Artificial Intelligence in Intelligent Tutoring Systems for Education Literature Review and Bibliometric Analysis Using R-Biblioshiny

 The rapid advancement of Artificial Intelligence (AI) has accelerated the integration of technology into digital learning, particularly through Intelligent Tutoring Systems (ITS) that are capable of adapting instructional content, feedback, and learning pathways to students’ individual needs. The growing volume of publications on AI-based ITS highlights the need for a systematic mapping of the literature to better understand research trends, thematic emphases, and future research directions. This study aims to analyze publication trends, identify influential authors, institutions, journals, and countries, map the conceptual structure of the research field, and uncover research gaps and potential avenues for future studies. A quantitative approach was employed using bibliometric analysis. Data were retrieved from the Scopus database through searches of titles, abstracts, and keywords, and were subsequently screened using the PRISMA flow diagram, resulting in 322 articles published between 2012 and 2026. Bibliometric analysis was conducted using the Bibliometrix package and Biblioshiny to examine publication patterns, citation performance, collaboration networks, and keyword and thematic relationships. The findings indicate a steady increase in publications, with dominant themes centered on AI, intelligent tutoring systems, and adaptive learning. However, studies focusing on pedagogical implementation and long-term learning outcomes remain relatively limited. These results point to significant opportunities for future research, particularly in empirical evaluation and pedagogical integration. Overall, this study provides a comprehensive overview of the development of AI-based ITS research and serves as a valuable reference for researchers and practitioners in designing learning systems that align with educational needs.