Artificial Intelligence in Higher Education (2012–2023): A Bibliometric Analysis of Research Trends and Student Learning Outcomes

HRMARS - Purpose: This study aims to examine the development trajectory, research hotspots, and thematic evolution of artificial intelligence (AI) applications in higher education over the past decade. By mapping global scholarly output, the study seeks to provide a comprehensive understanding of how AI is reshaping pedagogical practices, learning environments, and instructional strategies. Design/methodology/approach: A bibliometric analysis was conducted using literature published between 2012 and 2023 retrieved from Scopus, Web of Science, and CNKI. Citation analysis, keyword co-occurrence, and Latent Dirichlet Allocation (LDA) topic modeling were employed, supported by visualization tools such as VOSviewer and CiteSpace. The analysis identified research trends, collaboration patterns, and emerging thematic clusters in the field. Findings: The results indicate a sustained growth in the number of publications, reflecting the increasing academic interest in AI-enhanced higher education. Key research themes include personalized learning, intelligent tutoring systems, learning analytics, and data-driven decision-making. The findings also reveal strong international research contributions, particularly from the United States, China, and the United Kingdom, highlighting the global relevance and collaborative nature of this field. Research limitations/implications: This study is limited by its reliance on published literature from major databases, which may exclude relevant grey literature. Differences in indexing and data formats across databases may also introduce noise in data integration. Future research should consider broader data sources and incorporate cross-validation approaches to improve reliability. Practical implications: The insights generated offer valuable guidance for universities, policymakers, and educational technologists seeking to implement AI-driven innovations in teaching and learning. The findings underscore the importance of interdisciplinary collaboration and informed decision-making in developing effective AI applications in higher education. Originality/value: This study provides one of the most comprehensive bibliometric overviews of AI research in higher education from 2012 to 2023. By integrating citation metrics, co-occurrence analysis, and advanced topic modeling, it offers a nuanced understanding of the field’s evolution and proposes new directions for ethical, interdisciplinary, and sustainable AI development in university contexts.