Artificial Intelligence in Adaptive Learning for Education: A Bibliometric Analysis
Artificial intelligence (AI) has emerged as a transformative technology in education, particularly adaptive learning, which supports personalized and learner-centered experiences. Despite the rapid growth of research on AI for adaptive learning, a comprehensive understanding of its publication performance, thematic structure, and technological evolution remains limited. To overcome this gap, the study aims to systematically map and evaluate the development of research on AI for adaptive learning in education using bibliometric methods. A bibliometric analysis was conducted using data from the Scopus database covering the period from 2015 to 2026. The study analyzed 2,354 publications through two complementary methods: performance analysis and science mapping. Performance analysis was used to evaluate publication growth, influential countries, sources, and highly cited documents. Science mapping techniques, including keyword co-occurrence analysis, were used to identify major research themes and the emerging AI technologies landscape. The findings show that research output has significantly risen after 2023 and recorded the highest number of publications in 2025. India emerged as the most productive country, while the United States had the highest citation impact. Citation analysis highlights adaptive and personalized learning systems, intelligent educational systems, and generative AI applications as key intellectual foundations of the field. Science mapping further revealed that machine learning, intelligent tutoring systems, generative AI, learning analytics, and large language models are central AI technological themes in adaptive learning research. These recent trends indicate a growing shift towards conversational AI, generative AI, and personalized intelligent learning environments. In conclusion, this study provides insights into publication trends, contributors, research themes, and emerging AI technologies in adaptive learning, which could assist researchers, educators, policymakers, and educational technology developers in improving intelligent adaptive learning systems.