Generative Artificial Intelligence in Higher Education: Examining Students’ Perceived Learning Benefits in Relation to National Development
DOI:
https://doi.org/10.15170/MM.2026.60.03.05Keywords:
generative artificial intelligence, ChatGPT, higher education, digital inequality, national developmentAbstract
THE AIM OF THE PAPER
This study examines how differences in national development shape higher education students’ perceptions and learning-related use of generative artificial intelligence, particularly ChatGPT. It focuses on how the perceived learning benefits of the technology vary across socioeconomic contexts and how these differences relate to the various dimensions of digital inequality.
METHODOLOGY
The study draws on a large international dataset comprising responses from 11,645 students in 49 countries. Descriptive statistics, Pearson correlation analysis, linear regression, and cluster analysis were used to examine students’ perceptions and patterns of use. The findings are interpreted through the theoretical perspectives of digital inequality and socio-technical systems, with particular attention to the combined influence of individual and structural factors.
MOST IMPORTANT RESULTS
A statistically significant negative association was found between national development and the perceived learning benefits of generative artificial intelligence. The association was weak at the individual level and moderate at the country level. Students from less developed countries generally viewed the technology’s role in supporting learning more positively, a pattern that is consistent with its potential compensatory role. The cluster analysis identified three clearly distinguishable groups of students: techno optimists, skeptics, and moderately positive students. These groups differed in the extent to which they perceived positive learning-related effects, and their distribution was significantly associated with differences in national development.
RECOMMENDATIONS
The findings indicate that the use of generative artificial intelligence in higher education is strongly shaped by its context and should therefore not be approached in the same way in all settings. In more developed countries, greater emphasis should be placed on encouraging purposeful, carefully considered, and pedagogically grounded use. In less developed settings, the technology may help expand learning opportunities and reduce disparities in access. The study contributes to the understanding of the educational role of generative artificial intelligence by showing that its perceived learning benefits vary across socioeconomic contexts and therefore cannot be assumed to be universal. Higher education institutions should accordingly develop inclusive implementation strategies that respond to different needs and take local conditions into account.
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