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All Journal Acta Pedagogia Asiana
Na Li
Academy of Future Education, Xi’an Jiaotong-Liverpool University, Suzhou, China

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Application of Mixed Reality Technology in Vocational Education: A Bibliometric Analysis Wang Li; Tianyu Zhang; Na Li; Stuart Thomason; Xiaojun Zhang
Acta Pedagogia Asiana Volume 5 - Issue SI - 2026
Publisher : Tecno Scientifica Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/apga.v5iSI.1070

Abstract

Mixed reality (MR) technology, which integrates virtual reality (VR) and augmented reality (AR), has emerged as a transformative tool in vocational education by enabling immersive and interactive learning environments. This study aims to analyze the application status, research hotspots, and future development trends of MR technology in vocational education through a bibliometric approach. Data were collected from Scopus and Web of Science databases, resulting in 47 relevant publications after screening. Bibliometric analysis was conducted using Biblioshiny to evaluate publication trends, keyword co-occurrence, influential authors, and collaboration networks. The results indicate a significant increase in research activity following the release of major MR devices such as HoloLens and Vision Pro. Keyword analysis reveals that “mixed reality,” “augmented reality,” and “professional training” are dominant themes, highlighting the strong focus on immersive learning and skill development. MR applications are widely implemented in engineering, medical training, and professional education, providing safe and efficient simulation-based learning environments. Future research directions emphasize the integration of MR with artificial intelligence, personalized learning systems, and cross-platform applications, as well as improvements in usability and accessibility. This study provides valuable insights for researchers, educators, and policymakers to support the effective and sustainable implementation of MR technology in vocational education.
A Bibliometric Analysis of AI-supported Teacher Education Tianru Zhang; Wang Li; Na Li; Stuart Thomason; Xiaojun Zhang
Acta Pedagogia Asiana Volume 5 - Issue SI - 2026
Publisher : Tecno Scientifica Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/apga.v5iSI.1115

Abstract

Teacher education has undergone substantial transformation in recent decades, particularly with the rapid integration of artificial intelligence (AI)–driven educational technologies into training and professional development programs. These advancements have reshaped pedagogical approaches, instructional design, and the overall structure of teacher preparation. To provide a comprehensive understanding of the scientific development in this domain, this study systematically examines the application of AI in teacher education over the past 25 years. A total of 107 peer-reviewed articles were carefully screened and selected from two major academic databases, Scopus and the Web of Science (WoS) Core Collection. Using bibliometric analysis, this study identifies key publication trends, influential authors, collaborative networks, and emerging research themes. The findings reveal a consistent and significant increase in scholarly attention toward AI-assisted teacher education, particularly in the last decade. Moreover, the impact of AI is no longer confined to pre-service teacher training but has expanded to support continuous, lifelong professional development. AI technologies such as intelligent tutoring systems, adaptive learning platforms, and data-driven decision-making tools, are increasingly being utilized to enhance teachers’ instructional competencies, reflective practices, and personalized learning pathways. Importantly, the analysis highlights a recent surge in the adoption of generative AI within teacher education, especially over the past two years. This development signals a paradigm shift, where AI is not only used as a supportive tool but also as a co-creator of educational content and pedagogical strategies. As a result, teacher training models appear to be entering a new phase characterized by innovation, personalization, and increased reliance on human–AI collaboration. Overall, this study provides valuable insights into the evolving landscape of AI in teacher education and underscores its growing significance in shaping the future of teaching and learning.