Ekin, Cansu CigdemCantekin, Omer FarukPolat, ElifHopcan, Sinan2025-02-052025-02-0520251360-23571573-760810.1007/s10639-024-13225-6https://doi.org/10.1007/s10639-024-13225-6https://hdl.handle.net/20.500.14411/10410Cantekin, Omer Faruk/0000-0001-5096-3233; Ekin, Cansu Cigdem/0000-0003-4838-9708Artificial Intelligence in Education (AIED) is a broad and multifarious area of study that spans across various academic fields. Due to the high numbers of studies in this field, it seems too challenging to analyze all of them in depth in a single study. Additionally, there is a lack of research that provides a comprehensive overview of the main trends and topics in AIED. This study, hence, aims to fill this gap by using text mining techniques to examine how artificial intelligence (AI)-related research in education has evolved over time. To this end, a total of 11,027 articles indexed by the Scopus database in the field of education between 1967 and 2023 were examined. Based on the findings, there has been a significant increase in AIED since 2014, covering 73% of the publications. Over the past three decades, AIED research has increasingly concentrated on engineering student populations and conference proceedings. Notably, AI solutions are extensively employed in education, with a strong focus on personalization, assessment, and evaluation. They also play a prominent role in research review processes, such as text mining and topic modeling for summarizing research findings. The findings contribute to the field, enhancing our understanding of the patterns of AI's integration into education and offering guidance for prospective research endeavors.eninfo:eu-repo/semantics/closedAccessArtificial Intelligence in Education (AIED)Text miningTopic modelingLatent Dirichlet allocationAIEDArticleQ1Q1WOS:0013917474000010