AI-generated content in personalized learning and intelligent tutoring systems: a bibliometric analysis
Chengxuan Liu, Walton Wider, Hao Wu, Leilei Jiang, Jingjing LiPurpose
This study maps the intellectual structure and emerging thematic trajectories of research on AI-generated content (AIGC) in personalized learning and intelligent tutoring systems (ITS). It addresses the limited understanding of how AIGC-enabled personalization reorganizes learner observation, instructional communication, feedback, pedagogical mediation and institutional accountability.
Design/methodology/approach
A bibliometric science-mapping design was applied to 511 English-language journal articles retrieved from the Web of Science Core Collection on January 30, 2026. Bibliographic coupling identified contemporary research fronts, while co-word analysis examined emerging themes and future-oriented research directions. The mapped findings were interpreted through a cybernetic and social systems lens focused on feedback, adaptation, learner agency, pedagogical communication and organizational governance. This lens informed the interpretation of the bibliometric results; it did not constitute an additional empirical test.
Findings
Publications and citations increased sharply from 2023 onward. Bibliographic coupling identified five research fronts: cognitive and pedagogical infrastructure; large language model-driven adaptive architectures; human agency and learner experience; adoption, ethics and governance; and stakeholder readiness. Co-word analysis revealed five thematic trajectories: large language model-centered tutoring ecosystems, institutional integration, real-time adaptive learning, pedagogical integration in digital learning contexts, and ethical and creative personalization. Together, these patterns suggest a shift beyond technology-centered automation toward recursive educational regulation, in which technical generation, pedagogical interpretation, learner response, and institutional correction remain interdependent.
Originality/value
The study provides a focused bibliometric mapping of AIGC in personalized learning and ITS and offers a theory-informed integrative framework. The framework distinguishes AIGC and ITS as technical systems from pedagogical communication, learner meaning-making, organizational decision premises, and reflexive governance. It positions responsible personalization as dependent on feedback quality, educator oversight, learner agency and institutional capacity to review and correct AI-supported instructional decisions.