Personalized and Interactive Mobile Learning in Early Childhood Education: A Bibliometric Study (2015–2024)
DOI:
https://doi.org/10.3991/ijim.v19i10.53587Keywords:
early childhood education, bibliometric analysis, artificial intelligence, emerging mobile and interactive models, personalized learning, blended learning, adaptive learning, AI-assisted learningAbstract
Over the past decade, early childhood education (ECE) has undergone unprecedented transformations driven by rapid advancements in artificial intelligence (AI) and big data, as well as mobile and interactive technologies. Emerging technologies have profoundly reshaped the ECE landscape, fostering innovations in educational models, teaching methodologies, and learning experiences. This study conducts a bibliometric analysis to explore the evolution and innovation of emerging mobile and interactive models (EMIM) in ECE, with a specific focus on the application of personalized learning, adaptive learning, blended learning, intelligent tutoring, and AI-assisted learning on the foundation of EMIM. Through a bibliometric review of relevant literature over the last decade, this paper examines trends in annual publications, leading research sources, national and regional contributions, author collaborations, and the thematic evolution of research topics. The findings reveal a general upward trend in publication output in the emerging mobile and interactive models in early childhood education (ECE-EMIM) domain, with personalized learning and the application of mobile-enabled AI technologies emerging as central research themes. The United States, China, and European countries lead the field, with increasing cross-national collaboration. Moreover, research topics have progressively shifted from foundational technological exploration to more sophisticated personalized and intelligent learning models based on EMIM. As technological advancements continue, ECE-EMIM is poised to further drive global innovation in preschool education.
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Copyright (c) 2025 Lu Xu, Mohd Nazri Abdul Rahman, Seng Yue Wong, Zhongyao Chen

This work is licensed under a Creative Commons Attribution 4.0 International License.

