Identifying Research Trends and Themes in Artificial Intelligence in Education: A Topic Modeling Approach

• In Event: Future-Oriented Dialogues on AI in Education

Abstract

As artificial intelligence (AI) becomes increasingly integrated into education, synthesizing its expanding research landscape is essential. This study employs Latent Dirichlet Allocation (LDA) to analyze 5,821 articles (2016–2025) from the Web of Science, identifying ten key themes: the use of generative AI tools and large language models in classrooms; ethical and theoretical foundations; personalized learning; intelligent tutoring systems; educational infrastructure; teacher’s technology integration; language education via chatbots; game-based learning; curriculum design; and predictive analytics. The findings provide a data-driven overview of AI in education, highlighting prevailing trends and research gaps. This work informs future research, policy, and instructional design by mapping how AI is transforming educational theory and practice.

Authors

• Masoud Askarnia, Texas Tech University

• Jongpil Cheon, Texas Tech University

• Fethi A. Inan, Texas Tech University