Optimizing Teacher Training and Retraining for the Age of AI-Powered Personalized Learning: A Bibliometric Analysis

Abstract

The rapid advancement of artificial intelligence (AI) technologies has ushered in transformative changes in education, with AI-powered personalized learning systems emerging as a game-changing innovation. However, the successful implementation of these intelligent systems hinges on the preparedness and competence of educators to effectively harness their potential. This bibliometric analysis provides a comprehensive exploration of the research landscape on teacher training and retraining for AI-powered personalized learning. By analyzing publications, authors, institutions, countries, sources, and keyword co-occurrences, this study unveils key insights, trends, and potential gaps. The results highlight the recent surge in research interest, driven by practical AI applications and the COVID-19 pandemic’s impact on education. Influential contributors, institutions, and countries are identified, shedding light on the geographical distribution and collaborative networks shaping this field. The analysis reveals the multidisciplinary nature of the research, with contributions from diverse domains such as educational technology, artificial intelligence, sustainability, and wireless communications. Through keyword co-occurrence analysis, prevalent themes, concepts, and emerging trends are uncovered, including the central focus on teachers, technology, teaching practices, classroom environments, curriculum, and specific AI models like ChatGPT. While the study identifies potential research gaps, such as the need for more pedagogical implications of AI in education, the insights gained can assist in development of effective teacher training and retraining programs, equipping educators to navigate the transformative age of AI-powered personalized learning.