Journal Article: Generative AI in Higher Education: A Bibliometric Review of Emerging Trends, Power Dynamics, and Global Research Landscapes

The article linked below was recently published by Computers and Education: Artificial Intelligence.

Title

Generative AI in Higher Education: A Bibliometric Review of Emerging Trends, Power Dynamics, and Global Research Landscapes

Authors

Kun Dai
The Chinese University of Hong Kong

Yabing Liu
The Education University of Hong Kong

Xiaofan Zhang
The Chinese University of Hong Kong

Source

Computers and Education: Artificial Intelligence
Online: January 9, 2026

Abstract

The rapid evolution of Generative Artificial Intelligence (GenAI) is reshaping higher education (HE), offering transformative opportunities for academic engagement while posing significant challenges to academic integrity, ethical frameworks, and global research power dynamics. This study maps the recent (2022-2025) research landscape of GenAI in HE through a bibliometric analysis of 2762 articles from the Web of Science Core Collection. Employing multipolarity as an analytical lens, this study examines the power dynamics within this research domain reflected by publication records from different countries (or regions). Findings highlight surging global interest in GenAI in HE, with contributions led by the US, China, and the UK, alongside rising participation from non-Western scholars and institutions. By identifying the major topics, this study uncovers a more nuanced trajectory of GenAI-related discourse in HE. By examining publication status, contributors, and research topics, this study provides insights for stakeholders navigating the complexities of GenAI integration into HE and suggests trajectories for future research in this rapidly evolving field.

Keyword Co-Occurrence Networks Visualization Map Source:10.1016/j.caeai.2026.100544 1-s2.0-S0099133325001934-mainDownload PDF from here.

See original article ScienceDirect: https://www.sciencedirect.com/science/article/pii/S2666920X26000056

Read original article at InfoDocket: Read More

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