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.

Generative artificial intelligence is becoming embedded in higher education as students evaluate its usefulness and integrity implications. We analyzed Global ChatGPT Student Survey data to examine associations among academic-integrity concerns, satisfaction, and perceived learning benefits across national academic settings. The source dataset contained 23,218 records from 108 identifiable countries. The primary multigroup analysis included 8650 respondents from 15 countries, meeting study-defined thresholds for FIML-eligible sample size (≥300), complete nine-indicator data (≥200), completeness (≥80%), and model feasibility. A seven-country robustness analysis (N = 5871) applied stricter sample-size and completeness criteria. Configural and full metric invariance were broadly supported; full scalar invariance was not, so latent means were not compared. Satisfaction was positively associated with perceived learning benefits in all 15 countries. Academic-integrity concerns were negatively associated with satisfaction in 14 countries and directly with perceived learning benefits in 13, although five and six respective confidence intervals included zero. Robust Wald tests of unstandardized paths detected heterogeneity, while equality constraints caused only trivial global-fit deterioration. The robustness analysis reproduced the directional pattern and heterogeneity evidence. These findings provide a limited empirical window into students’ GenAI-related academic norms and evaluations; they do not directly measure culture or establish causal mediation.

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.

This study examines science teachers’ perceptions of ChatGPT’s impact on classroom assessments using the Technology Acceptance Model and Theory of Planned Behavior. Data from a publicly available survey of 19 science educators across life sciences, physical sciences, and technology/engineering disciplines were analyzed using one-way ANOVA and multiple regression. Results showed no statistically significant differences in perceptions across disciplines or based on teaching experience, age, and gender. These findings suggest uniform perceptions of ChatGPT, reflecting its perceived usefulness and ease of use, irrespective of teaching area or demographics. The study highlights ChatGPT’s potential to enhance assessment practices while emphasizing the need for institutional support and professional development. Recommendations for equitable AI integration and future research are provided.