From Global Landscapes to Local Contexts: A Bibliometric Analysis of Artificial Intelligence and Student Behavior

Abstract

The development of artificial intelligence (AI) applications has profound implications for higher education,including student behaviors, engagement, and productivity in learning. While academic literature addressing the implications of AI applications is on the rise, its relevance for fields of economics, business,and the social sciences remains disparate and localized. In this regard, this paper analyzes the bibliometric landscape of AI research using a set of indicators that include keyword co-occurrence, time and research density, and emerging research fronts using VOSviewer, and the search was limited to Scopus-indexed journal articles (2000-2025).A descriptive analysis was done based on three sets of documents, namely the global baseline, the discipline-specific body of work,and a local Malaysian dataset. The global dataset has produced a mature and integrated network with a primary emphasis on educational data mining and learning analytics.Nevertheless, temporal overlay visualization analysis reveals a sharp transition to the human and ethical factors surrounding generative artificial intelligence, including academic integrity and frameworks for evaluating teacher acceptance of disruptive technologies. In contrast, the Malaysian dataset was found to have a sparsely connected network, suggesting the growing nature of local research. Indeed, studies pertaining to Malaysia appear to focus on the interactions between students and ChatGPT and lack psychological theories in any of the included works.This study has identified the fundamental disconnect between regional and international research practices.As such, it can serve as the foundation for future studies that seek to understand the effects of artificial intelligence on the emotional,social, and academic development of students in developed digital economies.