This study presents a hybrid bibliometric and BERTopic-based synthesis of a decade of rapid growth in research on the convergence of augmented reality (AR) and artificial intelligence (AI) in education. Drawing on publications retrieved from Web of Science and Scopus between 2014 and 2024, the study combines bibliometric analysis with transformer-based semantic topic modelling to examine the evolution, intellectual structure, and thematic development of AR-AI educational research. An initial dataset of 2,634 records, comprising 648 from Web of Science and 1,986 from Scopus, was refined through deduplication, relevance screening, and manual validation, resulting in a master corpus of 2,159 records. From this corpus, 1,709 records were analysed using Bibliometrix to examine publication growth, geographic distribution, sources, authorship, collaboration patterns, citations, and keywords. A separate semantic corpus of 1,848 documents was used for BERTopic modelling after text-quality filtering and peripheral-topic removal. The BERTopic model identified 16 meaningful topics with a low outlier rate of 5.57%, which were synthesised into five higher-level themes: healthcare and clinical simulation; intelligent and adaptive learning systems; professional and high-risk training; human-centred, assistive, and inclusive learning; and metaverse, cultural, and emerging learning ecosystems. The findings indicate increasing convergence of AR and AI within domain-specific learning environments that combine immersive visualisation, learner modelling, adaptive feedback, simulation, and data-informed support. The study contributes a large-scale, dual-database, and semantically enriched synthesis that integrates structural mapping with conceptual interpretation. The results offer implications for designing inclusive, ethical, and pedagogically meaningful AR-AI learning environments.
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