Teaching Practices of Generative Artificial Intelligence in Distance Education: Bibliometric and Knowledge-Base Analysis

The rapid development of generative artificial intelligence (GenAI) has created new possibilities for transforming teaching practices in distance education. Although previous studies have discussed artificial intelligence applications in online learning environments, the overall knowledge structure, research development trends, and major teaching practice themes remain insufficiently explored. This study uses bibliometric and knowledge-base analyses to examine the development of research on generative artificial intelligence in distance education from 2000 to 2025. Academic publications were collected from Google Scholar and Scopus during June and July 2026. The analysis examined publication trends, research contributions, keyword relationships, citation patterns, and thematic evolution. The findings indicate that research interest in AI-supported distance education increased significantly after 2020, with particular attention given to personalized learning, automated feedback, intelligent tutoring systems, teacher support, and AI-based assessment. The knowledge structure shows a shift from early studies focused on digital learning technologies to human-centered AI integration and instructional innovation. The results also reveal several challenges, including the need for AI literacy, ethical concerns, academic integrity issues, and effective governance frameworks. This study contributes to understanding how generative artificial intelligence is reshaping teaching practices in distance education and provides theoretical and practical implications for educators, researchers, and educational policymakers.