Artificial Intelligence in Sustainable Education: A Bibliometric Analysis and Future Research Directions

This study investigates the role of Artificial Intelligence (AI) in sustainable education through a bibliometric analysis, aiming to explore research trends, key contributors, citation analysis, co-authorship, and thematic developments in the field.As AI becomes increasingly integrated into Education, it is crucial to understand its impact on learning personalization, institutional efficiency, and sustainability.The study also identifies research gaps and provides recommendations for future exploration.The study employs a bibliometric and content analysis methodology using Scopus data.Two hundred seventy-six documents (2016-2025) were analyzed through descriptive statistics, citation analysis, co-word analysis, and co-authorship networks, utilizing VOSviewer and Biblioshiny for data visualization.The analysis examines publication trends, top-cited articles, leading institutions, and international collaborations to map the intellectual landscape of AI in sustainable education.The findings indicate a significant increase in AI-related publications after 2019, reflecting growing global interest.India, the USA, and China lead research output, while Sustainability (Switzerland) and Lecture Notes in Networks and Systems are the most prominent publication sources.The co-authorship analysis highlights strong global research collaborations, with the UK, Brazil, and China playing key roles.Thematic clustering reveals four major research areas: AI-driven Environmental Education, AI in Education, sustainable education frameworks, and AI's technical advancements in learning systems.This study provides a comprehensive, macrolevel bibliometric analysis that maps global research dynamics, identifies intellectual structures, and visualizes collaborative networks in AI and sustainable education.Despite its contributions, the study has several limitations.First, while Scopus offers broad and reputable coverage of peer-reviewed literature, the exclusive reliance on this database limits the inclusion of potentially relevant studies indexed in other databases such as Web of Science (WoS).This may restrict the diversity and comprehensiveness of the findings.Future research should consider cross-validating results using multiple databases to ensure a more holistic understanding of AI in sustainable education.Second, the exclusion of non-English publications may limit the diversity of perspectives.Third, the study primarily focuses on journal articles and conference papers, excluding books and institutional reports that might offer more profound insights.