AI Publication Footprint and National AI Readiness: Global Geographic and Income-Based Disparities

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

In the contemporary world, artificial intelligence (AI) is driving profound changes across global institutional, technological, and social processes. However, countries’ readiness for its integration varies substantially by economic development, regional characteristics, and digital maturity. Accordingly, this article aims to assess the alignment between the AI publication footprint, based on Scopus publication data, and national AI readiness, measured by the IMF AI Preparedness Index, across 173 countries, classified by geographic region and income level. The application of analysis of variance (ANOVA), the Kruskal–Wallis test and post hoc comparisons, correlation, regression, and cluster analysis enabled the identification of multidimensional relationships between scientific activity and the four key dimensions of digital maturity: digital infrastructure, human capital/labor market, innovation/economic integration, and regulation/ethics. The results revealed a strong overall global correlation between AI publication footprint and national AI readiness (r = 0.68, R2 = 0.46), with the highest level of alignment observed in high-income countries’ readiness (r = 0.67, R2 = 0.45), and regions such as the Americas (r = 0.72, R2 = 0.51) and Europe (r = 0.57, R2 = 0.33). At the same time, lower-income countries demonstrate weak or statistically insignificant relationships, indicating persistent structural barriers. Cluster analysis identified four types of countries, ranging from those with high levels of digital maturity to those with lower levels of national AI readiness. These findings highlight diverse national development trajectories and the need for differentiated policy approaches. However, the AI Publication Footprint is based on absolute cumulative Scopus publication counts and should be interpreted as a proxy for AI knowledge-production capacity rather than a normalized measure of research intensity or actual AI adoption. Given the cross-sectional and primarily bivariate design, the results indicate associations rather than causal effects and do not directly capture educational outcomes. The findings may have implications for education and sustainability by suggesting that disparities in infrastructure, human capital, innovation capacity, and responsible governance may shape the conditions for inclusive and sustainable AI-enabled education; these implications require direct empirical testing. Accordingly, the study emphasizes the need for adaptive policy frameworks that account for regional and economic disparities.