Merith Eva Bardales Meneces, Alexandra Rivas Meza, Raymond Portelli Xueres, Karen Roxana Pulido Murillo
Introduction: Artificial intelligence (AI) is expanding opportunities for personalized learning, academic writing assistance, automated feedback, and access to educational resources in higher education. However, students may differ in their ability to benefit from these technologies because of socioeconomic circumstances, unequal technological access, differences in AI literacy, and variations in institutional support. This study aims to examine the relationships between unequal access to AI-enhanced learning technologies and academic inequality among university students.
Methodology: The study proposes a quantitative, non-experimental, cross-sectional design with a correlational and explanatory orientation. Data will be collected through a structured questionnaire administered to university students. The analytical framework includes socioeconomic conditions, access to AI-enhanced learning technologies, AI literacy, institutional support, learning engagement, and self-reported academic outcomes. The planned analyses include descriptive statistics, correlation analysis, and multivariable regression. Structural equation modeling may be considered if the sample size, measurement properties, and model specification justify its use.
**Results:** Empirical results are not yet available because data collection and statistical analysis have not been completed. The final manuscript will report the participant characteristics, measurement quality, estimated associations, confidence intervals, effect sizes, and hypothesis-testing results based on the observed data.
Conclusion: The proposed framework seeks to clarify how access to AI technologies, students' AI-related competencies, socioeconomic conditions, and institutional support may be associated with differences in academic experiences. The study may inform institutional strategies for promoting equitable access to AI-enhanced learning technologies. Any conclusions about educational effects will be based on the empirical findings and interpreted in light of the limitations of a cross-sectional design.
Juan Carlos Quijandria Lavarello, Julio Andres Palomino Silva, Pedro Liberato Valladares Jara, Lucio Alberto Sosa Bitulas
Artificial intelligence (AI) is increasingly transforming teaching and learning in higher education, creating new opportunities for personalized instruction, academic support, and learning enhancement. However, unequal access to AI-based educational technologies may reinforce existing academic inequalities among students from different socioeconomic backgrounds and institutional contexts. This article examines how disparities in access to, use of, and support for AI-enhanced learning technologies may contribute to unequal educational opportunities in higher education. It develops a conceptual framework connecting digital access, AI literacy, institutional readiness, and students’ opportunities to benefit from AI-supported learning. The framework also considers the role of institutional policies, digital infrastructure, and educational support in shaping the relationship between AI adoption and academic inequality. Rather than presenting unverified empirical findings, the article identifies conceptual relationships and proposes directions for future empirical investigation. The framework aims to support researchers, educators, and higher education policymakers in evaluating the equity implications of AI integration and in designing strategies that promote inclusive, responsible, and equitable access to educational technologies.
Nimota Jibola Kadir Abdullahi, Bello Muhinat Bolanle, Babatunde Akanmu Isiaka, O. M. Zubair, M. S. Mubarak, Eniola Keji Ola-Alani, Lasiele A. Yahaya
**Objective of the study:** This study examines the influence of artificial intelligence (AI)-enabled applications on the management and digitalization of higher education institutions, focusing on Pedagogical Enhancement, Data-driven Insight, and Administrative Effectiveness.
**Methodology/approach:** A quantitative research design was adopted, and data collected from academic and administrative staff in selected higher education institutions were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS. The structural model assessed the direct effects of Pedagogical Enhancement, Data-driven Insight, and Administrative Effectiveness on Management and Digitalization of Higher Institutions.
**Results:** The findings revealed that Administrative Effectiveness had the strongest and statistically significant positive association with institutional management and digitalization ( β = 0.627, p < 0.001). Pedagogical Enhancement also had a positive and statistically significant effect ( β = 0.193, p = 0.022), whereas Data-driven Insight had a positive but statistically non-significant effect ( β = 0.050, p = 0.470). The model explained 67.4% of the variance in Management and Digitalization of Higher Institutions. The findings indicate that AI-enabled administrative applications provide the strongest direct pathway to institutional digitalization, while pedagogical applications make a significant but comparatively smaller contribution. The non-significant effect of Data-driven Insight suggests that analytical capability alone may not translate into institutional transformation without adequate data governance, digital competence, institutional readiness, and the capacity to translate analytical outputs into action.
**Theoretical/methodological contributions:** The study reinforces theories on AI integration in education, demonstrating its role in improving learning experiences and operational efficiency. The findings suggest that AI-driven transformation in higher education is multidimensional and uneven, with administrative and pedagogical applications demonstrating stronger direct effects than data-driven insight.
**Social/managerial contributions:** The study recommends an integrated AI strategy that prioritises administrative process optimisation, strengthens AI-supported pedagogy, and develops the organisational capacity required to convert data-driven insights into effective institutional decisions.
This systematic review synthesises evidence on ICT-driven mathematical modelling strategies for embedding Education for Sustainable Development (ESD) in mathematics teacher education. Following PRISMA guidelines, we searched AI-powered, Google Scholar (Google Scholar Labs) (2016–March 2026) and identified 18 peer-reviewed studies that met the inclusion criteria. Thematic analysis revealed three most frequently reported effective strategy clusters for distinct competency outcomes: (1) iterative and cyclic modelling using dynamic mathematics software (GeoGebra, R, Python), frequently effective reported for developing modelling competence ( n = 4 studies); (2) dialectical and collaborative modelling, effective for developing sustainability integration competence ( n = 3 studies); and (3) interdisciplinary place-based modelling using sensor technologies, for developing pedagogical competence ( n = 3 studies). Further, based on the synthesis only, 22% of the studies ( n = 4) originated from Global South authors, including Sub-Saharan African contexts which provided empirical data on ICT-ESD integration in mathematics teacher education. In addition, based on the synthesis, we derived five design principles for professional development: (a) multidimensional competence alignment, (b) teacher agency development, (c) modelling as pedagogical bridge, (d) purposeful technology integration, and (e) flexible blended delivery. Given these findings and the severe resource constraints characteristic of many teacher-training contexts, particularly in underrepresented Global South settings, we recommend that effective strategies be embedded flexibly within existing teacher-training curricula rather than implemented as standalone add-ons. We also propose for curriculum mapping approaches that embed ESD within existing mathematics content rather than creating separate curricula. The findings of this study can inform development of a context-specific, digitally enhanced ESD course in teacher education programmes.
This conceptual analysis article examines the supervision of school principals through the paradigms of recognition and power relations. Using a comparative education approach, it examines three illustrative governance contexts: the United States, Abu Dhabi in the United Arab Emirates, and the People's Republic of China. Principal supervision is a critical site where leadership of recognition intersects with power dynamics. It is argued that although principal supervision is performed in the name of accountability and, to varying degrees, decentralization of educational administration, supervision structure and practice inextricably reproduce institutionalized power relations. The article integrates leadership of recognition and Foucauldian analyses of power to examine how supervisory policies institutionalize systems of differentiations, rationalization, and control, and to develop a comparative typology of instrumental, institutional-developmental, and ideological modes of recognition in principal supervision. It is suggested that systems of principal supervision are not merely compliance-oriented administrative routines or forms of professional support, but also arenas of conflicting ontologies of leadership, justice, and moral authority. The article proposes recognition and power relations as a theoretically robust lens for comparative analysis of educational supervision and argues that principal supervision is not merely a mechanism for evaluating leadership but also a process through which leadership itself is constituted.
