BIBLIOMETRIC AND VISUALIZED ANALYSIS OF GENERATIVE AI CHATBOT FOR LEARNING: A STUDY FROM SCOPUS DATABASE - Hebi Yego; Jaka Nugraha; Mohamed Mihilar Shamil (Universitas Negeri Surabaya, 2026)
Phenomenon/Issue: The rapid growth of generative artificial intelligence (AI) chatbots in education has led to a significant increase in scientific publications in recent years. However, comprehensive bibliometric studies focusing on this topic remain limited. Purpose: This study aims to provide an in-depth analysis of Generative AI Chatbot research in education using a bibliometric approach. Novelty: This research fills the gap in existing literature by utilizing Garza-Reyes’s five-phase framework to provide a systematic and comprehensive visualization of the evolution of the field, specifically identifying emerging social-emotional themes. Research Methods: Data were collected from the Scopus database, resulting in 16,590 initial documents, which were refined to 230 relevant articles published between 2020 and 2025. Visualization and analysis were conducted using VOSviewer. Results: The results identify six major research clusters representing key themes. Dominant topics include artificial intelligence, chatbots, and education, while emerging topics such as empathy, trust, feedback, and interpersonal communication offer potential directions for future research. Additionally, co-authorship analysis reveals limited collaboration among authors. Research Contributions: Overall, this study provides a comprehensive overview of research trends and highlights strategic opportunities for further development and integration of Generative AI Chatbots in the learning environment.