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

- Mintii, I.S. (orcid.org/0000-0003-3586-4311) and Semerikov, Serhiy O. (orcid.org/0000-0003-0789-0272) (2024) Optimizing Teacher Training and Retraining for the Age of AI-Powered Personalized Learning: A Bibliometric Analysis Information Technology for Education, Science, and Technics. ITEST 2024. Lecture Notes on Data Engineering and Communications Technologies, 2 (222). pp. 339-357. ISSN 2367-4520

Text ITEST_2024_vol_2-350-368_Mintii_Semerikov.pdf Download (2MB)

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.

Item Type:	Article
Additional Information:	https://link.springer.com/chapter/10.1007/978-3-031-71804-5_23
Keywords:	Teacher Training, Professional Development, Artificial Intelligence, Large Language Models, Personalized Learning, Adaptive Learning, Education Technology, Bibliometric Analysis, Research Trends
Subjects:	Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 00 Prolegomena. Fundamentals of knowledge and culture. Propaedeutics > 004 Computer science and technology. Computing. Data processing > 004.9 Application-oriented computer-based techniques Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 3 Social Sciences > 37 Education > 37.01/.09 Special auxiliary table for theory, principles, methods and organization of education > 37.02 General questions of didactics and method Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 3 Social Sciences > 37 Education > 374 Education and training out of school. Further education Science and knowledge. Organization. Computer science. Information. Documentation. Librarianship. Institutions. Publications > 3 Social Sciences > 37 Education > 378 Higher education. Universities. Academic study
Divisions:	Institute for Digitalisation of Education > Department of Open Education and Scientific Information Systems
Depositing User:	Сергій Олексійович Семеріков
Date Deposited:	08 Jul 2026 09:54
Last Modified:	08 Jul 2026 09:54
URI:	https://lib.iitta.gov.ua/id/eprint/749827

Downloads

Actions (login required)

View Item