Building Sustainable International Partnerships: Evidence-Informed Strategies Based on a Comparative Analysis of Nigerian Universities' AI Integration and Readiness
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Artificial intelligence (AI) technologies are transforming higher education globally, yet evidence-based guidance for designing international partnerships to build AI capacity in developing regions remains scarce. This study provides the first systematic comparative assessment of AI integration across Nigerian university types, establishing baseline data for institutions, international partners, and the broader technology adoption literature. We conducted comparative content analysis of 45 Nigerian universities stratified by institutional structure (15 Federal, 15 State, 15 Private), assessing AI integration across six dimensions: infrastructure, curriculum, research, industry partnerships, international collaborations, and policy frameworks using researcher-developed 10-point scales. Drawing on an integrated conceptual framework combining institutional theory, resource-based view, and network theory, data were analyzed using one-way ANOVA, Pearson correlation analysis, and multiple regression. Results reveal moderate overall AI integration (M = 4.79, SD = 1.71) with meaningful variation across dimensions and institution types. Federal universities demonstrated highest integration (M = 5.25), private universities excelled in curriculum integration (M = 5.87), and state universities showed emerging strength in policy frameworks. Institution age (β = 0.34, p = 0.021) and geographic location (β = 0.28, p = 0.045) significantly predicted integration levels, while institution type was not significant when controlling for other factors. The strong correlation between international collaborations and industry partnerships (r = 0.74, p < 0.001) suggests network connections reinforce each other. These findings challenge assumptions that governance structure determines integration capacity and provide foundation for evidence-informed international partnership design.
