Building Sustainable International Partnerships: Evidence-Informed Strategies Based on a Comparative Analysis of Nigerian Universities' AI Integration and Readiness

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Adeniranye, Stephanie J. Lunn, Olatunde Mosobalaje, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8816750/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. artificial intelligence Nigerian higher education technology adoption international partnerships comparative analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction The integration of artificial intelligence (AI) technologies in higher education represents one of the most significant transformational forces in contemporary academia (Zawacki-Richter et al., 2019a ). AI encompasses a broad spectrum of technologies including machine learning algorithms, natural language processing systems such as large language models (LLMs), e.g., OpenAI’s ChatGPT, intelligent tutoring systems, robotics, and data analytics platforms. These technologies can be leveraged by faculty for research and instruction, by administrators for institutional operations, and by students for learning support (Adamakis & Rachiotis, 2025 ). The emergence of generative AI tools since 2022 has particularly accelerated attention to how tertiary institutions prepare graduates for an AI-transformed workforce, with recent analyses indicating that AI competencies are increasingly essential for employability across sectors (Microsoft, 2025 ). International partnerships progressively aim to build AI capacity in developing regions through knowledge exchange, joint research initiatives, curriculum development support, and infrastructure sharing. For instance, the University of Michigan’s $ 6 million program centered on AI in Science African Faculty Fellows builds research capacity through extended faculty exchanges lasting up to 24 months (University of Michigan, 2024 ). Additionally, Carnegie Mellon University has established an Africa campus located in Rwanda that offers graduate programs explicitly focused on AI for social good applications (Carnegie Mellon University Africa, 2025 ). These initiatives, alongside partnerships involving European, Asian, and other African institutions, suggest movement toward collaborative approaches emphasizing mutual benefit and local ownership, though the extent to which these goals are achieved varies considerably across programs. However, international educational partnerships face significant sustainability challenges. Recent funding disruptions, including the 2025 USAID funding freeze for higher education partnerships in Africa, starkly demonstrated vulnerabilities when partnerships depend on single funding sources (Think Global Health, 2025 ; University World News, 2024 ). This disruption highlighted the urgent need for partnership models that build genuine local capacity rather than relying solely on external donor support. Effective partnership design requires understanding how institutional contexts shape AI integration and partnership readiness in developing countries’ higher education systems, yet this evidence base remains notably absent from the literature. Nigeria presents a particularly compelling case for examining AI integration patterns. As Africa’s most populous nation with over 200 million people and the continent’s largest economy, Nigeria’s higher education development carries significant regional implications (National Universities Commission, 2025 ). The Nigerian higher education landscape encompasses approximately 310 universities across three distinct governance structures: Federal universities ( n = 72) funded by the national government, State universities ( n = 67) supported by individual state governments across Nigeria’s 36 states and Federal Capital Territory, and Private universities ( n = 171) operated by religious organizations, foundations, or individuals (National Universities Commission, 2025 ). This institutional diversity creates natural variation in resources, capabilities, and governance approaches that enables systematic comparison of how different organizational factors influence AI adoption patterns. Given the recency of generative AI tools and the rapid pace of AI development, understanding of how higher education institutions in developing countries are incorporating these technologies remains limited. Recent comprehensive reviews of computing education in African countries have documented substantial bodies of work but emphasize the critical need for contextualized approaches that account for local realities (Hamouda et al., 2025 ). Research on aligning African tertiary computer science education with global standards highlights both progress and persistent gaps in preparing graduates for an AI-transformed workforce (Oudshoorn et al., 2025 ). A systematic review of AI in African education over the past five years identifies key trends, opportunities, and challenges while calling for more empirical research on institutional adoption patterns (Ahmed et al., 2025 ). Despite this growing attention, no systematic comparative research has analyzed AI integration patterns across Nigerian institutional types, limiting the ability of international partners to develop evidence-informed collaboration strategies. Our study addresses this gap by providing the first systematic comparative assessment of AI integration across Nigerian university types. Drawing on a conceptual framework combining institutional theory, resource-based view, and network theory, we assess integration across six dimensions: infrastructure, curriculum, research initiatives, industry partnerships, international collaborations, and policy frameworks. These dimensions capture the essential organizational capabilities required for meaningful AI integration: the technical foundation (infrastructure), human capital development (curriculum and research), external knowledge networks (industry and international partnerships), and strategic direction (policy frameworks). Two research questions (RQs) guided this investigation: RQ1: What is the current state of AI technology integration across Nigerian universities, and how does integration vary by institution type and dimension? RQ2: What institutional and contextual factors predict AI integration levels in Nigerian universities? By examining these questions, this study makes three contributions. First, it provides the first systematic comparative assessment of AI integration across Nigerian university types, establishing baseline data valuable for Nigerian institutions, potential international partners from any country, and the broader research community studying technology adoption in developing contexts. Second, it identifies institutional and contextual factors that predict integration levels, advancing theoretical understanding while providing practical guidance for partnership targeting. Third, building on these empirical findings, it offers evidence-informed insights for partnership development aligned with differentiated institutional capacities. 2 Literature Review The integration of AI technologies into higher education represents a transformative force reshaping pedagogical practices, research capabilities, and institutional operations globally (Crompton & Burke, 2023 ; Zawacki-Richter et al., 2019b ). This transformation manifests across multiple domains: in teaching through adaptive learning systems, intelligent tutoring, and AI-powered content generation; in administration through chatbots, enrollment analytics, and resource optimization; and in research through automated literature review, data analysis, and hypothesis generation (Adamakis & Rachiotis, 2025 ; Barnes & Hutson, 2024 ). However, AI adoption patterns vary significantly across institutional contexts and between developed and developing countries (Pedro et al., 2019 ). This literature review examines empirical evidence relevant to understanding AI integration in Nigerian universities and its implications for international educational partnerships. 2.1 AI Integration in Higher Education: Global Perspectives AI integration in higher education has accelerated dramatically in recent years, with scholarly publications on the topic increasing two to three times between 2021 and 2022 alone (Crompton & Burke, 2023 ). While AI technologies have existed for decades in forms such as robotics, natural language processing, machine learning algorithms, and expert systems, the public release of large language models such as ChatGPT in late 2022 brought AI capabilities into mainstream awareness and prompted urgent institutional responses across higher education (Chumakov, 2025 ; İpek et al., 2023 ; Nalepa & Stefanowski, 2020 ). This heightened attention reflects recognition that generative AI tools represent a qualitative shift in accessibility and applicability compared to earlier AI applications (Crompton & Burke, 2023 ; Moy & Feldstein, 2024 ). Contemporary AI applications in higher education span multiple domains. In teaching, educators employ adaptive learning systems that adjust content difficulty based on student performance, intelligent tutoring systems that provide personalized feedback, automated assessment tools that evaluate student work, and AI-powered content generators that assist with instructional material development (Lin & Chen, 2024 ; Ocen et al., 2025 ; Walter, 2024 ). Students increasingly use AI tools for research assistance, writing support, coding help, and personalized study guidance. Administrative applications include chatbots for student services, predictive analytics for enrollment management, learning analytics for early intervention with struggling students, and resource optimization systems (Microsoft, 2025 ; Mulford, 2025 ; Ocen et al., 2025 ; US Department of Education, 2023 ). These technologies are also reshaping workforce expectations for university graduates. Industry analyses indicate that employers increasingly expect graduates to demonstrate AI literacy and ability to work alongside AI systems, regardless of their field of study. Microsoft’s 2024 Work Trend Index found that AI skills are becoming essential across sectors, with employers seeking graduates who can leverage AI tools to enhance productivity (Microsoft, 2024 ). This shift creates pressure on universities to integrate AI not only as a subject of study but as a tool embedded throughout the educational experience. However, AI integration faces substantial challenges globally. Pedagogical concerns include impacts on critical thinking development and academic integrity, particularly with generative AI tools that can produce sophisticated text (Jin et al., 2025 ; Ruano-Borbalan, 2025 ; Walter, 2024 ). Equity concerns are acute, as AI adoption risks exacerbating educational inequalities through differential access to technology and digital literacy skills (Feng et al., 2025 ; US Department of Education, 2023 ). Ethical challenges include algorithmic bias, data privacy, and governance frameworks that have not kept pace with rapid AI development (Al-Zahrani & Alasmari, 2024 ). Policy responses are emerging at national and international levels, with the African Union releasing its Continental Artificial Intelligence Strategy in July 2024, providing comprehensive guidance for AI development and governance across member states (African Union, 2024 ). 2.2 Technology Adoption in African Higher Education Despite infrastructure challenges, AI integration in African higher education has progressed across multiple fronts. Recent studies document AI applications in educational contexts, including AI-driven career guidance systems that help students navigate employment pathways and bridge gaps in traditional career services (Eze et al., 2025 ). Computing education programs across Africa have developed innovative approaches to teaching AI and machine learning, with curricula increasingly incorporating practical projects and industry-relevant skills (Hamouda et al., 2025 ). Research on AI for educational development highlights emerging applications in areas such as personalized learning, administrative automation, and student support systems (Joel et al., 2025 ). A systematic review of AI in African education over the past five years identifies growing scholarly attention to these opportunities alongside persistent implementation challenges (Ahmed et al., 2025 ). However, significant barriers still constrain widespread AI adoption. The digital divide manifests through inconsistent electricity supply, limited internet connectivity, and inadequate computing facilities (Astuti & Ayinde, 2025 ; Barakabitze et al., 2019 ). Many African universities operate with aging infrastructure and unreliable power supplies that impede sustained operation of AI systems (Hamzat & Ansah, 2025 ; Maimela & Mbonde, 2025 ). Uneven distribution of resources between urban and rural institutions creates significant disparities in integration capacity (Mateko et al., 2025 ). Human capital constraints include limited faculty trained in AI technologies, insufficient professional development opportunities, and challenges retaining technically skilled personnel (Ibrahim, 2024 ; Tsakeni & Nwafor, 2025 ). A shortage of industry internship opportunities further limits students’ abilities to link theoretical knowledge to practical AI applications (Imizuokena & Ikechuku, 2025 ). Efforts to address these challenges through contextualized approaches and collaborative networks show promise. Intra-African collaborations are opening new trajectories for knowledge production and exchange among African universities, particularly within regional bodies where facilitative policy frameworks and funding mechanisms have been established (Jowi, 2024 ). The Africa Higher Education Centers of Excellence program, a World Bank-funded initiative, has established over 80 centers across 20 countries, training more than 90,000 students and fostering regional research collaboration (World Bank, 2025 ). These regional initiatives demonstrate potential pathways for building AI capacity through collaborative approaches. 2.3 The Nigerian Higher Education Context Nigeria’s higher education system, the largest in Sub-Saharan Africa, operates through three distinct governance structures that create meaningful variation in institutional priorities, resource allocation, and operational flexibility. Federal universities receive funding from the national government primarily through the Tertiary Education Trust Fund (TETFund), which provides relatively stable resources for infrastructure development, research support, and faculty capacity building (Lawal, 2019 ). This stable funding enables long-term planning but comes with bureaucratic oversight that can constrain rapid innovation. State universities are funded by individual state governments, creating significant variation in resources depending on each state’s economic conditions and political priorities; these institutions often have strong ties to regional development agendas but face funding uncertainty (Bamiro, 2012 ). Private universities operate with greater autonomy in curriculum design, hiring, and organizational decision-making, enabling rapid response to market demands, though they depend heavily on tuition revenue and philanthropic support (Bamiro, 2012 ; My School Insight, 2025 ). Within this diverse landscape, AI-related activities are emerging across all institution types. Recent studies document AI-enabled learning platforms, learning analytics systems, and AI curriculum integration in various Nigerian universities (Bali et al., 2024 ; Liverpool et al., 2009 ; Oguine et al., 2022 ). Yet, AI literacy among educators and students remains foundational across most institutions, with significant gaps in both awareness and practical skills (Ayanwale et al., 2024 ; Ibrahim, 2024 ). The COVID-19 pandemic accelerated technology adoption, forcing rapid e-learning implementation while exposing significant gaps in infrastructure, digital literacy, and institutional readiness (Azubuike et al., 2021 ; Nwachukwu et al., 2021 ; Olanrewaju et al., 2021 ). This experience catalyzed increased institutional attention to digital transformation but also highlighted the substantial investments required for sustainable technology integration. 2.4 International Educational Partnerships International educational partnerships in higher education can be defined as sustained institutional relationships in which partner organizations with diverse expertise and resources join together to devise and execute plans for common goals, emphasizing mutual benefit, capacity development, and local ownership rather than traditional donor-recipient dynamics (Gronski & Pigg, 2000 ; Obamba & Mwema, 2009 ). Such partnerships take various forms including faculty exchange programs, joint research initiatives, curriculum development collaborations, student mobility arrangements, and infrastructure sharing agreements. The effectiveness of these arrangements depends substantially on alignment between partnership structures and the capacities of participating institutions. Research on international educational collaborations identifies recurring challenges. Power asymmetries between Northern and Southern partners can undermine genuine collaboration (Bradley, 2008 ). Sustainability concerns arise when partnerships depend heavily on external funding without developing local resource bases. Misaligned priorities between partner institutions and insufficient attention to local contexts can limit partnership effectiveness (Obamba & Mwema, 2009 ). The 2025 USAID funding freeze for higher education partnerships in Africa starkly demonstrated these vulnerabilities, disrupting programs across the continent when a single major funding source was withdrawn (Think Global Health, 2025 ; University World News, 2024 ). This disruption highlighted the urgent need for partnership models that build genuine local capacity and diversify funding sources. Understanding how Federal, State, and Private universities differ in their capacities and partnership readiness would enable international partners to develop more targeted and sustainable collaboration strategies. 3 Conceptual Framework This study employs an integrated conceptual framework combining institutional theory, resource-based view (RBV), and network theory to understand AI technology integration in Nigerian universities. While classical technology adoption theories such as diffusion of innovations and the unified theory of acceptance and use of technology explain individual-level adoption (Rogers, 2003 ; Venkatesh et al., 2003 ), they inadequately capture organizational-level technology integration in resource-constrained developing country contexts. The three theoretical perspectives selected for this framework are complementary: institutional pressures create motivation for adoption, resources determine capacity to adopt, and networks provide knowledge and support for implementation. 3.1 Institutional Theory Institutional theory examines how organizational structures, norms, and legitimacy concerns influence technology adoption (DiMaggio & Powell, 1983 ; Scott, 2013 ). Universities face isomorphic pressures that shape technology adoption beyond purely technical considerations (DiMaggio & Powell, 1983 ). Coercive isomorphism arises from formal and informal pressures exerted by other organizations upon which institutions depend, including governments and resource providers. Mimetic isomorphism results from uncertainty, leading organizations to model themselves on successful peers. Normative isomorphism stems from professionalization, as members of occupational groups share common educational backgrounds and professional associations (DiMaggio & Powell, 1983 ). Empirical research has applied institutional theory to understand technology adoption in higher education contexts. Scholars examining organizational change in Colombian higher education institutions found that all three types of isomorphism influenced institutional responses to environmental pressures, with coercive pressures from government policies being particularly strong (Cardona Mejía et al., 2020 ). In the context of Nigerian higher education, institutional theory suggests that different university types may face distinct isomorphic pressures. Federal universities, as nationally-funded flagship institutions, may experience stronger normative pressures to maintain research prominence and adopt technologies that signal institutional legitimacy (Alshumrani et al., 2022 ; Amrozi et al., 2023 ; Erdmann & Toro-Dupouy, 2025 ). Private universities, operating in competitive enrollment markets, may face intensified mimetic pressures to adopt visible innovations that differentiate them from competitors (Hui, 2022 ; Saka et al., 2024 ; Tingling & Parent, 2002 ). State universities, dependent on state government funding, may encounter coercive pressures shaped by regional political priorities (Arthur, 2025 ; ÖZTÜRK ERKOÇAK, 2020 ; Sahni et al., 2025 ). 3.2 Resource-Based View Resource-based view theory explains how organizational resources and capabilities influence strategic decisions including technology adoption (Barney, 1991 ; Sun et al., 2024 ). Sustained competitive advantage derives from resources that are valuable, rare, inimitable, and non-substitutable, including infrastructure, human capital, and financial assets (Mailani et al., 2024 ). For AI integration, critical resources include computational infrastructure enabling advanced applications, faculty expertise and technical staff maintaining AI systems, and financial capacity supporting capital investments and ongoing operations (Adejo, 2025 ; Fauzi et al., 2025 ). Empirical research has applied a resource-based view to explore technology adoption in educational contexts. Researchers examining e-learning adoption in Ghanaian higher education found that institutional resources, particularly IT infrastructure, technical support staff, and faculty development programs, significantly predicted adoption success, with resource-rich institutions demonstrating more comprehensive implementation (Asunka, 2008 ). Similarly, a study of digital transformation in European universities demonstrated that human capital resources, specifically faculty digital competencies and dedicated technical personnel, were stronger predictors of successful technology integration than financial resources alone (Benavides et al., 2020 ). Resource heterogeneity is particularly salient in developing country contexts where constraints create significant capability variation across institutions (Chipembele et al., 2025 ; ul Haq et al., 2025 ). This perspective suggests that AI integration levels should correlate positively with institutional resource availability, and that resource differences across Federal, State, and Private universities should manifest in differentiated integration patterns. 3.3 Network Theory Network theory emphasizes how inter-organizational relationships facilitate knowledge transfer and resource access (Granovetter, 1973 ; Van Alstyne, 1997 ). Universities’ network connections, including research collaborations, industry partnerships, and professional associations, provide channels for acquiring technological knowledge and legitimizing innovations (Brunetti et al., 2020 ). This collaborative approach is vital in the integration of new technologies like AI, where external ties can significantly influence adoption and implementation (George & Wooden, 2023 ). Empirical research has demonstrated the importance of network relationships for technology adoption in higher education. A study of research collaboration networks among African universities found that institutions with stronger international ties produced more research output and demonstrated greater capacity to adopt new research technologies, with network centrality predicting institutional research productivity (Onyancha & Maluleka, 2011 ). Research on innovation adoption in Nigerian universities specifically found that institutions with established industry partnerships were significantly more likely to implement new technologies, as these relationships provided both resources and legitimacy for innovation (Siyanbola et al., 2012 ). AI integration networks operate at multiple levels: industry partnerships provide applied knowledge, datasets, and funding; international collaborations offer access to cutting-edge research and global standards; regional networks enable knowledge sharing among institutions facing similar challenges (Ho et al., 2023 ; Yeager et al., 2024 ). 3.4 Framework Integration and Predictions The integration of these three perspectives provides a unified conceptual framework for understanding AI adoption in Nigerian higher education (Fig. 1 ). The framework conceptualizes AI integration as simultaneously influenced by institutional pressures (institutional theory), resource capabilities (RBV), and network-facilitated knowledge transfer (network theory). These complementary lenses illuminate interconnected dimensions of technology adoption that no single theory could capture independently. The framework gives rise to testable predictions examined in this study. First, older institutions with established legitimacy should demonstrate higher overall AI integration due to accumulated organizational capabilities and resources. Second, AI integration should correlate positively with infrastructure capacity, human capital, and financial resources, with resource differences across institution types manifesting in differentiated integration patterns. Third, universities with stronger international collaborations and industry partnerships should exhibit higher AI integration, and these network connections should be positively correlated as inter-organizational ties tend to facilitate additional connections. Fourth, integration patterns should result from interactions among institutional, resource, and network factors rather than any single factor operating independently. The six dimensions assessed in this study (infrastructure, curriculum, research, industry partnerships, international collaborations, and policy frameworks) operationalize constructs from all three theoretical perspectives. 4 Research Design and Methodology This study employed a comparative content analysis methodology to systematically assess AI technology integration across Nigerian universities (Rössler, 2013 ). Content analysis is well suited for examining complex organizational phenomena through publicly available documents and institutional communications (Krippendorff, 2018 ). This approach enabled rigorous, replicable assessment without requiring human participant data collection while generating findings directly comparable across diverse institutional contexts (Elo & Kyngäs, 2008 ). 4.1 Sample Selection At sampling frame development (May 2025), Nigeria’s higher education system comprised approximately 299 universities recognized by the National Universities Commission across three governance categories: Federal universities ( n = 72, 24.1%) funded by the national government, State universities ( n = 67, 22.4%) supported by individual state governments, and Private universities ( n = 160, 53.5%) operated by religious organizations, foundations, or individuals (National Universities Commission, 2025 ). A total sample of 45 universities were targeted based on power analysis to detect medium effect sizes (Cohen’s d = 0.5) with 80% power at α = 0.05 for between-group comparisons (Cohen, 2013 ). This sample represents approximately 15% of all Nigerian universities. The study employed equal allocation stratified sampling (15 universities per institution type) rather than proportional allocation (Lohr, 2021 ). This design choice was methodologically grounded in three considerations. First, the study’s primary purpose is comparative: the research questions explicitly focus on comparing AI integration patterns across institution types rather than estimating population-level parameters for Nigerian higher education overall. Equal sample sizes maximize statistical power for between-group comparisons and pairwise tests (Maxwell et al., 2017 ). Second, equal allocation ensures adequate sample size within each stratum for reliable within-group analyses, including descriptive statistics, correlation analyses, and identification of predictive factors (Cohen, 2013 ; Maxwell et al., 2017 ). Proportional allocation would yield approximately 11 Federal, 10 State, and 24 Private universities, potentially underpowering analyses for Federal and State institutions. Third, each governance structure represents a distinct policy context worthy of independent examination; equal representation ensures that findings for smaller sectors carry equivalent analytical weight (Lohr, 2021 ). This approach aligns with established practice in comparative educational research (Cochran, 1977 ). Within each stratum, universities were selected using criteria to ensure representativeness. Geographic distribution ensured representation across all six geopolitical zones (North-Central, North-East, North-West, South-East, South-South, South-West). Figure 2 provides a map of Nigeria’s geopolitical zones with the distribution of sampled institutions. Establishment period captured temporal variation, with institutions established between 1948 and 2023 categorized as Legacy (pre-1980), Expansion (1980–2010), and Recent (2011–2025). Information availability ensured only universities with accessible websites and sufficient publicly available information were included. The final sample showed Federal universities with the longest establishment history (mean = 1979), followed by State (mean = 1998) and Private (mean = 2005). Geographic distribution showed concentration in the South-West ( n = 16, 35.6%), consistent with historical patterns, followed by South-East ( n = 8, 17.8%), North-Central ( n = 8, 17.8%), South-South ( n = 7, 15.6%), North-West ( n = 4, 8.9%), and North-East ( n = 2, 4.4%). 4.2 Data Collection Data collection followed a systematic protocol designed to ensure consistency and comprehensiveness across all institutions. Data were manually collected between May and October 2025 by trained researchers following standardized procedures. Primary data sources included official university websites and affiliated departmental sites, strategic plans and institutional documents where publicly available, course catalogs and academic program descriptions, faculty profiles and research publications, news releases and media coverage of AI-related initiatives, and partnership announcements and collaboration agreements. Each institution was assessed through a five-step process: (1) systematic review of the main university website; (2) department-level analysis with particular emphasis on computer science, engineering, and related technical departments, as these fields serve as primary indicators of AI integration given their direct engagement with AI technologies; (3) faculty profile assessment examining individual research interests and publications; (4) review of news and events for recent AI-related announcements and activities; and (5) identification of collaboration agreements and partnerships. A standardized keyword search strategy employed primary terms (“artificial intelligence,” “AI,” “machine learning,” “data science”) and secondary terms (“robotics,” “automation,” “intelligent systems,” “deep learning”). This multi-tiered approach offered a comprehensive identification of AI-related content while minimizing false positives. 4.3 Coding Framework AI integration was assessed across six dimensions using researcher-developed 10-point scales with anchored descriptors: AI Infrastructure (specialized facilities and computing resources reflecting resource-based capabilities), Curriculum Integration (AI content in academic programs capturing institutional responses to external demands), Research Initiatives (AI research, publications, and faculty expertise reflecting accumulated human capital), Industry Partnerships (private sector collaborations capturing network relationships), International Collaborations (foreign institution partnerships reflecting global network connections), and Policy Frameworks (institutional AI strategies and governance capturing formal organizational responses). Scores followed anchored descriptors: 1–2 (Minimal), 3–4 (Basic), 5–6 (Moderate), 7–8 (Advanced), and 9–10 (Cutting-edge). An overall AI integration score was calculated as the mean of the six-dimensional scores. Detailed scoring rubrics with illustrative examples for each dimension are provided in the Online Resource 1. 4.4 Data Analysis Data analysis employed both descriptive and inferential statistical techniques using Python (version 3.11) with the pandas, scipy.stats, and matplotlib libraries. Descriptive statistics were calculated for all variables, including measures of central tendency and dispersion. One-way analysis of variance (ANOVA) tested for significant differences between institution types across all dimensions. ANOVA assumptions were verified: normality was assessed using the Shapiro-Wilk test and homogeneity of variance was tested using Levene’s test (Alshahrani et al., 2025 ; Shapiro & Wilk, 1965 ; Yi et al., 2022 ). Where significant differences were detected, post-hoc Tukey HSD tests identified specific pairwise differences. Effect sizes were calculated using eta-squared (η²) for ANOVA results and Cohen’s d for pairwise mean differences (Cohen, 2013 ). Pearson correlation analysis examined relationships between dimensions, with Bonferroni correction applied for multiple comparisons to control family-wise error rate. Multiple regression analysis identified significant predictors of overall AI integration. Statistical significance was set at α = 0.05 for all analyses. 4.5 Validity and Reliability Multiple measures were implemented to ensure validity and reliability. Inter-rater reliability was established through independent coding of 20% of institutions ( n = 9) by two trained researchers. Cohen’s kappa (κ) values ranged from 0.79 to 0.85 across dimensions, indicating substantial to almost perfect agreement (Landis & Koch, 2021 ). Discrepancies were resolved through discussion and consensus, with coding guidelines refined accordingly. External validation cross-referenced news articles (67% of major initiatives verified), academic publications (78% of research claims verified), partnership announcements (54% verified), and government documents (43% of policy claims verified). Data quality varied across institutions: 55.6% had comprehensive website information, 26.7% had limited information, and 17.8% had accessibility issues addressed through additional data collection. Complete dimensional scores for all 45 institutions are available in the Online Resource 1. 4.6 Researcher Positionality Consistent with recommendations for transparency in research (Secules et al., 2021), we acknowledge relevant positionalities. The first author is Nigerian, completed undergraduate engineering education at a sampled state university, and subsequently navigated educational and professional systems across five countries (Nigeria, France, United Kingdom, United Arab Emirates, United States). The second author is a U.S.-based assistant professor of computing education who has contributed to studies on AI applications in African workforce development. The third author is a Nigerian faculty member at a sampled institution whose research focuses on integrating machine learning into engineering curricula, with visiting scholar experience at MIT and leadership roles in professional organizations at the interface of academia-industry partnerships. The fourth author is an associate Professor of multidisciplinary engineering and computing education, systems and management who draws upon experiences working in higher education and engineering education research. The fifth author is an undergraduate Computer Science student at Ivy Tech Community College whose emerging expertise in programming and data analysis contributed to the systematic data collection process; his interest in AI applications for real-world problem-solving informed attention to practical implementation considerations. These diverse perspectives, spanning faculty, professional and student roles across multiple countries, informed contextual interpretation while potential biases were mitigated through systematic coding procedures and inter-rater reliability checks. 5 Findings 5.1 Sample Characteristics The final sample comprised 45 Nigerian universities stratified across three institutional types (Table 1): Federal ( n = 15, 33.3%), State ( n = 15, 33.3%), and Private ( n = 15, 33.3%). This distribution ensured adequate statistical power for between-group comparisons while representing approximately 15% of all Nigerian universities recognized by the National Universities Commission. Temporal distribution revealed significant variation in institutional age across types. Among sampled institutions, Federal universities demonstrated the longest establishment history (range: 1948–2011, mean = 1979), followed by State universities (range: 1979–2023, mean = 1998), and Private universities (range: 1999–2014, mean = 2005). This temporal stratification enabled analysis of how institutional maturity relates to AI integration capacity. Geographic distribution showed concentration in the South-West zone ( n = 16, 35.6%), consistent with historical patterns of university establishment in Nigeria. Remaining institutions were distributed across South-South ( n = 7, 15.6%), North-Central ( n = 8, 17.8%), South-East ( n = 8, 17.8%), North-West ( n = 4, 8.9%), and North-East ( n = 2, 4.4%) zones. Table 1 Sample Distribution by Institution Type and Geographic Region 5.2 Overall AI Integration Patterns Addressing Research Question 1 regarding the current state of AI integration, analysis revealed (Table 2 and Fig. 3 ) moderate overall integration levels across the sample (M = 4.79, SD = 1.71, range: 1.83–7.83 on a 10-point scale). This finding indicates substantial room for improvement while demonstrating that Nigerian universities have initiated meaningful AI integration efforts. Federal universities had the highest mean integration scores (M = 5.25, SD = 1.64), followed by Private universities (M = 4.76, SD = 1.93) and State universities (M = 4.36, SD = 1.55). One-way ANOVA revealed that while these differences approached statistical significance (F(2,42) = 1.89, p = 0.163), the effect size was small (η²=0.08). The lack of statistical significance indicates that factors beyond institutional type may better explain integration variation. Table 2 Overall AI Integration Scores by Institution Type The top five performing institutions included University of Lagos (7.83), Afe Babalola University (7.67), Covenant University (7.33), University of Ibadan (7.25), and Ahmadu Bello University (6.83). The top performers represented both Federal universities (University of Lagos, University of Ibadan, Ahmadu Bello University) and Private universities (Afe Babalola University, Covenant University), indicating that high integration levels are achievable across governance structures. All 45 sampled institutions are listed in the Online Resource 1 with their institution type, geographic region, and dimensional scores. Geographic analysis revealed meaningful regional disparities in AI integration. Among regions with larger sample sizes, the South-West zone showed the highest integration (M = 5.67, SD = 1.61, n = 16), followed by South-South (M = 5.19, SD = 1.68, n = 7). The South-East region showed the lowest integration among regions with substantial samples (M = 3.50, SD = 0.66, n = 8). North-West universities showed the greatest within-region variability (SD = 2.07), reflecting heterogeneous integration patterns in that region. 5.3 Dimensional Analysis Analysis across the six AI integration dimensions revealed significant variation in institutional capabilities (Table 3 and Fig. 4 ). Curriculum Integration emerged as the strongest dimension (M = 5.73, SD = 1.68), indicating that Nigerian universities have made substantial progress incorporating AI content into academic programs. Research Initiatives demonstrated comparable strength (M = 5.57, SD = 1.83), reflecting active engagement with AI-related research activities. Policy Frameworks represented the weakest dimension (M = 4.09, SD = 2.00), with only 31% of institutions demonstrating formal AI strategies or governance structures. This finding indicates a critical gap between operational AI activities and strategic institutional planning. International Collaborations (M = 4.20, SD = 2.28) and Industry Partnerships (M = 4.68, SD = 2.20) also showed significant room for improvement. Table 3 Descriptive Statistics by AI Integration Dimension One-way ANOVA (Table 4) revealed significant between-group differences for International Collaborations (F(2,42) = 4.12, p = 0.023, η²=0.16), indicating that institution type meaningfully influences international partnership development. Federal universities demonstrated the strongest international collaboration scores (M = 5.07), followed by Private universities (M = 4.40) and State universities (M = 3.13). This medium effect size suggests that governance structure has practical significance for international engagement capacity. Curriculum Integration showed no significant between-group differences (F(2,42) = 2.14, p = 0.130, η²=0.09), indicating that AI curriculum development occurs across all institution types regardless of governance structure. Private universities demonstrated particular strength in this dimension (M = 5.87), the highest dimensional score among all institution types, reflecting their organizational agility in responding to market demands for AI-related programs. Table 4 ANOVA Results for AI Integration Dimensions by Institution Type *Note. p < .05 5.4 Correlation Analysis Pearson correlation analysis revealed significant positive relationships among AI integration dimensions, suggesting a hierarchical structure of institutional capabilities. The strongest relationships were found within internal institutional functions: AI Infrastructure and Curriculum Integration ( r = 0.80, p < 0.001), and between Research Initiatives and both AI Infrastructure ( r = 0.79, p < 0.001) and Curriculum Integration ( r = 0.79, p < 0.001). These strong correlations indicate that institutions investing in infrastructure tend to develop corresponding curricular and research capabilities in a mutually reinforcing pattern. From the perspective of external networks, the correlation between Industry Partnerships and International Collaborations ( r = 0.74, p < 0.001) was notable. While numerically lower than the internal capability correlations, it represents the strongest relationship involving external network connections. This finding suggests that globally connected institutions simultaneously develop robust local industry networks, supporting network theory predictions that connections facilitate additional connections. Policy Frameworks demonstrated the weakest correlations with other dimensions, particularly with Curriculum Integration ( r = 0.43, p < 0.01). This relatively weak relationship indicates a disconnect between strategic planning and operational activities, with many institutions pursuing AI curriculum development without formal institutional AI strategies to guide these efforts. Table 5 Correlation Matrix of AI Integration Dimensions Note **p < .01, ***p < .001 (Bonferroni corrected) 5.5 Predictors of AI Integration Addressing RQ2 (Table 6), regarding predictive factors, multiple regression analysis identified significant predictors of overall AI integration. The full model explained 42% of variance in integration scores (R²=0.42, Adjusted R²=0.36, F(4,40) = 7.24, p < 0.001). In social science research, R² values between 0.10 and 0.50 are generally considered acceptable given the complexity of human and organizational behavior (Ozili, 2023). The obtained R² of 0.42 falls within the upper range of this benchmark, indicating that the model captures a meaningful portion of variance while acknowledging that additional unmeasured factors also influence AI integration. Table 6 Multiple Regression Results: Predictors of Overall AI Integration Note R² = 0.42, Adjusted R² = 0.36, F(4,40) = 7.24, p < 0.001. *p < 0.05 Institution Age emerged as the strongest predictor (β = 0.34, p = 0.021), with older universities demonstrating higher integration levels. Each decade of institutional history was associated with a 0.34 standard deviation increase in AI integration scores, strongly supporting resource-based view predictions about organizational learning and capability accumulation over time. The Geographic Location of the institution also proved significant, with South-West region universities showing higher integration scores (β = 0.28, p = 0.045) compared to other regions. This pattern reflects historical concentration of educational resources, economic activity, and international connections in the Lagos-Ibadan corridor. Notably, Institution Type was not a significant predictor when controlling for other variables (β = 0.12, p = 0.387), indicating that observed differences between Federal, State, and Private universities are largely mediated by factors such as institutional age, geographic location, and resource availability rather than governance structure per se. This finding has important theoretical and practical implications discussed in the following section. 6 Discussion The moderate overall AI integration (M = 4.79) observed across Nigerian universities reflects the complex interplay of institutional, resource, and network factors predicted by our conceptual framework. This finding indicates substantial room for improvement while demonstrating that Nigerian universities have initiated meaningful AI integration efforts. The following discussion interprets these findings through the lens of the three theoretical perspectives guiding this study. 6.1 Integration Patterns by Institution Type Federal universities demonstrated the highest mean integration scores (M = 5.25), consistent with institutional theory predictions about advantages conferred by established legitimacy and stable resource bases (DiMaggio & Powell, 1983 ; Scott, 2013 ). These institutions, with mean establishment years dating to 1979, have accumulated organizational capabilities over decades that facilitate technology adoption. Consistent government funding through mechanisms such as the Tertiary Education Trust Fund (TETFund) provides the financial stability necessary for sustained infrastructure investment (Lawal, 2019 ). The normative pressures associated with their role as national flagship institutions further incentivize adoption of visible technologies like AI to maintain competitive positioning (Alshumrani et al., 2022 ). Private universities illustrated notable strength in curriculum integration (M = 5.87), the highest dimensional score among all institution types. This finding supports institutional theory predictions about mimetic pressures in competitive enrollment markets (Hui, 2022 ; Saka et al., 2024 ). Private universities depend heavily on tuition revenue and must signal innovation to attract students (Bamiro, 2012 ). The organizational agility afforded by more flexible governance structures enables rapid program development in response to market demands (Ediriweera et al., 2025 ; Mijan et al., 2025 ). This pattern suggests that resource constraints do not preclude innovation when institutions possess organizational flexibility and face competitive pressures that incentivize adoption. The implication is significant: curriculum innovation can proceed even under resource constraints when institutional structures permit organizational agility. State universities showed the lowest overall integration (M = 4.36), reflecting resource constraints predicted by resource-based view theory (Barney, 1991 ). These institutions face funding variability tied to state-level economic conditions, limiting capacity for capital-intensive AI investments (Jesam et al., 2025 ; Umar et al., 2025 ). However, this finding should not be interpreted as indicating state universities lack capacity for AI development. Rather, their resource limitations may channel institutional energy toward areas requiring less capital investment, such as strategic planning and policy development. Their direct connections to state governments create unique opportunities for partnerships focused on regional development priorities. 6.2 The Significance of Non-Significant Institutional Type Effects The finding that institution type was not significant when controlling for age and location (β = 0.12, p = 0.387) is theoretically important and warrants careful interpretation. While descriptive statistics showed differences between Federal, State, and Private universities, these differences are largely explained by other factors. Governance structure alone does not determine integration outcomes; accumulated resources and network positions matter more. This challenges assumptions that simply identifying institution types provides sufficient basis for partnership design. This finding has practical implications for international partners. Rather than seeking out partnerships based on governance category, partners should assess specific institutional profiles including establishment history, geographic context, existing network connections, and dimensional strengths. A newer Federal university may have less integration capacity than an established Private university, despite governance-based assumptions to the contrary. 6.3 Network Effects and Partnership Implications The strong correlation between international collaborations and industry partnerships ( r = 0.74, p < 0.001) represents a key finding with both theoretical and practical significance. Network theory predicts that inter-organizational connections facilitate knowledge transfer and resource access (Brunetti et al., 2020 ; Granovetter, 1973 ). The observed correlation suggests that universities developing strong international networks are simultaneously building robust local industry relationships. Several mechanisms may explain this pattern. International partners often facilitate local industry connections through their own networks. Globally engaged faculty attract industry attention through enhanced visibility and demonstrated competence. The capabilities developed through international collaboration, including research skills, project management experience, and exposure to global standards, enhance attractiveness to industry partners. This finding suggests that international and industry partnerships should be pursued as complementary rather than competing strategies. Effective international partnerships may serve as pathways to industry engagement, while industry connections can enhance attractiveness to international collaborators. In light of recent disruptions to international funding mechanisms, the strong correlation between international collaborations and industry partnerships also presents a strategic opportunity for sustainability. International collaborations may be most resilient when anchored in local industry relevance, creating triangular partnerships where local industry partners provide context and sustainability while international partners contribute expertise and global networks. 6.4 The Policy Framework Gap Policy Frameworks being the weakest dimension (M = 4.09) indicates a critical gap between operational AI activities and strategic institutional planning. Many universities are adopting AI technologies reactively rather than through deliberate institutional strategy. The relatively weak correlation between Policy Frameworks and Curriculum Integration ( r = 0.43) underscores this disconnect; institutions are developing AI curricula without formal policies to guide these efforts. This gap has implications for partnership sustainability. Effective international collaborations typically require clear institutional policies and governance frameworks to ensure alignment of objectives, allocation of resources, and continuity beyond individual relationships. The African Union’s Continental Artificial Intelligence Strategy (2024) provides a continental framework that institutions could adapt to develop institution-specific policies addressing ethical AI deployment, faculty capacity building, and alignment with national development priorities. The regional concentration of high-performing universities in the South-West (β = 0.28, p = 0.045) reflects historical patterns of educational resource distribution and economic development in the Lagos-Ibadan corridor. This raises equity concerns, as universities in northern and eastern regions may face structural disadvantages in AI development. Investment in AI infrastructure and capacity building across regions could help address these disparities. Regional consortium models that enable resource sharing and knowledge transfer between well-resourced and developing institutions offer one mechanism for addressing these disparities. 7 Implications and Conclusion 7.1 Implications for Policy The identification of institution age and geographic location as significant predictors of AI integration, while institution type was not significant when controlling for other factors, suggests that policy interventions should focus on mechanisms that build institutional capacity regardless of governance structure. Newer universities and those in underserved regions may benefit from targeted support programs that accelerate capability development. Such programs could include dedicated infrastructure grants for computing facilities and AI laboratories, faculty development initiatives providing training in AI pedagogy and research methods, mentorship arrangements pairing emerging institutions with established universities, and access to shared cloud computing resources that reduce individual infrastructure burdens. The National Information Technology Development Agency (NITDA) and TETFund could coordinate such initiatives, ensuring resources reach institutions with the greatest development potential (Federal Ministry of Communications and Digital Economy, 2020 ). Such coordinated efforts would help address regional disparities and accelerate AI capacity building across the Nigerian higher education sector. The weakness in policy frameworks across all institution types (M = 4.09) indicates a need for sector-wide attention to strategic planning and governance. Nigerian universities would benefit from developing formal AI strategies that articulate integration goals, resource allocation priorities, and partnership approaches. For faculty, such strategies should address professional development pathways, research support mechanisms, and guidelines for AI use in teaching and assessment. For students, strategies should articulate AI literacy expectations across programs, experiential learning opportunities, and ethical frameworks for AI use in academic work. The National Universities Commission could facilitate this development through guidance documents, benchmarking exercises, and incentive mechanisms that encourage strategic planning. The strong correlation between international collaborations and industry partnerships ( r = 0.74) suggests that policies promoting one form of partnership may generate spillover benefits for the other (Brunetti et al., 2020 ; Granovetter, 1973 ). Government programs supporting international engagement, such as faculty mobility schemes and research exchange programs, may simultaneously strengthen industry relationships. Similarly, policies encouraging university-industry collaboration may enhance international visibility and attractiveness to foreign partners. This underscores the value of integrated partnership policies that recognize the interconnected nature of institutional networks rather than treating international and industry engagement as separate domains. 7.2 Implications for International Partners The differentiated institutional profiles suggest that effective international collaboration strategies must be tailored to institutional characteristics and capabilities rather than governance structure alone. Federal universities’ combination of strong research capacity (M = 6.3 for Research Initiatives) and international collaboration experience (M = 5.07 for International Collaborations) positions them for comprehensive research partnerships emphasizing faculty exchange and joint research projects. Their established infrastructure, faculty expertise, and administrative capacity support sustained research collaboration. Furthermore, private universities’ excellence in curriculum integration (M = 5.87) and organizational agility positions them for innovation-focused collaborations. Their industry orientation and entrepreneurial culture align with partnership models emphasizing practical application and technology transfer. Shorter-duration, project-based partnerships that leverage private university flexibility may prove more effective than long-term institutional arrangements requiring extensive bureaucratic navigation. State universities, despite lower overall scores, offer unique opportunities through their direct connections to state governments. These connections create pathways for partnerships focused on public sector objectives and regional development priorities. Collaborations aligned with state development plans and addressing regional challenges may find particularly receptive partners among state universities. 7.3 Limitations and Future Research Several limitations should be acknowledged. First, reliance on publicly available information may underestimate AI integration at institutions with limited web presence or outdated website content. Second, the cross-sectional design captures AI integration at a single point in time, potentially missing dynamic changes in institutional capacity given the rapid pace of AI development. Longitudinal research tracking integration over time would provide insights into adoption trajectories and factors that accelerate or impede progress. Third, while the study collected institution-wide data, department-level analysis prioritized computing and engineering fields as primary indicators; AI integration in non-technical fields may follow different patterns. Fourth, the focus on Nigerian universities limits generalizability to other African countries with different higher education systems and development contexts. Comparative studies across multiple African countries would enhance understanding of how national contexts influence institutional AI adoption patterns. Future research should examine experiences of faculty, administrators, and students through qualitative methods to illuminate implementation challenges and success factors. Research on partnership outcomes and effectiveness would inform development of more successful collaboration models. Studies examining how Nigerian universities navigate tensions between global AI trends and local cultural contexts could contribute to more culturally sensitive implementation approaches. 7.4 Conclusion This study established that Nigerian universities have initiated meaningful AI integration efforts, yet significant gaps remain, particularly in policy frameworks and international collaborations. More importantly, the findings challenge assumptions that governance structure alone determines integration capacity. Institutional age, geographic location, and network connections matter as much, if not more, than whether a university is Federal, State, or Private. This insight reframes how international partners should approach collaboration: not by selecting institution types, but by understanding specific institutional profiles and designing partnerships accordingly. Institutional diversity, the very characteristic that might appear to complicate partnership design, represents a strategic asset when collaborations leverage complementary strengths across institution types. Federal universities can contribute research infrastructure and international networks, private universities can offer curriculum innovation and industry connections, and state universities can provide pathways to regional development priorities and public sector engagement. Effective international educational partnerships must move beyond traditional donor-recipient models toward collaboration that builds genuine local capacity. The findings establish essential baseline data for evidence-informed partnership development while contributing to broader understanding of technology adoption in developing country higher education contexts. As AI technologies continue reshaping higher education globally, ensuring that universities across all regions can meaningfully participate in this transformation is both an educational imperative and a matter of global equity. References Adamakis M, Rachiotis T (2025) Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration. Encyclopedia 5(4):180 Adejo LO (2025) Evaluating the Effectiveness and Challenges of Artificial Intelligence Tools in Chemistry Instruction at the Federal University of Education, Zaria. Fac Nat Appl Sci J Math Sci Educ 6(4):16–35 African Union (2024) Continental Artificial Intelligence Strategy. African Union. https://au.int/en/documents/20240809/continental-artificial-intelligence-strategy Ahmed A-B, King BD, Hiran KK, Dadhich M, Malcalm E (2025) Half a decade of artificial intelligence in education in africa: Trends, opportunities, challenges and future directions. J Eng Educ Transformations, 81–100 Alshahrani ND, Coolen-Maturi T, Coolen FP (2025) On Statistical Reproducibility of Normality and Equality of Variances Tests. J Stat Theory Pract 19(4):81 Alshumrani S, Baird K, Munir R (2022) Management innovation: The influence of institutional pressures and the impact on competitive advantage. Int J Manpow 43(5):1204–1220 Al-Zahrani AM, Alasmari TM (2024) Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications. Humanit Social Sci Commun 11(1):1–12 Amrozi Y, Ekowati D, Putranto H, Zikky M, Zulkarnain M (2023) Adoption of Information Technology as a Mediator Between Institutional Pressure and Change Performance. J Namibian Studies: History Politics Cult 34:788–808 Arthur CS (2025) A preliminary case study: Challenges, approaches, and pressures in the policy implementation of iCGPA. J Praxis High Educ 7(1):98–122 Astuti HM, Ayinde LA (2025) Uneven progress: Analyzing the factors behind digital technology adoption rates in Sub-Saharan Africa (SSA). Data Policy 7:e23. https://doi.org/10.1017/dap.2024.89 Asunka S (2008) Online learning in higher education in Sub-Saharan Africa: Ghanaian University students’ experiences and perceptions. Int Rev Res Open Distrib Learn 9(3):1–23 Ayanwale M, Adelana O, Molefi R, Adeeko O (2024) Examining artificial intelligence literacy among pre-service teachers for future classrooms. Elsevier BV. https://doi.org/10.1016/j.caeo.2024.100179 Azubuike OB, Adegboye O, Quadri H (2021) Who gets to learn in a pandemic? Exploring the digital divide in remote learning during the COVID-19 pandemic in Nigeria. Int J Educational Res Open 2:100022 Bali B, Garba EJ, Ahmadu AS, Takwate KT, Malgwi YM (2024) Analysis of emerging trends in artificial intelligence for education in Nigeria. Discover Artif Intell 4(1):110 Bamiro OA (2012) Sustainable financing of higher education in Nigeria: Funding models. Two-Day Consultative Policy Dialogue. Theme: The Future and Relevance of Nigerian Universities and Other Tertiary Institutions. Towards Higher Education Transformation Barakabitze AA, William-Andey Lazaro A, Ainea N, Mkwizu MH, Maziku H, Matofali AX, Iddi A, Sanga C (2019) Transforming African education systems in science, technology, engineering, and mathematics (STEM) using ICTs: Challenges and opportunities. Educ Res Int 2019(1):6946809 Barnes E, Hutson J (2024) Strategic Integration of AI in Higher Education and Industry: The AI8-Point Model. Adv Social Sci Manage 2(6):39–52. https://doi.org/10.63002/assm.26.520 Barney J (1991) Firm resources and sustained competitive advantage. J Manag 17(1):99–120 Benavides L, Tamayo Arias J, Arango Serna M, Bedoya B, J., Burgos D (2020) Digital Transformation in Higher Education Institutions: A Systematic Literature. Rev Sens 20(11):3291. https://doi.org/10.3390/s20113291 Bradley M (2008) On the agenda: North–South research partnerships and agenda-setting processes. Dev Pract 18(6):673–685. https://doi.org/10.1080/09614520802386314 Brunetti F, Matt DT, Bonfanti A, De Longhi A, Pedrini G, Orzes G (2020) Digital transformation challenges: Strategies emerging from a multi-stakeholder approach. TQM J 32(4):697–724. https://doi.org/10.1108/TQM-12-2019-0309 Cardona Mejía LM, del Val P, M., Dasí Coscollar M (2020) del S. The institutional isomorphism in the context of organizational changes in higher education institutions. International Journal of Research in Education and Science, 6(1), 61–73 Carnegie Mellon University Africa (2025) AI and robotics research. https://www.africa.engineering.cmu.edu/research/artificial-intelligence.html Chipembele M, Ngandwe E, Monde MW, Ndalame EM (2025) Adoption of Artificial Intelligence in Academic Libraries in African Universities: A Scoping Review. IJSAT-International J Sci Technol, 16(3) Chumakov E (2025) History and definitions of AI. Eur Psychiatry 68(S1):S61–S61. https://doi.org/10.1192/j.eurpsy.2025.238 Cochran WG (1977) Sampling techniques, 3 edn. Wiley Cohen J (2013) Statistical power analysis for the behavioral sciences. routledge Crompton H, Burke D (2023) Artificial intelligence in higher education: The state of the field. Int J Educational Technol High Educ 20(1):22 DiMaggio PJ, Powell WW (1983) The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. Am Sociol Rev 48(2):147–160 Ediriweera C, Pramudika H, Fernando M (2025) The Influence of AI-Driven Marketing Analytics on Strategic Decision-Making in Private Educational Institutes in Sri Lanka: Moderator Role of Organisational Culture. Asian J Mark Manage, 4(2) Elo S, Kyngäs H (2008) The qualitative content analysis process. J Adv Nurs 62(1):107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x Erdmann A, Toro-Dupouy L (2025) The influence of the institutional environment on AI adoption in universities: Identifying value drivers and necessary conditions. European Journal of Innovation Management Eze P, Alabi A, Osunbunmi I (2025) AI-driven career guidance: Comparing Nigerian undergraduate and postgraduate perceptions. Discover Educ 4(1):543. https://doi.org/10.1007/s44217-025-00900-0 Fauzi HA, Yusoff H, Jasni NS, THE ROLE OF INFORMATION TECHNOLOGY (IT) FOR FINANCIAL SUSTAINABILITY IN HIGHER EDUCATION INSTITUTIONS (HEIS) (2025) Int J Mod Educ 7(24):1125–1138. https://doi.org/10.35631/IJMOE.724079 Federal Ministry of Communications and Digital Economy (2020) National Digital Economy Policy and Strategy (2020–2030). https://nitda.gov.ng/wp-content/uploads/2020/06/National-Digital-Economy-Policy-and-Strategy.pdf Feng J, Yu B, Tan WH, Dai Z, Li Z (2025) Key factors influencing educational technology adoption in higher education: A systematic review. PLOS Digit Health, 4(4), e0000764 George B, Wooden O (2023) Managing the Strategic Transformation of Higher Education through Artificial Intelligence. Administrative Sci 13(9):196. https://doi.org/10.3390/admsci13090196 Granovetter MS (1973) The strength of weak ties. Am J Sociol 78(6):1360–1380 Gronski R, Pigg K (2000) University and community collaboration: Experiential learning in human services. Am Behav Sci 43(5):781–792 Hamouda S, Marshall L, Sanders K, Tshukudu E, Adelakun-Adeyemo O, Becker BA, Dodoo ER, Korsah GA, Luvhengo S, Ola O, Parkinson J, Sanusi IT (2025) Computing Education in African Countries: A Literature Review and Contextualised Learning Materials. 2024 Working Group Reports on Innovation and Technology in Computer Science Education, 1–33. https://doi.org/10.1145/3689187.3709606 Hamzat AA, Ansah SD (2025) Artificial intelligence awareness and readiness among undergraduate students in Kwara State universities Nigeria. J Educational Manage Instruction (JEMIN) 5(2):335–349. https://doi.org/10.22515/jemin.v5i2.11988 Ho L, Barnhart J, Trager R, Bengio Y, Brundage M, Carnegie A, Chowdhury R, Dafoe A, Hadfield G, Levi M, Snidal D (2023) International Institutions for Advanced AI (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2307.04699 Hui TC (2022) Market Isomorphism and Mobile Commerce Adoption in The Omnichannel: A Systematic Literature Review. Asian J Bus Res, 12(2) Ibrahim AB (2024) Assessing the knowledge and perception of artificial intelligence for teaching and research among lecturers in the faculties of arts in Nigeria. J Global Res Educ Social Sci 18(2):25–33 Imizuokena I-AJ, Ikechuku AE (2025) The Exploring AI capacity building programs in Nigerian higher education institutions: A study of curriculum design, faculty competence, and student engagement. https://doi.org/10.5281/ZENODO.15356893 İpek ZH, Gözüm AİC, Papadakis S, Kallogiannakis M (2023) Educational Applications of the ChatGPT AI System: A Systematic Review Research. Educational Process Int J 12(3). https://doi.org/10.22521/edupij.2023.123.2 Jesam M, Onwubuya UO, Akpo AC (2025) Challenges in the Adoption of Artificial Intelligence for Effective Delivery of Business Education Course Content at the University of Cross River State. Mediterranean J Social Sci 16(5):135. https://doi.org/10.36941/mjss-2025-0055 Jin Y, Yan L, Echeverria V, Gašević D, Martinez-Maldonado R (2025) Generative AI in higher education: A global perspective of institutional adoption policies and guidelines, vol 8. Artificial Intelligence, Computers and Education, p 100348 Joel A-D, Victor A, I., Obinna E (2025) Leveraging artificial intelligence (AI) for educational development in Nigeria: Examining the challenges and opportunities. J Social Sci, 2(2) Jowi J (2024) Recent developments in higher education in Africa: Partnerships for knowledge transformations. J Int Cooperation Educ 26(1):34–48 Krippendorff K (2018) Content analysis: An introduction to its methodology. Sage Landis J, Koch G (2021) The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977). Sci Rep, 11, 10459 Lawal I (2019), March 22 In search of autonomy for government-owned universities. https://guardian.ng/features/in-search-of-autonomy-for-government-owned-universities/ Lin H, Chen Q (2024) Artificial intelligence (AI) -integrated educational applications and college students’ creativity and academic emotions: Students and teachers’ perceptions and attitudes. BMC Psychol 12(1):487. https://doi.org/10.1186/s40359-024-01979-0 Liverpool L, Marut M, Ndam J, Oti D (2009) Towards a model for e-learning in Nigerian HEIS: lessons from the University of Jos ICT Maths initiative. Proceedings of the ICT Obafemi Awolowo University Ile-Ife Lohr SL (2021) Sampling: Design and analysis. Chapman and Hall/CRC Mailani D, Hulu MZT, Simamora MR, Kesuma SA (2024) Resource-Based View Theory to achieve a sustainable competitive advantage of the firm: Systematic literature review. Int J Entrepreneurship Sustain Stud 4(1):1 Maimela C, Mbonde P (2025) Artificial intelligence in South African universities: Curriculum transformation and decolonisation—aid or obstacle? Front Sociol 10:1543471. https://doi.org/10.3389/fsoc.2025.1543471 Mateko FM, Dowelani M, Sinamano R (2025) Digital Inequality and Transformation in South African Higher Education During COVID-19: A Comparative Analysis of Historically Disadvantaged and Historically Advantaged Universities. https://doi.org/10.1057/s41307-025-00416-0 . Higher Education Policy Maxwell SE, Delaney HD, Kelley K (2017) Designing experiments and analyzing data: A model comparison perspective. Routledge Microsoft (2024) 2024 Work Trend Index Annual Report: AI at work is here. Now comes the hard part. https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part Microsoft (2025) 2025 AI in Education: A Microsoft Special Report. https://cdn-dynmedia-1.microsoft.com/is/content/microsoftcorp/microsoft/bade/documents/products-and-services/en-us/education/2025-Microsoft-AI-in-Education-Report.pdf Mijan A-A, Hasan MR, Hasan M (2025) AI and academia: Navigating the adoption of artificial intelligence in universities. Int J Technol Educ Sci 9(1):54–65 Moy M, Feldstein A (2024) Generative AI: A University’s Approach to Understanding Opportunities and Challenges. Proceedings of the International Conference on Networked Learning, 14. https://doi.org/10.54337/nlc.v14i1.8091 Mulford D (2025) AI in Higher Education: A Meta Summary of Recent Surveys of Students and Faculty. Campbell University Academic Technology Services. https://sites.campbell.edu/academictechnology/2025/03/06/ai-in-higher-education-a-summary-of-recent-surveys-of-students-and-faculty/ My School Insight (2025), July 26 Differences Between Federal, State, and Private Universities in Nigeria: A Comprehensive Guide. https://myschoolinsight.com/differences-between-federal-state-and-private-universities-in-nigeria-a-comprehensive-guide/ Nalepa GJ, Stefanowski J (2020) Artificial Intelligence Research Community and Associations in Poland. Found Comput Decis Sci 45(3):159–177. https://doi.org/10.2478/fcds-2020-0009 National Universities Commission (2025) List of Nigerian Universities. https://www.nuc.edu.ng/ Nwachukwu ST, Ugwu CM, Wogu JO (2021) Digital learning in post covid-19 era: Policy options and prospects for quality education in Nigeria. Library Philosophy and Practice, pp 1–18 Obamba M, Mwema J (2009) Symmetry and Asymmetry: New Contours, Paradigms, and Politics in African Academic Partnerships. High Educ Policy 22(3):349–371 Ocen S, Elasu J, Aarakit SM, Olupot C (2025) Artificial intelligence in higher education institutions: Review of innovations, opportunities and challenges. Front Educ 10:1530247. https://doi.org/10.3389/feduc.2025.1530247 Oguine OC, Oguine KJ, Bisallah HI (2022) Big Data and Analytics Implementation in Tertiary Institutions to Predict Students Performance in Nigeria. arXiv Preprint arXiv :220714677 Olanrewaju GS, Omotosho AY, Falajiki OC, Osama G (2021) E-learning in a pandemic era: Exploring the digital gaps and needs of rural secondary schools and remote communities across 6 Nigerian states. Int J Innov Educ Res 9:696–710 Onyancha OB, Maluleka JR (2011) Knowledge production through collaborative research in sub-Saharan Africa: How much do countries contribute to each other’s knowledge output and citation impact? Scientometrics, 87(2), 315–336. https://doi.org/10.1007/s11192-010-0330-5 Oudshoorn MJ, Bradshaw K, Abandoh-Sam JA, Adelakun-Adeyemo O, Bainomugisha E, Binamungu LP, Khedr ME, Liebenberg J, Mwaura J, Nachuma C (2025) Future Ready, Globally Relevant: Aligning Africa’s Tertiary Computer Science Education Objectives with Global Standards. Proceedings of the ACM Global on Computing Education Conference 2025 Vol 2, 373–375. https://doi.org/10.1145/3736251.3749527 ÖZTÜRK ERKOÇAK İ (2020) Coercive isomorphism in higher education: Direct pressures from the state to the Turkish universities Pedro F, Subosa M, Rivas A, Valverde P (2019) Artificial intelligence in education: Challenges and opportunities for sustainable development Rogers E (2003) Diffusion of Innovations 5th Rössler P (2013) Comparative content analysis. The handbook of comparative communication research. Routledge, pp 459–468 Ruano-Borbalan J-C (2025) The transformative impact of artificial intelligence on higher education: A critical reflection on current trends and futures directions. Int J Chin Educ 14(1):2212585X251319364 Sahni J, Darda P, Gaur SS (2025) Integration of principles for responsible management education principles (PRME) in Saudi Arabian universities: A thematic analysis of claims on university websites. Society and Business Review Saka AB, Chan DW, Ajayi SO (2024) Institutional isomorphism and adoption of building information modelling (BIM) in small and medium-sized enterprises (SMEs) of the Nigerian Construction Industry. Eng Constr Architectural Manage 31(1):179–199 Scott WR (2013) Institutions and organizations: Ideas, interests, and identities. Sage Shapiro SS, Wilk MB (1965) An analysis of variance test for normality (complete samples). Biometrika 52(3–4):591–611 Siyanbola WO, Egbetokun AA, Oluseyi I, Olamade OO, Aderemi HO, Sanni M (2012) Indigenous technologies and innovation in Nigeria. Opportunities for SMEs Sun W, Chen K, Mei J (2024) Integrating the resource-based view and dynamic capabilities: A comprehensive framework for sustaining competitive advantage in dynamic markets. EPRA Int J Economic Bus Rev 12(9):1–8 Think Global Health (2025) Life after USAID: Africa’s development, education, and health care. https://www.thinkglobalhealth.org/article/life-after-usaid-africas-development-education-and-health-care Tingling P, Parent M (2002) Mimetic isomorphism and technologyevaluation: Does imitation transcendjudgment? J Association Inform Syst 3(1):5 Tsakeni M, Nwafor SC (2025) Assessing Artificial Intelligence Literacy Among Pre-Service Science Teachers: Challenges and Strategies for Improvement. Int J Learn Teach Educational Res 24(11):356–375. https://doi.org/10.26803/ijlter.24.11.17 ul Haq F, Asim M, Suki NM, Zakaria N, Hussain S (2025) AI Adoption and Educational Effectiveness in Emerging Higher Education Institutions: The Moderating Role of Digital Literacy and Institutional Support. J Inform Knowl Manage, 2550090 Umar M, Lee S, Gidado SL, Bakari GB, Adamu Ibrahim A (2025) Assessing Lecturers’ Knowledge and Utilisation of Artificial Intelligence for Teaching and Research in Adamawa State Tertiary Institutions. Int J Adv Eng Manage 7(7):969–976. https://doi.org/10.35629/5252-0707969976 University of Michigan (2024) U-M launches AI training program for African scholars with $ 6M initiative. https://midas.umich.edu/midas-expands-its-ai-in-science-and-engineering-training-effort-to-africa/ University World News (2024) Views on Africanising tech integration in higher education. https://www.universityworldnews.com/post.php?story=20231219172805108 US Department of Education (2023) Artificial intelligence and the future of teaching and learning. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf Van Alstyne M (1997) The State of Network Organization: A Survey in Three Frameworks. J Organizational Comput Electron Commer 7(2–3):83–151. https://doi.org/10.1080/10919392.1997.9681069 Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Q, 425–478 Walter Y (2024) Embracing the future of Artificial Intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. Int J Educational Technol High Educ 21(1):15 World Bank (2025) Africa’s Higher Education Centers of Excellence: Celebrating a decade of transformative journey in revolutionizing Africa’s contribution to global research and innovation. https://www.worldbank.org/en/news/press-release/2025/04/07/africa-higher-education-centers-of-excellence-africa-contribution-to-global-research-and-innovation Yeager B, Yalim J, Knuth S, Romanella A, Reidy C, Nickell B (2024) Identifying high-performance computing needs at member institutions in a regional consortium. Practice and Experience in Advanced Research Computing 2024: Human Powered Computing. 1–5. https://doi.org/10.1145/3626203.3670602 Yi Z, Chen Y-H, Yin Y, Cheng K, Wang Y, Nguyen D, Pham T, Kim E (2022) Brief research report: A comparison of robust tests for homogeneity of variance in factorial ANOVA. J Experimental Educ 90(2):505–520 Zawacki-Richter O, Marín VI, Bond M, Gouverneur F (2019a) Systematic review of research on artificial intelligence applications in higher education–where are the educators? Int J Educational Technol High Educ 16(1):1–27 Zawacki-Richter O, Marín VI, Bond M, Gouverneur F (2019b) Systematic review of research on artificial intelligence applications in higher education–where are the educators? Int J Educational Technol High Educ 16(1):1–27 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8816750","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587497524,"identity":"cf42f28a-137f-4e67-b780-79a82f392710","order_by":0,"name":"Daniel I. Adeniranye","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-1907-9463","institution":"Florida International University","correspondingAuthor":true,"prefix":"","firstName":"Daniel","middleName":"I.","lastName":"Adeniranye","suffix":""},{"id":587497525,"identity":"4fbf5bdd-c754-4a80-bb7b-38e91b5ec51d","order_by":1,"name":"Stephanie J. Lunn","email":"","orcid":"https://orcid.org/0000-0003-3840-1822","institution":"Florida International University","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"J.","lastName":"Lunn","suffix":""},{"id":587497526,"identity":"34d61f40-1a35-4272-bad7-40e3a027370e","order_by":2,"name":"Olatunde Mosobalaje","email":"","orcid":"https://orcid.org/0000-0002-0989-3066","institution":"Covenant University","correspondingAuthor":false,"prefix":"","firstName":"Olatunde","middleName":"","lastName":"Mosobalaje","suffix":""},{"id":587497527,"identity":"f350347a-6ed7-4b4b-b1e5-f30ba60d2765","order_by":3,"name":"Bruk Berhane","email":"","orcid":"https://orcid.org/0000-0001-8164-1835","institution":"Florida International University","correspondingAuthor":false,"prefix":"","firstName":"Bruk","middleName":"","lastName":"Berhane","suffix":""},{"id":587497528,"identity":"c2ffca4a-36cf-45fc-92ce-6f06ce470eb9","order_by":4,"name":"Daniel Adeniyi","email":"","orcid":"","institution":"Ivy Tech Community College","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Adeniyi","suffix":""}],"badges":[],"createdAt":"2026-02-07 16:02:59","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8816750/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8816750/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102275142,"identity":"2ce27b8d-6a1c-4822-97af-7f119fbc987b","added_by":"auto","created_at":"2026-02-10 05:37:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":634686,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework for AI Integration in Nigerian Universities\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8816750/v1/c7733ae246fcc153b0081f71.png"},{"id":102297870,"identity":"a0a3d3af-7771-4608-bdb6-72e24b69190e","added_by":"auto","created_at":"2026-02-10 10:29:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":267096,"visible":true,"origin":"","legend":"\u003cp\u003eNigeria’s geopolitical zones and sampled institutions\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8816750/v1/1c59e8466541a9b84e14e479.png"},{"id":102297650,"identity":"eaad265f-675e-46b4-8a04-21da8db969a0","added_by":"auto","created_at":"2026-02-10 10:28:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54321,"visible":true,"origin":"","legend":"\u003cp\u003eOverall AI Integration Scores by Institution Type\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8816750/v1/e18e8ef68dec9b8360b3fc65.png"},{"id":102297926,"identity":"515eaafe-6d45-4a31-97fd-5a56f1a47378","added_by":"auto","created_at":"2026-02-10 10:29:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71957,"visible":true,"origin":"","legend":"\u003cp\u003eAI Integration by Dimension and Institution Type\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8816750/v1/f4446229619a51bafd2e340b.png"},{"id":102300559,"identity":"b9771526-a930-415a-93b2-6f41179180b6","added_by":"auto","created_at":"2026-02-10 11:15:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2270891,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8816750/v1/53a3f26a-7e35-48ad-844e-37f1b0790a27.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eBuilding Sustainable International Partnerships: Evidence-Informed Strategies Based on a Comparative Analysis of Nigerian Universities' AI Integration and Readiness\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe integration of artificial intelligence (AI) technologies in higher education represents one of the most significant transformational forces in contemporary academia (Zawacki-Richter et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e). AI encompasses a broad spectrum of technologies including machine learning algorithms, natural language processing systems such as large language models (LLMs), e.g., OpenAI\u0026rsquo;s ChatGPT, intelligent tutoring systems, robotics, and data analytics platforms. These technologies can be leveraged by faculty for research and instruction, by administrators for institutional operations, and by students for learning support (Adamakis \u0026amp; Rachiotis, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The emergence of generative AI tools since 2022 has particularly accelerated attention to how tertiary institutions prepare graduates for an AI-transformed workforce, with recent analyses indicating that AI competencies are increasingly essential for employability across sectors (Microsoft, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInternational partnerships progressively aim to build AI capacity in developing regions through knowledge exchange, joint research initiatives, curriculum development support, and infrastructure sharing. For instance, the University of Michigan\u0026rsquo;s \u003cspan\u003e$\u003c/span\u003e6\u0026nbsp;million program centered on AI in Science African Faculty Fellows builds research capacity through extended faculty exchanges lasting up to 24 months (University of Michigan, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, Carnegie Mellon University has established an Africa campus located in Rwanda that offers graduate programs explicitly focused on AI for social good applications (Carnegie Mellon University Africa, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These initiatives, alongside partnerships involving European, Asian, and other African institutions, suggest movement toward collaborative approaches emphasizing mutual benefit and local ownership, though the extent to which these goals are achieved varies considerably across programs.\u003c/p\u003e \u003cp\u003eHowever, international educational partnerships face significant sustainability challenges. Recent funding disruptions, including the 2025 USAID funding freeze for higher education partnerships in Africa, starkly demonstrated vulnerabilities when partnerships depend on single funding sources (Think Global Health, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; University World News, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This disruption highlighted the urgent need for partnership models that build genuine local capacity rather than relying solely on external donor support. Effective partnership design requires understanding how institutional contexts shape AI integration and partnership readiness in developing countries\u0026rsquo; higher education systems, yet this evidence base remains notably absent from the literature.\u003c/p\u003e \u003cp\u003eNigeria presents a particularly compelling case for examining AI integration patterns. As Africa\u0026rsquo;s most populous nation with over 200\u0026nbsp;million people and the continent\u0026rsquo;s largest economy, Nigeria\u0026rsquo;s higher education development carries significant regional implications (National Universities Commission, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The Nigerian higher education landscape encompasses approximately 310 universities across three distinct governance structures: Federal universities (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;72) funded by the national government, State universities (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;67) supported by individual state governments across Nigeria\u0026rsquo;s 36 states and Federal Capital Territory, and Private universities (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;171) operated by religious organizations, foundations, or individuals (National Universities Commission, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This institutional diversity creates natural variation in resources, capabilities, and governance approaches that enables systematic comparison of how different organizational factors influence AI adoption patterns.\u003c/p\u003e \u003cp\u003eGiven the recency of generative AI tools and the rapid pace of AI development, understanding of how higher education institutions in developing countries are incorporating these technologies remains limited. Recent comprehensive reviews of computing education in African countries have documented substantial bodies of work but emphasize the critical need for contextualized approaches that account for local realities (Hamouda et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Research on aligning African tertiary computer science education with global standards highlights both progress and persistent gaps in preparing graduates for an AI-transformed workforce (Oudshoorn et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A systematic review of AI in African education over the past five years identifies key trends, opportunities, and challenges while calling for more empirical research on institutional adoption patterns (Ahmed et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Despite this growing attention, no systematic comparative research has analyzed AI integration patterns across Nigerian institutional types, limiting the ability of international partners to develop evidence-informed collaboration strategies.\u003c/p\u003e \u003cp\u003eOur study addresses this gap by providing the first systematic comparative assessment of AI integration across Nigerian university types. Drawing on a conceptual framework combining institutional theory, resource-based view, and network theory, we assess integration across six dimensions: infrastructure, curriculum, research initiatives, industry partnerships, international collaborations, and policy frameworks. These dimensions capture the essential organizational capabilities required for meaningful AI integration: the technical foundation (infrastructure), human capital development (curriculum and research), external knowledge networks (industry and international partnerships), and strategic direction (policy frameworks). Two research questions (RQs) guided this investigation:\u003c/p\u003e \u003cp\u003e \u003cem\u003eRQ1: What is the current state of AI technology integration across Nigerian universities, and how does integration vary by institution type and dimension?\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRQ2: What institutional and contextual factors predict AI integration levels in Nigerian universities?\u003c/em\u003e \u003c/p\u003e \u003cp\u003eBy examining these questions, this study makes three contributions. First, it provides the first systematic comparative assessment of AI integration across Nigerian university types, establishing baseline data valuable for Nigerian institutions, potential international partners from any country, and the broader research community studying technology adoption in developing contexts. Second, it identifies institutional and contextual factors that predict integration levels, advancing theoretical understanding while providing practical guidance for partnership targeting. Third, building on these empirical findings, it offers evidence-informed insights for partnership development aligned with differentiated institutional capacities.\u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cp\u003eThe integration of AI technologies into higher education represents a transformative force reshaping pedagogical practices, research capabilities, and institutional operations globally (Crompton \u0026amp; Burke, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zawacki-Richter et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e). This transformation manifests across multiple domains: in teaching through adaptive learning systems, intelligent tutoring, and AI-powered content generation; in administration through chatbots, enrollment analytics, and resource optimization; and in research through automated literature review, data analysis, and hypothesis generation (Adamakis \u0026amp; Rachiotis, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Barnes \u0026amp; Hutson, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, AI adoption patterns vary significantly across institutional contexts and between developed and developing countries (Pedro et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This literature review examines empirical evidence relevant to understanding AI integration in Nigerian universities and its implications for international educational partnerships.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 AI Integration in Higher Education: Global Perspectives\u003c/h2\u003e \u003cp\u003eAI integration in higher education has accelerated dramatically in recent years, with scholarly publications on the topic increasing two to three times between 2021 and 2022 alone (Crompton \u0026amp; Burke, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While AI technologies have existed for decades in forms such as robotics, natural language processing, machine learning algorithms, and expert systems, the public release of large language models such as ChatGPT in late 2022 brought AI capabilities into mainstream awareness and prompted urgent institutional responses across higher education (Chumakov, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; İpek et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nalepa \u0026amp; Stefanowski, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This heightened attention reflects recognition that generative AI tools represent a qualitative shift in accessibility and applicability compared to earlier AI applications (Crompton \u0026amp; Burke, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Moy \u0026amp; Feldstein, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eContemporary AI applications in higher education span multiple domains. In teaching, educators employ adaptive learning systems that adjust content difficulty based on student performance, intelligent tutoring systems that provide personalized feedback, automated assessment tools that evaluate student work, and AI-powered content generators that assist with instructional material development (Lin \u0026amp; Chen, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ocen et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Walter, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Students increasingly use AI tools for research assistance, writing support, coding help, and personalized study guidance. Administrative applications include chatbots for student services, predictive analytics for enrollment management, learning analytics for early intervention with struggling students, and resource optimization systems (Microsoft, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mulford, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ocen et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; US Department of Education, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese technologies are also reshaping workforce expectations for university graduates. Industry analyses indicate that employers increasingly expect graduates to demonstrate AI literacy and ability to work alongside AI systems, regardless of their field of study. Microsoft\u0026rsquo;s \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e Work Trend Index found that AI skills are becoming essential across sectors, with employers seeking graduates who can leverage AI tools to enhance productivity (Microsoft, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This shift creates pressure on universities to integrate AI not only as a subject of study but as a tool embedded throughout the educational experience.\u003c/p\u003e \u003cp\u003eHowever, AI integration faces substantial challenges globally. Pedagogical concerns include impacts on critical thinking development and academic integrity, particularly with generative AI tools that can produce sophisticated text (Jin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ruano-Borbalan, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Walter, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Equity concerns are acute, as AI adoption risks exacerbating educational inequalities through differential access to technology and digital literacy skills (Feng et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; US Department of Education, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ethical challenges include algorithmic bias, data privacy, and governance frameworks that have not kept pace with rapid AI development (Al-Zahrani \u0026amp; Alasmari, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Policy responses are emerging at national and international levels, with the African Union releasing its Continental Artificial Intelligence Strategy in July 2024, providing comprehensive guidance for AI development and governance across member states (African Union, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Technology Adoption in African Higher Education\u003c/h2\u003e \u003cp\u003eDespite infrastructure challenges, AI integration in African higher education has progressed across multiple fronts. Recent studies document AI applications in educational contexts, including AI-driven career guidance systems that help students navigate employment pathways and bridge gaps in traditional career services (Eze et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Computing education programs across Africa have developed innovative approaches to teaching AI and machine learning, with curricula increasingly incorporating practical projects and industry-relevant skills (Hamouda et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Research on AI for educational development highlights emerging applications in areas such as personalized learning, administrative automation, and student support systems (Joel et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A systematic review of AI in African education over the past five years identifies growing scholarly attention to these opportunities alongside persistent implementation challenges (Ahmed et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, significant barriers still constrain widespread AI adoption. The digital divide manifests through inconsistent electricity supply, limited internet connectivity, and inadequate computing facilities (Astuti \u0026amp; Ayinde, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Barakabitze et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Many African universities operate with aging infrastructure and unreliable power supplies that impede sustained operation of AI systems (Hamzat \u0026amp; Ansah, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Maimela \u0026amp; Mbonde, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Uneven distribution of resources between urban and rural institutions creates significant disparities in integration capacity (Mateko et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Human capital constraints include limited faculty trained in AI technologies, insufficient professional development opportunities, and challenges retaining technically skilled personnel (Ibrahim, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tsakeni \u0026amp; Nwafor, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A shortage of industry internship opportunities further limits students\u0026rsquo; abilities to link theoretical knowledge to practical AI applications (Imizuokena \u0026amp; Ikechuku, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEfforts to address these challenges through contextualized approaches and collaborative networks show promise. Intra-African collaborations are opening new trajectories for knowledge production and exchange among African universities, particularly within regional bodies where facilitative policy frameworks and funding mechanisms have been established (Jowi, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Africa Higher Education Centers of Excellence program, a World Bank-funded initiative, has established over 80 centers across 20 countries, training more than 90,000 students and fostering regional research collaboration (World Bank, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These regional initiatives demonstrate potential pathways for building AI capacity through collaborative approaches.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The Nigerian Higher Education Context\u003c/h2\u003e \u003cp\u003eNigeria\u0026rsquo;s higher education system, the largest in Sub-Saharan Africa, operates through three distinct governance structures that create meaningful variation in institutional priorities, resource allocation, and operational flexibility. Federal universities receive funding from the national government primarily through the Tertiary Education Trust Fund (TETFund), which provides relatively stable resources for infrastructure development, research support, and faculty capacity building (Lawal, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This stable funding enables long-term planning but comes with bureaucratic oversight that can constrain rapid innovation. State universities are funded by individual state governments, creating significant variation in resources depending on each state\u0026rsquo;s economic conditions and political priorities; these institutions often have strong ties to regional development agendas but face funding uncertainty (Bamiro, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Private universities operate with greater autonomy in curriculum design, hiring, and organizational decision-making, enabling rapid response to market demands, though they depend heavily on tuition revenue and philanthropic support (Bamiro, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; My School Insight, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin this diverse landscape, AI-related activities are emerging across all institution types. Recent studies document AI-enabled learning platforms, learning analytics systems, and AI curriculum integration in various Nigerian universities (Bali et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Liverpool et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Oguine et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Yet, AI literacy among educators and students remains foundational across most institutions, with significant gaps in both awareness and practical skills (Ayanwale et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ibrahim, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The COVID-19 pandemic accelerated technology adoption, forcing rapid e-learning implementation while exposing significant gaps in infrastructure, digital literacy, and institutional readiness (Azubuike et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nwachukwu et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Olanrewaju et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This experience catalyzed increased institutional attention to digital transformation but also highlighted the substantial investments required for sustainable technology integration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 International Educational Partnerships\u003c/h2\u003e \u003cp\u003eInternational educational partnerships in higher education can be defined as sustained institutional relationships in which partner organizations with diverse expertise and resources join together to devise and execute plans for common goals, emphasizing mutual benefit, capacity development, and local ownership rather than traditional donor-recipient dynamics (Gronski \u0026amp; Pigg, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Obamba \u0026amp; Mwema, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Such partnerships take various forms including faculty exchange programs, joint research initiatives, curriculum development collaborations, student mobility arrangements, and infrastructure sharing agreements. The effectiveness of these arrangements depends substantially on alignment between partnership structures and the capacities of participating institutions.\u003c/p\u003e \u003cp\u003eResearch on international educational collaborations identifies recurring challenges. Power asymmetries between Northern and Southern partners can undermine genuine collaboration (Bradley, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Sustainability concerns arise when partnerships depend heavily on external funding without developing local resource bases. Misaligned priorities between partner institutions and insufficient attention to local contexts can limit partnership effectiveness (Obamba \u0026amp; Mwema, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The 2025 USAID funding freeze for higher education partnerships in Africa starkly demonstrated these vulnerabilities, disrupting programs across the continent when a single major funding source was withdrawn (Think Global Health, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; University World News, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This disruption highlighted the urgent need for partnership models that build genuine local capacity and diversify funding sources. Understanding how Federal, State, and Private universities differ in their capacities and partnership readiness would enable international partners to develop more targeted and sustainable collaboration strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Conceptual Framework","content":"\u003cp\u003eThis study employs an integrated conceptual framework combining institutional theory, resource-based view (RBV), and network theory to understand AI technology integration in Nigerian universities. While classical technology adoption theories such as diffusion of innovations and the unified theory of acceptance and use of technology explain individual-level adoption (Rogers, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Venkatesh et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), they inadequately capture organizational-level technology integration in resource-constrained developing country contexts. The three theoretical perspectives selected for this framework are complementary: institutional pressures create motivation for adoption, resources determine capacity to adopt, and networks provide knowledge and support for implementation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Institutional Theory\u003c/h2\u003e \u003cp\u003eInstitutional theory examines how organizational structures, norms, and legitimacy concerns influence technology adoption (DiMaggio \u0026amp; Powell, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Scott, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Universities face isomorphic pressures that shape technology adoption beyond purely technical considerations (DiMaggio \u0026amp; Powell, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). Coercive isomorphism arises from formal and informal pressures exerted by other organizations upon which institutions depend, including governments and resource providers. Mimetic isomorphism results from uncertainty, leading organizations to model themselves on successful peers. Normative isomorphism stems from professionalization, as members of occupational groups share common educational backgrounds and professional associations (DiMaggio \u0026amp; Powell, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1983\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmpirical research has applied institutional theory to understand technology adoption in higher education contexts. Scholars examining organizational change in Colombian higher education institutions found that all three types of isomorphism influenced institutional responses to environmental pressures, with coercive pressures from government policies being particularly strong (Cardona Mej\u0026iacute;a et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the context of Nigerian higher education, institutional theory suggests that different university types may face distinct isomorphic pressures. Federal universities, as nationally-funded flagship institutions, may experience stronger normative pressures to maintain research prominence and adopt technologies that signal institutional legitimacy (Alshumrani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Amrozi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Erdmann \u0026amp; Toro-Dupouy, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Private universities, operating in competitive enrollment markets, may face intensified mimetic pressures to adopt visible innovations that differentiate them from competitors (Hui, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Saka et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tingling \u0026amp; Parent, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). State universities, dependent on state government funding, may encounter coercive pressures shaped by regional political priorities (Arthur, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; \u0026Ouml;ZT\u0026Uuml;RK ERKO\u0026Ccedil;AK, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sahni et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Resource-Based View\u003c/h2\u003e \u003cp\u003eResource-based view theory explains how organizational resources and capabilities influence strategic decisions including technology adoption (Barney, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Sustained competitive advantage derives from resources that are valuable, rare, inimitable, and non-substitutable, including infrastructure, human capital, and financial assets (Mailani et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For AI integration, critical resources include computational infrastructure enabling advanced applications, faculty expertise and technical staff maintaining AI systems, and financial capacity supporting capital investments and ongoing operations (Adejo, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Fauzi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmpirical research has applied a resource-based view to explore technology adoption in educational contexts. Researchers examining e-learning adoption in Ghanaian higher education found that institutional resources, particularly IT infrastructure, technical support staff, and faculty development programs, significantly predicted adoption success, with resource-rich institutions demonstrating more comprehensive implementation (Asunka, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Similarly, a study of digital transformation in European universities demonstrated that human capital resources, specifically faculty digital competencies and dedicated technical personnel, were stronger predictors of successful technology integration than financial resources alone (Benavides et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Resource heterogeneity is particularly salient in developing country contexts where constraints create significant capability variation across institutions (Chipembele et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; ul Haq et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This perspective suggests that AI integration levels should correlate positively with institutional resource availability, and that resource differences across Federal, State, and Private universities should manifest in differentiated integration patterns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Network Theory\u003c/h2\u003e \u003cp\u003eNetwork theory emphasizes how inter-organizational relationships facilitate knowledge transfer and resource access (Granovetter, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Van Alstyne, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Universities\u0026rsquo; network connections, including research collaborations, industry partnerships, and professional associations, provide channels for acquiring technological knowledge and legitimizing innovations (Brunetti et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This collaborative approach is vital in the integration of new technologies like AI, where external ties can significantly influence adoption and implementation (George \u0026amp; Wooden, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmpirical research has demonstrated the importance of network relationships for technology adoption in higher education. A study of research collaboration networks among African universities found that institutions with stronger international ties produced more research output and demonstrated greater capacity to adopt new research technologies, with network centrality predicting institutional research productivity (Onyancha \u0026amp; Maluleka, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Research on innovation adoption in Nigerian universities specifically found that institutions with established industry partnerships were significantly more likely to implement new technologies, as these relationships provided both resources and legitimacy for innovation (Siyanbola et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). AI integration networks operate at multiple levels: industry partnerships provide applied knowledge, datasets, and funding; international collaborations offer access to cutting-edge research and global standards; regional networks enable knowledge sharing among institutions facing similar challenges (Ho et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yeager et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Framework Integration and Predictions\u003c/h2\u003e \u003cp\u003eThe integration of these three perspectives provides a unified conceptual framework for understanding AI adoption in Nigerian higher education (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The framework conceptualizes AI integration as simultaneously influenced by institutional pressures (institutional theory), resource capabilities (RBV), and network-facilitated knowledge transfer (network theory). These complementary lenses illuminate interconnected dimensions of technology adoption that no single theory could capture independently.\u003c/p\u003e \u003cp\u003eThe framework gives rise to testable predictions examined in this study. First, older institutions with established legitimacy should demonstrate higher overall AI integration due to accumulated organizational capabilities and resources. Second, AI integration should correlate positively with infrastructure capacity, human capital, and financial resources, with resource differences across institution types manifesting in differentiated integration patterns. Third, universities with stronger international collaborations and industry partnerships should exhibit higher AI integration, and these network connections should be positively correlated as inter-organizational ties tend to facilitate additional connections. Fourth, integration patterns should result from interactions among institutional, resource, and network factors rather than any single factor operating independently. The six dimensions assessed in this study (infrastructure, curriculum, research, industry partnerships, international collaborations, and policy frameworks) operationalize constructs from all three theoretical perspectives.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Research Design and Methodology","content":"\u003cp\u003eThis study employed a comparative content analysis methodology to systematically assess AI technology integration across Nigerian universities (R\u0026ouml;ssler, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Content analysis is well suited for examining complex organizational phenomena through publicly available documents and institutional communications (Krippendorff, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This approach enabled rigorous, replicable assessment without requiring human participant data collection while generating findings directly comparable across diverse institutional contexts (Elo \u0026amp; Kyng\u0026auml;s, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Sample Selection\u003c/h2\u003e \u003cp\u003eAt sampling frame development (May 2025), Nigeria\u0026rsquo;s higher education system comprised approximately 299 universities recognized by the National Universities Commission across three governance categories: Federal universities (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;72, 24.1%) funded by the national government, State universities (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;67, 22.4%) supported by individual state governments, and Private universities (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;160, 53.5%) operated by religious organizations, foundations, or individuals (National Universities Commission, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A total sample of 45 universities were targeted based on power analysis to detect medium effect sizes (Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5) with 80% power at α\u0026thinsp;=\u0026thinsp;0.05 for between-group comparisons (Cohen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This sample represents approximately 15% of all Nigerian universities.\u003c/p\u003e \u003cp\u003eThe study employed equal allocation stratified sampling (15 universities per institution type) rather than proportional allocation (Lohr, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This design choice was methodologically grounded in three considerations. First, the study\u0026rsquo;s primary purpose is comparative: the research questions explicitly focus on comparing AI integration patterns across institution types rather than estimating population-level parameters for Nigerian higher education overall. Equal sample sizes maximize statistical power for between-group comparisons and pairwise tests (Maxwell et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Second, equal allocation ensures adequate sample size within each stratum for reliable within-group analyses, including descriptive statistics, correlation analyses, and identification of predictive factors (Cohen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Maxwell et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Proportional allocation would yield approximately 11 Federal, 10 State, and 24 Private universities, potentially underpowering analyses for Federal and State institutions. Third, each governance structure represents a distinct policy context worthy of independent examination; equal representation ensures that findings for smaller sectors carry equivalent analytical weight (Lohr, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This approach aligns with established practice in comparative educational research (Cochran, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1977\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin each stratum, universities were selected using criteria to ensure representativeness. Geographic distribution ensured representation across all six geopolitical zones (North-Central, North-East, North-West, South-East, South-South, South-West). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a map of Nigeria\u0026rsquo;s geopolitical zones with the distribution of sampled institutions. Establishment period captured temporal variation, with institutions established between 1948 and 2023 categorized as Legacy (pre-1980), Expansion (1980\u0026ndash;2010), and Recent (2011\u0026ndash;2025). Information availability ensured only universities with accessible websites and sufficient publicly available information were included. The final sample showed Federal universities with the longest establishment history (mean\u0026thinsp;=\u0026thinsp;1979), followed by State (mean\u0026thinsp;=\u0026thinsp;1998) and Private (mean\u0026thinsp;=\u0026thinsp;2005). Geographic distribution showed concentration in the South-West (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16, 35.6%), consistent with historical patterns, followed by South-East (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, 17.8%), North-Central (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, 17.8%), South-South (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7, 15.6%), North-West (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4, 8.9%), and North-East (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2, 4.4%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Data Collection\u003c/h2\u003e \u003cp\u003eData collection followed a systematic protocol designed to ensure consistency and comprehensiveness across all institutions. Data were manually collected between May and October 2025 by trained researchers following standardized procedures. Primary data sources included official university websites and affiliated departmental sites, strategic plans and institutional documents where publicly available, course catalogs and academic program descriptions, faculty profiles and research publications, news releases and media coverage of AI-related initiatives, and partnership announcements and collaboration agreements.\u003c/p\u003e \u003cp\u003eEach institution was assessed through a five-step process: (1) systematic review of the main university website; (2) department-level analysis with particular emphasis on computer science, engineering, and related technical departments, as these fields serve as primary indicators of AI integration given their direct engagement with AI technologies; (3) faculty profile assessment examining individual research interests and publications; (4) review of news and events for recent AI-related announcements and activities; and (5) identification of collaboration agreements and partnerships. A standardized keyword search strategy employed primary terms (\u0026ldquo;artificial intelligence,\u0026rdquo; \u0026ldquo;AI,\u0026rdquo; \u0026ldquo;machine learning,\u0026rdquo; \u0026ldquo;data science\u0026rdquo;) and secondary terms (\u0026ldquo;robotics,\u0026rdquo; \u0026ldquo;automation,\u0026rdquo; \u0026ldquo;intelligent systems,\u0026rdquo; \u0026ldquo;deep learning\u0026rdquo;). This multi-tiered approach offered a comprehensive identification of AI-related content while minimizing false positives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Coding Framework\u003c/h2\u003e \u003cp\u003eAI integration was assessed across six dimensions using researcher-developed 10-point scales with anchored descriptors: AI Infrastructure (specialized facilities and computing resources reflecting resource-based capabilities), Curriculum Integration (AI content in academic programs capturing institutional responses to external demands), Research Initiatives (AI research, publications, and faculty expertise reflecting accumulated human capital), Industry Partnerships (private sector collaborations capturing network relationships), International Collaborations (foreign institution partnerships reflecting global network connections), and Policy Frameworks (institutional AI strategies and governance capturing formal organizational responses). Scores followed anchored descriptors: 1\u0026ndash;2 (Minimal), 3\u0026ndash;4 (Basic), 5\u0026ndash;6 (Moderate), 7\u0026ndash;8 (Advanced), and 9\u0026ndash;10 (Cutting-edge). An overall AI integration score was calculated as the mean of the six-dimensional scores. Detailed scoring rubrics with illustrative examples for each dimension are provided in the Online Resource 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Data Analysis\u003c/h2\u003e \u003cp\u003eData analysis employed both descriptive and inferential statistical techniques using Python (version 3.11) with the pandas, scipy.stats, and matplotlib libraries. Descriptive statistics were calculated for all variables, including measures of central tendency and dispersion. One-way analysis of variance (ANOVA) tested for significant differences between institution types across all dimensions. ANOVA assumptions were verified: normality was assessed using the Shapiro-Wilk test and homogeneity of variance was tested using Levene\u0026rsquo;s test (Alshahrani et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Shapiro \u0026amp; Wilk, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1965\u003c/span\u003e; Yi et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Where significant differences were detected, post-hoc Tukey HSD tests identified specific pairwise differences. Effect sizes were calculated using eta-squared (η\u0026sup2;) for ANOVA results and Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e for pairwise mean differences (Cohen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Pearson correlation analysis examined relationships between dimensions, with Bonferroni correction applied for multiple comparisons to control family-wise error rate. Multiple regression analysis identified significant predictors of overall AI integration. Statistical significance was set at α\u0026thinsp;=\u0026thinsp;0.05 for all analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Validity and Reliability\u003c/h2\u003e \u003cp\u003eMultiple measures were implemented to ensure validity and reliability. Inter-rater reliability was established through independent coding of 20% of institutions (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9) by two trained researchers. Cohen\u0026rsquo;s kappa (κ) values ranged from 0.79 to 0.85 across dimensions, indicating substantial to almost perfect agreement (Landis \u0026amp; Koch, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Discrepancies were resolved through discussion and consensus, with coding guidelines refined accordingly. External validation cross-referenced news articles (67% of major initiatives verified), academic publications (78% of research claims verified), partnership announcements (54% verified), and government documents (43% of policy claims verified). Data quality varied across institutions: 55.6% had comprehensive website information, 26.7% had limited information, and 17.8% had accessibility issues addressed through additional data collection. Complete dimensional scores for all 45 institutions are available in the Online Resource 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Researcher Positionality\u003c/h2\u003e \u003cp\u003eConsistent with recommendations for transparency in research (Secules et al., 2021), we acknowledge relevant positionalities. The first author is Nigerian, completed undergraduate engineering education at a sampled state university, and subsequently navigated educational and professional systems across five countries (Nigeria, France, United Kingdom, United Arab Emirates, United States). The second author is a U.S.-based assistant professor of computing education who has contributed to studies on AI applications in African workforce development. The third author is a Nigerian faculty member at a sampled institution whose research focuses on integrating machine learning into engineering curricula, with visiting scholar experience at MIT and leadership roles in professional organizations at the interface of academia-industry partnerships. The fourth author is an associate Professor of multidisciplinary engineering and computing education, systems and management who draws upon experiences working in higher education and engineering education research. The fifth author is an undergraduate Computer Science student at Ivy Tech Community College whose emerging expertise in programming and data analysis contributed to the systematic data collection process; his interest in AI applications for real-world problem-solving informed attention to practical implementation considerations. These diverse perspectives, spanning faculty, professional and student roles across multiple countries, informed contextual interpretation while potential biases were mitigated through systematic coding procedures and inter-rater reliability checks.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Findings","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1 Sample Characteristics\u003c/h2\u003e\n \u003cp\u003eThe final sample comprised 45 Nigerian universities stratified across three institutional types (Table\u0026nbsp;1): Federal (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15, 33.3%), State (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15, 33.3%), and Private (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15, 33.3%). This distribution ensured adequate statistical power for between-group comparisons while representing approximately 15% of all Nigerian universities recognized by the National Universities Commission.\u003c/p\u003e\n \u003cp\u003eTemporal distribution revealed significant variation in institutional age across types. Among sampled institutions, Federal universities demonstrated the longest establishment history (range: 1948\u0026ndash;2011, mean\u0026thinsp;=\u0026thinsp;1979), followed by State universities (range: 1979\u0026ndash;2023, mean\u0026thinsp;=\u0026thinsp;1998), and Private universities (range: 1999\u0026ndash;2014, mean\u0026thinsp;=\u0026thinsp;2005). This temporal stratification enabled analysis of how institutional maturity relates to AI integration capacity.\u003c/p\u003e\n \u003cp\u003eGeographic distribution showed concentration in the South-West zone (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16, 35.6%), consistent with historical patterns of university establishment in Nigeria. Remaining institutions were distributed across South-South (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7, 15.6%), North-Central (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, 17.8%), South-East (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, 17.8%), North-West (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4, 8.9%), and North-East (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2, 4.4%) zones.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;1\u003c/strong\u003e Sample Distribution by Institution Type and Geographic Region\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e5.2 Overall AI Integration Patterns\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAddressing Research Question 1 regarding the current state of AI integration, analysis revealed (Table\u0026nbsp;2 and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) moderate overall integration levels across the sample (M\u0026thinsp;=\u0026thinsp;4.79, SD\u0026thinsp;=\u0026thinsp;1.71, range: 1.83\u0026ndash;7.83 on a 10-point scale). This finding indicates substantial room for improvement while demonstrating that Nigerian universities have initiated meaningful AI integration efforts.\u003c/p\u003e\n \u003cp\u003eFederal universities had the highest mean integration scores (M\u0026thinsp;=\u0026thinsp;5.25, SD\u0026thinsp;=\u0026thinsp;1.64), followed by Private universities (M\u0026thinsp;=\u0026thinsp;4.76, SD\u0026thinsp;=\u0026thinsp;1.93) and State universities (M\u0026thinsp;=\u0026thinsp;4.36, SD\u0026thinsp;=\u0026thinsp;1.55). One-way ANOVA revealed that while these differences approached statistical significance (F(2,42)\u0026thinsp;=\u0026thinsp;1.89, p\u0026thinsp;=\u0026thinsp;0.163), the effect size was small (\u0026eta;\u0026sup2;=0.08). The lack of statistical significance indicates that factors beyond institutional type may better explain integration variation.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;2\u003c/strong\u003e Overall AI Integration Scores by Institution Type\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003eThe top five performing institutions included University of Lagos (7.83), Afe Babalola University (7.67), Covenant University (7.33), University of Ibadan (7.25), and Ahmadu Bello University (6.83). The top performers represented both Federal universities (University of Lagos, University of Ibadan, Ahmadu Bello University) and Private universities (Afe Babalola University, Covenant University), indicating that high integration levels are achievable across governance structures. All 45 sampled institutions are listed in the Online Resource 1 with their institution type, geographic region, and dimensional scores.\u003c/p\u003e\n \u003cp\u003eGeographic analysis revealed meaningful regional disparities in AI integration. Among regions with larger sample sizes, the South-West zone showed the highest integration (M\u0026thinsp;=\u0026thinsp;5.67, SD\u0026thinsp;=\u0026thinsp;1.61, n\u0026thinsp;=\u0026thinsp;16), followed by South-South (M\u0026thinsp;=\u0026thinsp;5.19, SD\u0026thinsp;=\u0026thinsp;1.68, n\u0026thinsp;=\u0026thinsp;7). The South-East region showed the lowest integration among regions with substantial samples (M\u0026thinsp;=\u0026thinsp;3.50, SD\u0026thinsp;=\u0026thinsp;0.66, n\u0026thinsp;=\u0026thinsp;8). North-West universities showed the greatest within-region variability (SD\u0026thinsp;=\u0026thinsp;2.07), reflecting heterogeneous integration patterns in that region.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3 Dimensional Analysis\u003c/h2\u003e\n \u003cp\u003eAnalysis across the six AI integration dimensions revealed significant variation in institutional capabilities (Table\u0026nbsp;3 and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Curriculum Integration emerged as the strongest dimension (M\u0026thinsp;=\u0026thinsp;5.73, SD\u0026thinsp;=\u0026thinsp;1.68), indicating that Nigerian universities have made substantial progress incorporating AI content into academic programs. Research Initiatives demonstrated comparable strength (M\u0026thinsp;=\u0026thinsp;5.57, SD\u0026thinsp;=\u0026thinsp;1.83), reflecting active engagement with AI-related research activities.\u003c/p\u003e\n \u003cp\u003ePolicy Frameworks represented the weakest dimension (M\u0026thinsp;=\u0026thinsp;4.09, SD\u0026thinsp;=\u0026thinsp;2.00), with only 31% of institutions demonstrating formal AI strategies or governance structures. This finding indicates a critical gap between operational AI activities and strategic institutional planning. International Collaborations (M\u0026thinsp;=\u0026thinsp;4.20, SD\u0026thinsp;=\u0026thinsp;2.28) and Industry Partnerships (M\u0026thinsp;=\u0026thinsp;4.68, SD\u0026thinsp;=\u0026thinsp;2.20) also showed significant room for improvement.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;3\u003c/strong\u003e Descriptive Statistics by AI Integration Dimension\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003eOne-way ANOVA (Table\u0026nbsp;4) revealed significant between-group differences for International Collaborations (F(2,42)\u0026thinsp;=\u0026thinsp;4.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023, \u0026eta;\u0026sup2;=0.16), indicating that institution type meaningfully influences international partnership development. Federal universities demonstrated the strongest international collaboration scores (M\u0026thinsp;=\u0026thinsp;5.07), followed by Private universities (M\u0026thinsp;=\u0026thinsp;4.40) and State universities (M\u0026thinsp;=\u0026thinsp;3.13). This medium effect size suggests that governance structure has practical significance for international engagement capacity.\u003c/p\u003e\n \u003cp\u003eCurriculum Integration showed no significant between-group differences (F(2,42)\u0026thinsp;=\u0026thinsp;2.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.130, \u0026eta;\u0026sup2;=0.09), indicating that AI curriculum development occurs across all institution types regardless of governance structure. Private universities demonstrated particular strength in this dimension (M\u0026thinsp;=\u0026thinsp;5.87), the highest dimensional score among all institution types, reflecting their organizational agility in responding to market demands for AI-related programs.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;4\u003c/strong\u003e ANOVA Results for AI Integration Dimensions by Institution Type\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003e*Note. \u003cem\u003ep \u0026lt; .05\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e5.4 Correlation Analysis\u003c/h2\u003e\n \u003cp\u003ePearson correlation analysis revealed significant positive relationships among AI integration dimensions, suggesting a hierarchical structure of institutional capabilities. The strongest relationships were found within internal institutional functions: AI Infrastructure and Curriculum Integration (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and between Research Initiatives and both AI Infrastructure (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Curriculum Integration (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These strong correlations indicate that institutions investing in infrastructure tend to develop corresponding curricular and research capabilities in a mutually reinforcing pattern.\u003c/p\u003e\n \u003cp\u003eFrom the perspective of external networks, the correlation between Industry Partnerships and International Collaborations (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was notable. While numerically lower than the internal capability correlations, it represents the strongest relationship involving external network connections. This finding suggests that globally connected institutions simultaneously develop robust local industry networks, supporting network theory predictions that connections facilitate additional connections.\u003c/p\u003e\n \u003cp\u003ePolicy Frameworks demonstrated the weakest correlations with other dimensions, particularly with Curriculum Integration (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This relatively weak relationship indicates a disconnect between strategic planning and operational activities, with many institutions pursuing AI curriculum development without formal institutional AI strategies to guide these efforts.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;5\u003c/strong\u003e Correlation Matrix of AI Integration Dimensions\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u0026nbsp;\u003c/strong\u003e\u003cem\u003e**p \u0026lt; .01, ***p \u0026lt; .001 (Bonferroni corrected)\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003e5.5 Predictors of AI Integration\u003c/h2\u003e\n \u003cp\u003eAddressing RQ2 (Table\u0026nbsp;6), regarding predictive factors, multiple regression analysis identified significant predictors of overall AI integration. The full model explained 42% of variance in integration scores (R\u0026sup2;=0.42, Adjusted R\u0026sup2;=0.36, F(4,40)\u0026thinsp;=\u0026thinsp;7.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In social science research, R\u0026sup2; values between 0.10 and 0.50 are generally considered acceptable given the complexity of human and organizational behavior (Ozili, 2023). The obtained R\u0026sup2; of 0.42 falls within the upper range of this benchmark, indicating that the model captures a meaningful portion of variance while acknowledging that additional unmeasured factors also influence AI integration.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;6\u003c/strong\u003e Multiple Regression Results: Predictors of Overall AI Integration\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u0026nbsp;\u003c/strong\u003e\u003cem\u003eR\u0026sup2; = 0.42, Adjusted R\u0026sup2; = 0.36, F(4,40)\u0026thinsp;=\u0026thinsp;7.24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eInstitution Age emerged as the strongest predictor (\u0026beta;\u0026thinsp;=\u0026thinsp;0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021), with older universities demonstrating higher integration levels. Each decade of institutional history was associated with a 0.34 standard deviation increase in AI integration scores, strongly supporting resource-based view predictions about organizational learning and capability accumulation over time. The Geographic Location of the institution also proved significant, with South-West region universities showing higher integration scores (\u0026beta;\u0026thinsp;=\u0026thinsp;0.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045) compared to other regions. This pattern reflects historical concentration of educational resources, economic activity, and international connections in the Lagos-Ibadan corridor. Notably, Institution Type was not a significant predictor when controlling for other variables (\u0026beta;\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.387), indicating that observed differences between Federal, State, and Private universities are largely mediated by factors such as institutional age, geographic location, and resource availability rather than governance structure per se. This finding has important theoretical and practical implications discussed in the following section.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"6 Discussion","content":"\u003cp\u003eThe moderate overall AI integration (M\u0026thinsp;=\u0026thinsp;4.79) observed across Nigerian universities reflects the complex interplay of institutional, resource, and network factors predicted by our conceptual framework. This finding indicates substantial room for improvement while demonstrating that Nigerian universities have initiated meaningful AI integration efforts. The following discussion interprets these findings through the lens of the three theoretical perspectives guiding this study.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Integration Patterns by Institution Type\u003c/h2\u003e \u003cp\u003eFederal universities demonstrated the highest mean integration scores (M\u0026thinsp;=\u0026thinsp;5.25), consistent with institutional theory predictions about advantages conferred by established legitimacy and stable resource bases (DiMaggio \u0026amp; Powell, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Scott, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These institutions, with mean establishment years dating to 1979, have accumulated organizational capabilities over decades that facilitate technology adoption. Consistent government funding through mechanisms such as the Tertiary Education Trust Fund (TETFund) provides the financial stability necessary for sustained infrastructure investment (Lawal, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The normative pressures associated with their role as national flagship institutions further incentivize adoption of visible technologies like AI to maintain competitive positioning (Alshumrani et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrivate universities illustrated notable strength in curriculum integration (M\u0026thinsp;=\u0026thinsp;5.87), the highest dimensional score among all institution types. This finding supports institutional theory predictions about mimetic pressures in competitive enrollment markets (Hui, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Saka et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Private universities depend heavily on tuition revenue and must signal innovation to attract students (Bamiro, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The organizational agility afforded by more flexible governance structures enables rapid program development in response to market demands (Ediriweera et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mijan et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This pattern suggests that resource constraints do not preclude innovation when institutions possess organizational flexibility and face competitive pressures that incentivize adoption. The implication is significant: curriculum innovation can proceed even under resource constraints when institutional structures permit organizational agility.\u003c/p\u003e \u003cp\u003eState universities showed the lowest overall integration (M\u0026thinsp;=\u0026thinsp;4.36), reflecting resource constraints predicted by resource-based view theory (Barney, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). These institutions face funding variability tied to state-level economic conditions, limiting capacity for capital-intensive AI investments (Jesam et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Umar et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, this finding should not be interpreted as indicating state universities lack capacity for AI development. Rather, their resource limitations may channel institutional energy toward areas requiring less capital investment, such as strategic planning and policy development. Their direct connections to state governments create unique opportunities for partnerships focused on regional development priorities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e6.2 The Significance of Non-Significant Institutional Type Effects\u003c/h2\u003e \u003cp\u003eThe finding that institution type was not significant when controlling for age and location (β\u0026thinsp;=\u0026thinsp;0.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.387) is theoretically important and warrants careful interpretation. While descriptive statistics showed differences between Federal, State, and Private universities, these differences are largely explained by other factors. Governance structure alone does not determine integration outcomes; accumulated resources and network positions matter more. This challenges assumptions that simply identifying institution types provides sufficient basis for partnership design.\u003c/p\u003e \u003cp\u003eThis finding has practical implications for international partners. Rather than seeking out partnerships based on governance category, partners should assess specific institutional profiles including establishment history, geographic context, existing network connections, and dimensional strengths. A newer Federal university may have less integration capacity than an established Private university, despite governance-based assumptions to the contrary.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Network Effects and Partnership Implications\u003c/h2\u003e \u003cp\u003eThe strong correlation between international collaborations and industry partnerships (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.74, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) represents a key finding with both theoretical and practical significance. Network theory predicts that inter-organizational connections facilitate knowledge transfer and resource access (Brunetti et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Granovetter, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). The observed correlation suggests that universities developing strong international networks are simultaneously building robust local industry relationships.\u003c/p\u003e \u003cp\u003eSeveral mechanisms may explain this pattern. International partners often facilitate local industry connections through their own networks. Globally engaged faculty attract industry attention through enhanced visibility and demonstrated competence. The capabilities developed through international collaboration, including research skills, project management experience, and exposure to global standards, enhance attractiveness to industry partners. This finding suggests that international and industry partnerships should be pursued as complementary rather than competing strategies. Effective international partnerships may serve as pathways to industry engagement, while industry connections can enhance attractiveness to international collaborators.\u003c/p\u003e \u003cp\u003eIn light of recent disruptions to international funding mechanisms, the strong correlation between international collaborations and industry partnerships also presents a strategic opportunity for sustainability. International collaborations may be most resilient when anchored in local industry relevance, creating triangular partnerships where local industry partners provide context and sustainability while international partners contribute expertise and global networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e6.4 The Policy Framework Gap\u003c/h2\u003e \u003cp\u003ePolicy Frameworks being the weakest dimension (M\u0026thinsp;=\u0026thinsp;4.09) indicates a critical gap between operational AI activities and strategic institutional planning. Many universities are adopting AI technologies reactively rather than through deliberate institutional strategy. The relatively weak correlation between Policy Frameworks and Curriculum Integration (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43) underscores this disconnect; institutions are developing AI curricula without formal policies to guide these efforts.\u003c/p\u003e \u003cp\u003eThis gap has implications for partnership sustainability. Effective international collaborations typically require clear institutional policies and governance frameworks to ensure alignment of objectives, allocation of resources, and continuity beyond individual relationships. The African Union\u0026rsquo;s Continental Artificial Intelligence Strategy (2024) provides a continental framework that institutions could adapt to develop institution-specific policies addressing ethical AI deployment, faculty capacity building, and alignment with national development priorities.\u003c/p\u003e \u003cp\u003eThe regional concentration of high-performing universities in the South-West (β\u0026thinsp;=\u0026thinsp;0.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045) reflects historical patterns of educational resource distribution and economic development in the Lagos-Ibadan corridor. This raises equity concerns, as universities in northern and eastern regions may face structural disadvantages in AI development. Investment in AI infrastructure and capacity building across regions could help address these disparities. Regional consortium models that enable resource sharing and knowledge transfer between well-resourced and developing institutions offer one mechanism for addressing these disparities.\u003c/p\u003e \u003c/div\u003e"},{"header":"7 Implications and Conclusion","content":"\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Implications for Policy\u003c/h2\u003e \u003cp\u003eThe identification of institution age and geographic location as significant predictors of AI integration, while institution type was not significant when controlling for other factors, suggests that policy interventions should focus on mechanisms that build institutional capacity regardless of governance structure. Newer universities and those in underserved regions may benefit from targeted support programs that accelerate capability development. Such programs could include dedicated infrastructure grants for computing facilities and AI laboratories, faculty development initiatives providing training in AI pedagogy and research methods, mentorship arrangements pairing emerging institutions with established universities, and access to shared cloud computing resources that reduce individual infrastructure burdens. The National Information Technology Development Agency (NITDA) and TETFund could coordinate such initiatives, ensuring resources reach institutions with the greatest development potential (Federal Ministry of Communications and Digital Economy, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such coordinated efforts would help address regional disparities and accelerate AI capacity building across the Nigerian higher education sector.\u003c/p\u003e \u003cp\u003eThe weakness in policy frameworks across all institution types (M\u0026thinsp;=\u0026thinsp;4.09) indicates a need for sector-wide attention to strategic planning and governance. Nigerian universities would benefit from developing formal AI strategies that articulate integration goals, resource allocation priorities, and partnership approaches. For faculty, such strategies should address professional development pathways, research support mechanisms, and guidelines for AI use in teaching and assessment. For students, strategies should articulate AI literacy expectations across programs, experiential learning opportunities, and ethical frameworks for AI use in academic work. The National Universities Commission could facilitate this development through guidance documents, benchmarking exercises, and incentive mechanisms that encourage strategic planning.\u003c/p\u003e \u003cp\u003eThe strong correlation between international collaborations and industry partnerships (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.74) suggests that policies promoting one form of partnership may generate spillover benefits for the other (Brunetti et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Granovetter, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). Government programs supporting international engagement, such as faculty mobility schemes and research exchange programs, may simultaneously strengthen industry relationships. Similarly, policies encouraging university-industry collaboration may enhance international visibility and attractiveness to foreign partners. This underscores the value of integrated partnership policies that recognize the interconnected nature of institutional networks rather than treating international and industry engagement as separate domains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Implications for International Partners\u003c/h2\u003e \u003cp\u003eThe differentiated institutional profiles suggest that effective international collaboration strategies must be tailored to institutional characteristics and capabilities rather than governance structure alone. Federal universities\u0026rsquo; combination of strong research capacity (M\u0026thinsp;=\u0026thinsp;6.3 for Research Initiatives) and international collaboration experience (M\u0026thinsp;=\u0026thinsp;5.07 for International Collaborations) positions them for comprehensive research partnerships emphasizing faculty exchange and joint research projects. Their established infrastructure, faculty expertise, and administrative capacity support sustained research collaboration.\u003c/p\u003e \u003cp\u003eFurthermore, private universities\u0026rsquo; excellence in curriculum integration (M\u0026thinsp;=\u0026thinsp;5.87) and organizational agility positions them for innovation-focused collaborations. Their industry orientation and entrepreneurial culture align with partnership models emphasizing practical application and technology transfer. Shorter-duration, project-based partnerships that leverage private university flexibility may prove more effective than long-term institutional arrangements requiring extensive bureaucratic navigation.\u003c/p\u003e \u003cp\u003eState universities, despite lower overall scores, offer unique opportunities through their direct connections to state governments. These connections create pathways for partnerships focused on public sector objectives and regional development priorities. Collaborations aligned with state development plans and addressing regional challenges may find particularly receptive partners among state universities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Limitations and Future Research\u003c/h2\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, reliance on publicly available information may underestimate AI integration at institutions with limited web presence or outdated website content. Second, the cross-sectional design captures AI integration at a single point in time, potentially missing dynamic changes in institutional capacity given the rapid pace of AI development. Longitudinal research tracking integration over time would provide insights into adoption trajectories and factors that accelerate or impede progress. Third, while the study collected institution-wide data, department-level analysis prioritized computing and engineering fields as primary indicators; AI integration in non-technical fields may follow different patterns. Fourth, the focus on Nigerian universities limits generalizability to other African countries with different higher education systems and development contexts. Comparative studies across multiple African countries would enhance understanding of how national contexts influence institutional AI adoption patterns.\u003c/p\u003e \u003cp\u003eFuture research should examine experiences of faculty, administrators, and students through qualitative methods to illuminate implementation challenges and success factors. Research on partnership outcomes and effectiveness would inform development of more successful collaboration models. Studies examining how Nigerian universities navigate tensions between global AI trends and local cultural contexts could contribute to more culturally sensitive implementation approaches.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e7.4 Conclusion\u003c/h2\u003e \u003cp\u003eThis study established that Nigerian universities have initiated meaningful AI integration efforts, yet significant gaps remain, particularly in policy frameworks and international collaborations. More importantly, the findings challenge assumptions that governance structure alone determines integration capacity. Institutional age, geographic location, and network connections matter as much, if not more, than whether a university is Federal, State, or Private. This insight reframes how international partners should approach collaboration: not by selecting institution types, but by understanding specific institutional profiles and designing partnerships accordingly.\u003c/p\u003e \u003cp\u003eInstitutional diversity, the very characteristic that might appear to complicate partnership design, represents a strategic asset when collaborations leverage complementary strengths across institution types. Federal universities can contribute research infrastructure and international networks, private universities can offer curriculum innovation and industry connections, and state universities can provide pathways to regional development priorities and public sector engagement. Effective international educational partnerships must move beyond traditional donor-recipient models toward collaboration that builds genuine local capacity. The findings establish essential baseline data for evidence-informed partnership development while contributing to broader understanding of technology adoption in developing country higher education contexts. As AI technologies continue reshaping higher education globally, ensuring that universities across all regions can meaningfully participate in this transformation is both an educational imperative and a matter of global equity.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamakis M, Rachiotis T (2025) Artificial Intelligence in Higher Education: A State-of-the-Art Overview of Pedagogical Integrity, Artificial Intelligence Literacy, and Policy Integration. Encyclopedia 5(4):180\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdejo LO (2025) Evaluating the Effectiveness and Challenges of Artificial Intelligence Tools in Chemistry Instruction at the Federal University of Education, Zaria. Fac Nat Appl Sci J Math Sci Educ 6(4):16\u0026ndash;35\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfrican Union (2024) Continental Artificial Intelligence Strategy. African Union. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://au.int/en/documents/20240809/continental-artificial-intelligence-strategy\u003c/span\u003e\u003cspan address=\"https://au.int/en/documents/20240809/continental-artificial-intelligence-strategy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed A-B, King BD, Hiran KK, Dadhich M, Malcalm E (2025) Half a decade of artificial intelligence in education in africa: Trends, opportunities, challenges and future directions. J Eng Educ Transformations, 81\u0026ndash;100\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlshahrani ND, Coolen-Maturi T, Coolen FP (2025) On Statistical Reproducibility of Normality and Equality of Variances Tests. J Stat Theory Pract 19(4):81\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlshumrani S, Baird K, Munir R (2022) Management innovation: The influence of institutional pressures and the impact on competitive advantage. Int J Manpow 43(5):1204\u0026ndash;1220\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Zahrani AM, Alasmari TM (2024) Exploring the impact of artificial intelligence on higher education: The dynamics of ethical, social, and educational implications. Humanit Social Sci Commun 11(1):1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmrozi Y, Ekowati D, Putranto H, Zikky M, Zulkarnain M (2023) Adoption of Information Technology as a Mediator Between Institutional Pressure and Change Performance. J Namibian Studies: History Politics Cult 34:788\u0026ndash;808\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArthur CS (2025) A preliminary case study: Challenges, approaches, and pressures in the policy implementation of iCGPA. J Praxis High Educ 7(1):98\u0026ndash;122\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAstuti HM, Ayinde LA (2025) Uneven progress: Analyzing the factors behind digital technology adoption rates in Sub-Saharan Africa (SSA). Data Policy 7:e23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/dap.2024.89\u003c/span\u003e\u003cspan address=\"10.1017/dap.2024.89\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsunka S (2008) Online learning in higher education in Sub-Saharan Africa: Ghanaian University students\u0026rsquo; experiences and perceptions. Int Rev Res Open Distrib Learn 9(3):1\u0026ndash;23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyanwale M, Adelana O, Molefi R, Adeeko O (2024) Examining artificial intelligence literacy among pre-service teachers for future classrooms. Elsevier BV. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.caeo.2024.100179\u003c/span\u003e\u003cspan address=\"10.1016/j.caeo.2024.100179\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzubuike OB, Adegboye O, Quadri H (2021) Who gets to learn in a pandemic? Exploring the digital divide in remote learning during the COVID-19 pandemic in Nigeria. Int J Educational Res Open 2:100022\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBali B, Garba EJ, Ahmadu AS, Takwate KT, Malgwi YM (2024) Analysis of emerging trends in artificial intelligence for education in Nigeria. Discover Artif Intell 4(1):110\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBamiro OA (2012) Sustainable financing of higher education in Nigeria: Funding models. Two-Day Consultative Policy Dialogue. Theme: The Future and Relevance of Nigerian Universities and Other Tertiary Institutions. Towards Higher Education Transformation\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarakabitze AA, William-Andey Lazaro A, Ainea N, Mkwizu MH, Maziku H, Matofali AX, Iddi A, Sanga C (2019) Transforming African education systems in science, technology, engineering, and mathematics (STEM) using ICTs: Challenges and opportunities. Educ Res Int 2019(1):6946809\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarnes E, Hutson J (2024) Strategic Integration of AI in Higher Education and Industry: The AI8-Point Model. Adv Social Sci Manage 2(6):39\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.63002/assm.26.520\u003c/span\u003e\u003cspan address=\"10.63002/assm.26.520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarney J (1991) Firm resources and sustained competitive advantage. J Manag 17(1):99\u0026ndash;120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenavides L, Tamayo Arias J, Arango Serna M, Bedoya B, J., Burgos D (2020) Digital Transformation in Higher Education Institutions: A Systematic Literature. Rev Sens 20(11):3291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/s20113291\u003c/span\u003e\u003cspan address=\"10.3390/s20113291\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBradley M (2008) On the agenda: North\u0026ndash;South research partnerships and agenda-setting processes. Dev Pract 18(6):673\u0026ndash;685. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/09614520802386314\u003c/span\u003e\u003cspan address=\"10.1080/09614520802386314\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunetti F, Matt DT, Bonfanti A, De Longhi A, Pedrini G, Orzes G (2020) Digital transformation challenges: Strategies emerging from a multi-stakeholder approach. TQM J 32(4):697\u0026ndash;724. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/TQM-12-2019-0309\u003c/span\u003e\u003cspan address=\"10.1108/TQM-12-2019-0309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardona Mej\u0026iacute;a LM, del Val P, M., Das\u0026iacute; Coscollar M (2020) del S. The institutional isomorphism in the context of organizational changes in higher education institutions. International Journal of Research in Education and Science, 6(1), 61\u0026ndash;73\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarnegie Mellon University Africa (2025) AI and robotics research. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.africa.engineering.cmu.edu/research/artificial-intelligence.html\u003c/span\u003e\u003cspan address=\"https://www.africa.engineering.cmu.edu/research/artificial-intelligence.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChipembele M, Ngandwe E, Monde MW, Ndalame EM (2025) Adoption of Artificial Intelligence in Academic Libraries in African Universities: A Scoping Review. IJSAT-International J Sci Technol, 16(3)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChumakov E (2025) History and definitions of AI. Eur Psychiatry 68(S1):S61\u0026ndash;S61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1192/j.eurpsy.2025.238\u003c/span\u003e\u003cspan address=\"10.1192/j.eurpsy.2025.238\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCochran WG (1977) Sampling techniques, 3 edn. Wiley\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen J (2013) Statistical power analysis for the behavioral sciences. routledge\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrompton H, Burke D (2023) Artificial intelligence in higher education: The state of the field. Int J Educational Technol High Educ 20(1):22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiMaggio PJ, Powell WW (1983) The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. Am Sociol Rev 48(2):147\u0026ndash;160\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdiriweera C, Pramudika H, Fernando M (2025) The Influence of AI-Driven Marketing Analytics on Strategic Decision-Making in Private Educational Institutes in Sri Lanka: Moderator Role of Organisational Culture. Asian J Mark Manage, 4(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElo S, Kyng\u0026auml;s H (2008) The qualitative content analysis process. J Adv Nurs 62(1):107\u0026ndash;115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-2648.2007.04569.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2648.2007.04569.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErdmann A, Toro-Dupouy L (2025) The influence of the institutional environment on AI adoption in universities: Identifying value drivers and necessary conditions. European Journal of Innovation Management\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEze P, Alabi A, Osunbunmi I (2025) AI-driven career guidance: Comparing Nigerian undergraduate and postgraduate perceptions. Discover Educ 4(1):543. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s44217-025-00900-0\u003c/span\u003e\u003cspan address=\"10.1007/s44217-025-00900-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFauzi HA, Yusoff H, Jasni NS, THE ROLE OF INFORMATION TECHNOLOGY (IT) FOR FINANCIAL SUSTAINABILITY IN HIGHER EDUCATION INSTITUTIONS (HEIS) (2025) Int J Mod Educ 7(24):1125\u0026ndash;1138. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.35631/IJMOE.724079\u003c/span\u003e\u003cspan address=\"10.35631/IJMOE.724079\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFederal Ministry of Communications and Digital Economy (2020) National Digital Economy Policy and Strategy (2020\u0026ndash;2030). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nitda.gov.ng/wp-content/uploads/2020/06/National-Digital-Economy-Policy-and-Strategy.pdf\u003c/span\u003e\u003cspan address=\"https://nitda.gov.ng/wp-content/uploads/2020/06/National-Digital-Economy-Policy-and-Strategy.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng J, Yu B, Tan WH, Dai Z, Li Z (2025) Key factors influencing educational technology adoption in higher education: A systematic review. PLOS Digit Health, 4(4), e0000764\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorge B, Wooden O (2023) Managing the Strategic Transformation of Higher Education through Artificial Intelligence. Administrative Sci 13(9):196. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/admsci13090196\u003c/span\u003e\u003cspan address=\"10.3390/admsci13090196\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGranovetter MS (1973) The strength of weak ties. Am J Sociol 78(6):1360\u0026ndash;1380\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGronski R, Pigg K (2000) University and community collaboration: Experiential learning in human services. Am Behav Sci 43(5):781\u0026ndash;792\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamouda S, Marshall L, Sanders K, Tshukudu E, Adelakun-Adeyemo O, Becker BA, Dodoo ER, Korsah GA, Luvhengo S, Ola O, Parkinson J, Sanusi IT (2025) Computing Education in African Countries: A Literature Review and Contextualised Learning Materials. 2024 Working Group Reports on Innovation and Technology in Computer Science Education, 1\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3689187.3709606\u003c/span\u003e\u003cspan address=\"10.1145/3689187.3709606\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamzat AA, Ansah SD (2025) Artificial intelligence awareness and readiness among undergraduate students in Kwara State universities Nigeria. J Educational Manage Instruction (JEMIN) 5(2):335\u0026ndash;349. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.22515/jemin.v5i2.11988\u003c/span\u003e\u003cspan address=\"10.22515/jemin.v5i2.11988\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHo L, Barnhart J, Trager R, Bengio Y, Brundage M, Carnegie A, Chowdhury R, Dafoe A, Hadfield G, Levi M, Snidal D (2023) International Institutions for Advanced AI (Version 2). arXiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.48550/ARXIV.2307.04699\u003c/span\u003e\u003cspan address=\"10.48550/ARXIV.2307.04699\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHui TC (2022) Market Isomorphism and Mobile Commerce Adoption in The Omnichannel: A Systematic Literature Review. Asian J Bus Res, 12(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIbrahim AB (2024) Assessing the knowledge and perception of artificial intelligence for teaching and research among lecturers in the faculties of arts in Nigeria. J Global Res Educ Social Sci 18(2):25\u0026ndash;33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImizuokena I-AJ, Ikechuku AE (2025) The Exploring AI capacity building programs in Nigerian higher education institutions: A study of curriculum design, faculty competence, and student engagement. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/ZENODO.15356893\u003c/span\u003e\u003cspan address=\"10.5281/ZENODO.15356893\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eİpek ZH, G\u0026ouml;z\u0026uuml;m AİC, Papadakis S, Kallogiannakis M (2023) Educational Applications of the ChatGPT AI System: A Systematic Review Research. Educational Process Int J 12(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.22521/edupij.2023.123.2\u003c/span\u003e\u003cspan address=\"10.22521/edupij.2023.123.2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJesam M, Onwubuya UO, Akpo AC (2025) Challenges in the Adoption of Artificial Intelligence for Effective Delivery of Business Education Course Content at the University of Cross River State. Mediterranean J Social Sci 16(5):135. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.36941/mjss-2025-0055\u003c/span\u003e\u003cspan address=\"10.36941/mjss-2025-0055\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin Y, Yan L, Echeverria V, Gašević D, Martinez-Maldonado R (2025) Generative AI in higher education: A global perspective of institutional adoption policies and guidelines, vol 8. Artificial Intelligence, Computers and Education, p 100348\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoel A-D, Victor A, I., Obinna E (2025) Leveraging artificial intelligence (AI) for educational development in Nigeria: Examining the challenges and opportunities. J Social Sci, 2(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJowi J (2024) Recent developments in higher education in Africa: Partnerships for knowledge transformations. J Int Cooperation Educ 26(1):34\u0026ndash;48\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrippendorff K (2018) Content analysis: An introduction to its methodology. Sage\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandis J, Koch G (2021) The measurement of observer agreement for categorical data. Biometrics 33, 159\u0026ndash;174 (1977). Sci Rep, 11, 10459\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawal I (2019), March 22 In search of autonomy for government-owned universities. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guardian.ng/features/in-search-of-autonomy-for-government-owned-universities/\u003c/span\u003e\u003cspan address=\"https://guardian.ng/features/in-search-of-autonomy-for-government-owned-universities/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin H, Chen Q (2024) Artificial intelligence (AI) -integrated educational applications and college students\u0026rsquo; creativity and academic emotions: Students and teachers\u0026rsquo; perceptions and attitudes. BMC Psychol 12(1):487. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40359-024-01979-0\u003c/span\u003e\u003cspan address=\"10.1186/s40359-024-01979-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiverpool L, Marut M, Ndam J, Oti D (2009) Towards a model for e-learning in Nigerian HEIS: lessons from the University of Jos ICT Maths initiative. Proceedings of the ICT Obafemi Awolowo University Ile-Ife\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLohr SL (2021) Sampling: Design and analysis. Chapman and Hall/CRC\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMailani D, Hulu MZT, Simamora MR, Kesuma SA (2024) Resource-Based View Theory to achieve a sustainable competitive advantage of the firm: Systematic literature review. Int J Entrepreneurship Sustain Stud 4(1):1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaimela C, Mbonde P (2025) Artificial intelligence in South African universities: Curriculum transformation and decolonisation\u0026mdash;aid or obstacle? Front Sociol 10:1543471. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fsoc.2025.1543471\u003c/span\u003e\u003cspan address=\"10.3389/fsoc.2025.1543471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMateko FM, Dowelani M, Sinamano R (2025) Digital Inequality and Transformation in South African Higher Education During COVID-19: A Comparative Analysis of Historically Disadvantaged and Historically Advantaged Universities. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1057/s41307-025-00416-0\u003c/span\u003e\u003cspan address=\"10.1057/s41307-025-00416-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Higher Education Policy\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxwell SE, Delaney HD, Kelley K (2017) Designing experiments and analyzing data: A model comparison perspective. Routledge\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMicrosoft (2024) 2024 Work Trend Index Annual Report: AI at work is here. Now comes the hard part. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part\u003c/span\u003e\u003cspan address=\"https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMicrosoft (2025) 2025 AI in Education: A Microsoft Special Report. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cdn-dynmedia-1.microsoft.com/is/content/microsoftcorp/microsoft/bade/documents/products-and-services/en-us/education/2025-Microsoft-AI-in-Education-Report.pdf\u003c/span\u003e\u003cspan address=\"https://cdn-dynmedia-1.microsoft.com/is/content/microsoftcorp/microsoft/bade/documents/products-and-services/en-us/education/2025-Microsoft-AI-in-Education-Report.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMijan A-A, Hasan MR, Hasan M (2025) AI and academia: Navigating the adoption of artificial intelligence in universities. Int J Technol Educ Sci 9(1):54\u0026ndash;65\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoy M, Feldstein A (2024) Generative AI: A University\u0026rsquo;s Approach to Understanding Opportunities and Challenges. Proceedings of the International Conference on Networked Learning, 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.54337/nlc.v14i1.8091\u003c/span\u003e\u003cspan address=\"10.54337/nlc.v14i1.8091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMulford D (2025) AI in Higher Education: A Meta Summary of Recent Surveys of Students and Faculty. Campbell University Academic Technology Services. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sites.campbell.edu/academictechnology/2025/03/06/ai-in-higher-education-a-summary-of-recent-surveys-of-students-and-faculty/\u003c/span\u003e\u003cspan address=\"https://sites.campbell.edu/academictechnology/2025/03/06/ai-in-higher-education-a-summary-of-recent-surveys-of-students-and-faculty/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMy School Insight (2025), July 26 Differences Between Federal, State, and Private Universities in Nigeria: A Comprehensive Guide. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://myschoolinsight.com/differences-between-federal-state-and-private-universities-in-nigeria-a-comprehensive-guide/\u003c/span\u003e\u003cspan address=\"https://myschoolinsight.com/differences-between-federal-state-and-private-universities-in-nigeria-a-comprehensive-guide/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNalepa GJ, Stefanowski J (2020) Artificial Intelligence Research Community and Associations in Poland. Found Comput Decis Sci 45(3):159\u0026ndash;177. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2478/fcds-2020-0009\u003c/span\u003e\u003cspan address=\"10.2478/fcds-2020-0009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Universities Commission (2025) List of Nigerian Universities. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nuc.edu.ng/\u003c/span\u003e\u003cspan address=\"https://www.nuc.edu.ng/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNwachukwu ST, Ugwu CM, Wogu JO (2021) Digital learning in post covid-19 era: Policy options and prospects for quality education in Nigeria. Library Philosophy and Practice, pp 1\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObamba M, Mwema J (2009) Symmetry and Asymmetry: New Contours, Paradigms, and Politics in African Academic Partnerships. High Educ Policy 22(3):349\u0026ndash;371\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOcen S, Elasu J, Aarakit SM, Olupot C (2025) Artificial intelligence in higher education institutions: Review of innovations, opportunities and challenges. Front Educ 10:1530247. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/feduc.2025.1530247\u003c/span\u003e\u003cspan address=\"10.3389/feduc.2025.1530247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOguine OC, Oguine KJ, Bisallah HI (2022) Big Data and Analytics Implementation in Tertiary Institutions to Predict Students Performance in Nigeria. arXiv Preprint arXiv :220714677\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlanrewaju GS, Omotosho AY, Falajiki OC, Osama G (2021) E-learning in a pandemic era: Exploring the digital gaps and needs of rural secondary schools and remote communities across 6 Nigerian states. Int J Innov Educ Res 9:696\u0026ndash;710\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnyancha OB, Maluleka JR (2011) Knowledge production through collaborative research in sub-Saharan Africa: How much do countries contribute to each other\u0026rsquo;s knowledge output and citation impact? Scientometrics, 87(2), 315\u0026ndash;336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11192-010-0330-5\u003c/span\u003e\u003cspan address=\"10.1007/s11192-010-0330-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOudshoorn MJ, Bradshaw K, Abandoh-Sam JA, Adelakun-Adeyemo O, Bainomugisha E, Binamungu LP, Khedr ME, Liebenberg J, Mwaura J, Nachuma C (2025) Future Ready, Globally Relevant: Aligning Africa\u0026rsquo;s Tertiary Computer Science Education Objectives with Global Standards. Proceedings of the ACM Global on Computing Education Conference 2025 Vol 2, 373\u0026ndash;375. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3736251.3749527\u003c/span\u003e\u003cspan address=\"10.1145/3736251.3749527\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Ouml;ZT\u0026Uuml;RK ERKO\u0026Ccedil;AK İ (2020) Coercive isomorphism in higher education: Direct pressures from the state to the Turkish universities\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedro F, Subosa M, Rivas A, Valverde P (2019) Artificial intelligence in education: Challenges and opportunities for sustainable development\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogers E (2003) Diffusion of Innovations 5th\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026ouml;ssler P (2013) Comparative content analysis. The handbook of comparative communication research. Routledge, pp 459\u0026ndash;468\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuano-Borbalan J-C (2025) The transformative impact of artificial intelligence on higher education: A critical reflection on current trends and futures directions. Int J Chin Educ 14(1):2212585X251319364\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahni J, Darda P, Gaur SS (2025) Integration of principles for responsible management education principles (PRME) in Saudi Arabian universities: A thematic analysis of claims on university websites. Society and Business Review\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaka AB, Chan DW, Ajayi SO (2024) Institutional isomorphism and adoption of building information modelling (BIM) in small and medium-sized enterprises (SMEs) of the Nigerian Construction Industry. Eng Constr Architectural Manage 31(1):179\u0026ndash;199\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScott WR (2013) Institutions and organizations: Ideas, interests, and identities. Sage\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShapiro SS, Wilk MB (1965) An analysis of variance test for normality (complete samples). Biometrika 52(3\u0026ndash;4):591\u0026ndash;611\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiyanbola WO, Egbetokun AA, Oluseyi I, Olamade OO, Aderemi HO, Sanni M (2012) Indigenous technologies and innovation in Nigeria. Opportunities for SMEs\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun W, Chen K, Mei J (2024) Integrating the resource-based view and dynamic capabilities: A comprehensive framework for sustaining competitive advantage in dynamic markets. EPRA Int J Economic Bus Rev 12(9):1\u0026ndash;8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThink Global Health (2025) Life after USAID: Africa\u0026rsquo;s development, education, and health care. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.thinkglobalhealth.org/article/life-after-usaid-africas-development-education-and-health-care\u003c/span\u003e\u003cspan address=\"https://www.thinkglobalhealth.org/article/life-after-usaid-africas-development-education-and-health-care\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTingling P, Parent M (2002) Mimetic isomorphism and technologyevaluation: Does imitation transcendjudgment? J Association Inform Syst 3(1):5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsakeni M, Nwafor SC (2025) Assessing Artificial Intelligence Literacy Among Pre-Service Science Teachers: Challenges and Strategies for Improvement. Int J Learn Teach Educational Res 24(11):356\u0026ndash;375. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.26803/ijlter.24.11.17\u003c/span\u003e\u003cspan address=\"10.26803/ijlter.24.11.17\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eul Haq F, Asim M, Suki NM, Zakaria N, Hussain S (2025) AI Adoption and Educational Effectiveness in Emerging Higher Education Institutions: The Moderating Role of Digital Literacy and Institutional Support. J Inform Knowl Manage, 2550090\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUmar M, Lee S, Gidado SL, Bakari GB, Adamu Ibrahim A (2025) Assessing Lecturers\u0026rsquo; Knowledge and Utilisation of Artificial Intelligence for Teaching and Research in Adamawa State Tertiary Institutions. Int J Adv Eng Manage 7(7):969\u0026ndash;976. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.35629/5252-0707969976\u003c/span\u003e\u003cspan address=\"10.35629/5252-0707969976\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUniversity of Michigan (2024) U-M launches AI training program for African scholars with \u003cspan\u003e$\u003c/span\u003e6M initiative. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://midas.umich.edu/midas-expands-its-ai-in-science-and-engineering-training-effort-to-africa/\u003c/span\u003e\u003cspan address=\"https://midas.umich.edu/midas-expands-its-ai-in-science-and-engineering-training-effort-to-africa/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUniversity World News (2024) Views on Africanising tech integration in higher education. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.universityworldnews.com/post.php?story=20231219172805108\u003c/span\u003e\u003cspan address=\"https://www.universityworldnews.com/post.php?story=20231219172805108\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUS Department of Education (2023) Artificial intelligence and the future of teaching and learning. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf\u003c/span\u003e\u003cspan address=\"https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Alstyne M (1997) The State of Network Organization: A Survey in Three Frameworks. J Organizational Comput Electron Commer 7(2\u0026ndash;3):83\u0026ndash;151. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10919392.1997.9681069\u003c/span\u003e\u003cspan address=\"10.1080/10919392.1997.9681069\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view. MIS Q, 425\u0026ndash;478\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalter Y (2024) Embracing the future of Artificial Intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. Int J Educational Technol High Educ 21(1):15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank (2025) Africa\u0026rsquo;s Higher Education Centers of Excellence: Celebrating a decade of transformative journey in revolutionizing Africa\u0026rsquo;s contribution to global research and innovation. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldbank.org/en/news/press-release/2025/04/07/africa-higher-education-centers-of-excellence-africa-contribution-to-global-research-and-innovation\u003c/span\u003e\u003cspan address=\"https://www.worldbank.org/en/news/press-release/2025/04/07/africa-higher-education-centers-of-excellence-africa-contribution-to-global-research-and-innovation\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeager B, Yalim J, Knuth S, Romanella A, Reidy C, Nickell B (2024) Identifying high-performance computing needs at member institutions in a regional consortium. Practice and Experience in Advanced Research Computing 2024: Human Powered Computing. 1\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1145/3626203.3670602\u003c/span\u003e\u003cspan address=\"10.1145/3626203.3670602\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYi Z, Chen Y-H, Yin Y, Cheng K, Wang Y, Nguyen D, Pham T, Kim E (2022) Brief research report: A comparison of robust tests for homogeneity of variance in factorial ANOVA. J Experimental Educ 90(2):505\u0026ndash;520\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZawacki-Richter O, Mar\u0026iacute;n VI, Bond M, Gouverneur F (2019a) Systematic review of research on artificial intelligence applications in higher education\u0026ndash;where are the educators? Int J Educational Technol High Educ 16(1):1\u0026ndash;27\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZawacki-Richter O, Mar\u0026iacute;n VI, Bond M, Gouverneur F (2019b) Systematic review of research on artificial intelligence applications in higher education\u0026ndash;where are the educators? Int J Educational Technol High Educ 16(1):1\u0026ndash;27\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Florida International University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"artificial intelligence, Nigerian higher education, technology adoption, international partnerships, comparative analysis","lastPublishedDoi":"10.21203/rs.3.rs-8816750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8816750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial 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\u0026thinsp;=\u0026thinsp;4.79, SD\u0026thinsp;=\u0026thinsp;1.71) with meaningful variation across dimensions and institution types. Federal universities demonstrated highest integration (M\u0026thinsp;=\u0026thinsp;5.25), private universities excelled in curriculum integration (M\u0026thinsp;=\u0026thinsp;5.87), and state universities showed emerging strength in policy frameworks. Institution age (β\u0026thinsp;=\u0026thinsp;0.34, p\u0026thinsp;=\u0026thinsp;0.021) and geographic location (β\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;=\u0026thinsp;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\u0026thinsp;=\u0026thinsp;0.74, p\u0026thinsp;\u0026lt;\u0026thinsp;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.\u003c/p\u003e","manuscriptTitle":"Building Sustainable International Partnerships: Evidence-Informed Strategies Based on a Comparative Analysis of Nigerian Universities' AI Integration and Readiness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 05:37:37","doi":"10.21203/rs.3.rs-8816750/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d51b3602-bf3e-4eba-b2c4-3e2ba864fb7f","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-10T05:37:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 05:37:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8816750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8816750","identity":"rs-8816750","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}