Purpose: This research systematically examines how AI technologies in higher education contribute to achieving Sustainable Development Goals (SDGs), focusing on SDG 4 (quality education) and SDG 10 (reduced inequalities). Design/methodology/approach: We included peer-reviewed articles published in English between 2015 and 2025, identified through ERIC, Web of Science, Education Source Ultimate, Sustainability Reference Center and Academic Search Ultimate databases. Out of the 1,673 records, 1,360 were screened after 313 duplicates were removed. We conducted full-text reviews on 75 eligible articles and 51 met the inclusion criteria. Findings: The findings indicate that AI advances quality education through personalized learning, predictive analytics and adaptive systems, while promoting reduced inequalities via accessibility and inclusive support for underrepresented groups. Key challenges include ethical concerns, infrastructure gaps and cultural resistance. Research limitations/implications: This review is limited by its focus on English-language, peer-reviewed articles published between 2015 and 2025, potentially excluding relevant studies in other languages and publication types. Practical implications: Our review highlights the need for equitable AI implementation in HEIs by promoting ethical frameworks, infrastructure investment and educator training to maximize its potential for advancing SDGs. Originality/value: This study is the first to have a broad view of the application of AI in higher education institutions (HEIs) in the effort of achieving SDGs 4 and 10. It reveals the scope, patterns and implications of AI use and synthesizes barriers impacting its integration and their influence on fulfilling SDG 4 and SDG 10 objectives.

Despite Generative artificial intelligence (GenAI) opportunities, African Higher Education Institutions (AHEIs) still face distinctive GenAI adoption challenges. This systematic literature review examines the opportunities, ethical concerns, and preparedness of AHEIs for GenAI’s adoption. The search was conducted on May 27, 2025 from 2022 to 2025. Only the 39 peer-reviewed journal articles and conference proceedings that met the inclusion criteria were included. Findings showed that studies are mostly distributed across South Africa, Egypt, and Nigeria. Furthermore, transformational teaching and learning, research enhancement, and Promotion of equity were the major GenAI’s opportunities. However, several challenges, including academic misconduct, bias, and the digital divide, remain. In addition, AHEIs' preparedness for GenAI integration remains inconsistent; hence, addressing these challenges to maximize GenAI’s is crucial.

Purpose: Principals’ capacity for school improvement efforts is influenced by their leadership self-efficacy which is particularly susceptible to psychological stress. While the impact of work-stress on self-efficacy is a global issue among principals and headmasters, the role of mental health conditions such as anxiety and depression is under-explored. This study aims to examine how work-stress influences leaders’ perception of total, management, instructional and moral self-efficacies and how mental health conditions operate as moderators and/or mediators in these relationships. Design/methodology/approach: We conducted a quantitative study with data collected from Texas, US principals (n = 181–194) and performed a series of regression analyses to examine work-stress and demographic factors as predictors of leadership self-efficacies and the role of mental health as mediators/moderators of significant relationships. Findings: Our findings highlight work-stress as a central negative predictor of leadership self-efficacies, and depression, not anxiety, as a significant partial mediator between work-stress and leadership self-efficacies. Additionally, mental health symptoms were not significant moderators, suggesting that mental health concerns do not affect the strength of the predictive relationships. Practical implications: We provide recommendations for individuals, districts, and principal preparation programs to support leaders’ stress and depression which have downstream effects on principals’ confidence to lead schools. Originality/value: This study extends the literature in school leadership well-being by providing evidence that work-stress induces depression which then negatively impacts principals’ leadership self-efficacy. Simultaneously, principals’ mental health conditions, whether induced by work or other aspects of life including genetic predispositions, do not change the magnitude of the work-stress self-efficacy relationship.

Purpose: This research systematically examines how AI technologies in higher education contribute to achieving Sustainable Development Goals (SDGs), focusing on SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities). Methodology: We included peer-reviewed articles published in English between 2015 and 2025, identified through ERIC, Web of Science, Education Source Ultimate, Sustainability Reference Center, and Academic Search Ultimate databases. Out of the 1673 records, 1360 were screened after 313 duplicates were removed. We conducted full-text reviews on 75 eligible articles, and 51 met the inclusion criteria.Findings: The findings indicate that AI advances quality education through personalized learning, predictive analytics, and adaptive systems, while promoting reduced inequalities via accessibility and inclusive support for underrepresented groups. Key challenges include ethical concerns, infrastructure gaps, and cultural resistance.Originality: This study is the first to have a broad view of the application of AI in higher education institutions (HEIs) in the effort of achieving SDGs 4 and 10. It reveals the scope, patterns, and implications of AI use and synthesizes barriers impacting its integration and their influence on fulfilling SDG 4 and SDG 10 objectives. Research Limitations: This review is limited by its focus on English-language, peer-reviewed articles published between 2015 and 2025, potentially excluding relevant studies in other languages, and publication types. Practical Implications: Our review highlights the need for equitable AI implementation in HEIs by promoting ethical frameworks, infrastructure investment, and educator training to maximize its potential for advancing SDGs.