Generative AI Tools in Higher Education Emerging Research: A Bibliometric analysis of co-citation and co-word analysis
Year: 2024
Type: conference-paper
Abstract: Artificial Intelligence (AI) is positively grasped as promising to disrupt the higher education system but it also poses a number of challenges. Although so many studies attempt to explore the matching of the possibility of this growing technology with the higher education system, ample research needs to be conducted to solve the challenges facing the renovation of higher education. With this in mind, our aim of bibliometric studies is to conduct a deep investigation into the increasingly developing scenario of Generative AI tools in higher education. We extracted data from the Web of Science database that is up-to-date till July 2024, comprising 934 relevant articles. Co-citation and co-word analyses revealed three main research clusters: advanced computationable methods, AI application in higher education, and user technology and adoption. The findings illustrated rapid diffusion of generative AI technologies with prominent emphasis on large-language models in pedagogical practices. Other critical themes center around developing AI-facilitated learning interventions, ethical challenges, and usage impact on learning outcomes. The results show that the field is inherently interdisciplinary, using ideas from educational technology, cognitive science, and AI. In addition, a rising trend is noted for the focus on academic honesty and users' involvement with AI devices. The results indicate the important implications of this study for teachers and policymakers alongside contributions to teaching and research that offer a guide to sustainable improvement across education. Future research would benefit from longitudinal studies drawing on an interdisciplinary approach to realize the long-term implications and address complex issues surrounding the integration of AI within universities.
Authors: Ye Yang, Xiong Wen, Siti Sarah Maidin
Institutions: INTI International University, Wuhan Institute of Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4409966103