized higher education talent cultivation, research on intelligent online education platforms—transformation of personalized teaching modes through human-machine collaboration—application and impact of generative artificial intelligence. The purpose is to provide insights into research on the integration of artificial intelligence into higher education.
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Hou, J., Wan, J. (2024). Theme Mining and Evolutionary Analysis of Artificial Intelligence Integration in Higher Education Research. In: Tu, Y.P., Chi, M. (eds) E-Business. New Challenges and Opportunities for Digital-Enabled Intelligent Future. WHICEB 2024. Lecture Notes in Business Information Processing, vol 516. Springer, Cham. https://doi.org/10.1007/978-3-031-60260-3_10
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