AI-Driven Transformations in Higher Education: A Citation and Co-citation Analysis

Authors

DOI:

https://doi.org/10.52756/ijerr.2024.v45spl.021

Keywords:

Artificial intelligence, AI, bibliometric analysis, citation analysis, co-citation analysis, higher education

Abstract

Integrating artificial intelligence (AI) in the educational field can revolutionize teaching and learning outcomes, increase research capacity, and expedite administrative processes. The application of AI-powered virtual learning aids, customized learning platforms, and intelligent educational platforms, can boost the engagement of students, offer real-time feedback and support, and aid customized learning. Additionally, AI-based administrative systems may automate repetitive processes like financial aid processing, enrolment, and admissions, freeing up important resources for more strategic endeavors. Thus, this study aims to synthesize the literature exploring AI’s impact on higher education through citation and co-citation analysis. Data from the Scopus database from 2015 to 2024 yielded 1140 papers. The data was analyzed using Biblioshiny and Vosviwer software to determine the publishing pattern, the most cited papers, the most prolific writers, sources, nations, and the network of co-citations between authors and references. The findings reveal a sharp and rapid growth (79.24%) in this industry, suggesting a significant spike in interest. In terms of overall citations, the UK became one of the top countries (1689). The author "Tan S" obtained the most citations (1869) with 10 publications, whereas "Kerr S" and "Poenici Sad" produced just one article and had the highest average citation (588). "Research And Practice in Technology-Enhanced Learning" and "Journal of Applied Learning and Teaching" were the most influential journals in terms of average and total citations, respectively. The uniqueness of the study is that it assesses the literature on AI's impacts in the fields of business and management as well as social science using citation and co-citation analysis. The outcomes of the study will have substantial implications that can help professionals, researchers, and decision-makers create policies.

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Published

2024-11-30

How to Cite

., S., Priyalaxmi Gurumayum, Neelu Tiwari, Meenakshi Kaushik, Chitra Jha, & Arora, M. (2024). AI-Driven Transformations in Higher Education: A Citation and Co-citation Analysis. International Journal of Experimental Research and Review, 45(Spl Vol), 261–273. https://doi.org/10.52756/ijerr.2024.v45spl.021