From leadership research to district action: AI inclusivity and the centrality of Africa: a systematic review of representation, innovation, and governance in global artificial intelligence

AI-assisted editorial implementation guide. Proposed district actions are separate from the original authors’ findings and recommendations. Independent peer review of this guide is not asserted.

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Evidence and transfer

Education-related source: verify which participants, institutions and outcomes were actually studied before district adoption. Source publication type: systematic-review. License and identity checks do not establish study quality or causality.

Original authors’ abstract, reproduced under the source license:

Abstract Artificial Intelligence (AI) is reshaping global systems across education, healthcare, finance, and agriculture. Despite its transformative potential, concerns about inclusivity persist, particularly regarding the underrepresentation of African contexts in AI development. This study examines Africa’s role in achieving globally inclusive AI and explores the implications of its marginalization. A qualitative systematic review design was adopted, guided by PRISMA protocols, synthesizing peer-reviewed literature, policy documents, and grey sources published between 2018 and 2025. The analysis was structured around a conceptual framework linking data diversity, development processes, and AI outcomes. Findings reveal that limited African representation in datasets, governance institutions, and innovation ecosystems contributes to algorithmic bias, reduced contextual relevance, and reinforcement of global digital inequalities. Conversely, African contributions, including context-sensitive innovation, sector-specific AI innovation, and ethical frameworks grounded in communal philosophies such as Ubuntu, demonstrate the continent’s centrality to equitable AI systems. Structural barriers such as infrastructure gaps, funding constraints, and unequal global partnerships continue to limit participation. The study concludes that AI inclusivity is unattainable without meaningful African engagement in data production, model development, and governance structures. Policy recommendations emphasize capacity building, data sovereignty, equitable partnerships, and strengthened continental AI strategies. Integrating Africa as a core participant is not only a moral imperative but a technical necessity for globally representative and socially responsive AI systems. Keywords: Africa, AI bias, artificial intelligence, digital innovation, inclusivity

Study methods

This study adopted a systematic qualitative literature review to synthesize existing evidence on Africa’s role in promoting inclusive AI. A systematic review was deemed appropriate because it provides a transparent, rigorous, and reproducible approach to identifying, evaluating, and synthesizing existing knowledge on a specific research problem, thereby facilitating the development of a comprehensive understanding of the subject and the identification of research gaps (Snyder, 2019; Tranfield et al., 2003). The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines developed by Page et al. (2021), which provides a structured, widely accepted framework for literature identification, screening, eligibility assessment, and study inclusion. The application of PRISMA enhances the transparency, consistency, and reliability of the review process while reducing the potential for selection bias and improving the reproducibility of findings. To address the research questions presented in the Introduction, this study adopted a systematic review methodology guided by the Preferred Reporting Items for PRISMA framework. The review followed a structured process comprising literature identification, screening, eligibility assessment, data extraction, quality appraisal, and thematic synthesis. This methodological approach ensured a transparent, rigorous, and reproducible synthesis of the literature on AI inclusivity and the centrality of Africa in global AI research.

Limitations

This study has several limitations. First, the review was restricted to English-language publications and therefore may not capture relevant research published in other languages. Second, the analysis focused primarily on studies published between 2018 and 2025, potentially excluding earlier foundational work. Third, as a qualitative systematic review, the study synthesises existing evidence rather than generating new empirical data. Future research should undertake comparative analyses across African regions and countries to better understand variations in AI development, governance, and innovation capacity. Additional empirical studies are also needed to examine the long-term impacts of African participation in global AI ecosystems and to evaluate emerging governance models grounded in African ethical traditions and socio-cultural contexts.

A 90-day district inquiry

Days 1–15 · Appraise. Read the source methods, recruitment, comparator, outcomes and limitations. Record how the study setting differs from your district and which claim the design can support.
Days 16–30 · Design. Select one recurring district decision. Publish the decision owner, evidence requirements, consultation process and appeal route; test the process on a bounded low-risk decision. Agree participation, privacy protections, baseline and stopping rules before starting.
Days 31–60 · Observe. Collect the pre-agreed measures, keep a log of implementation changes and invite confidential voluntary feedback. Preserve existing support for staff and learners.
Days 61–90 · Decide. Track decision time, unresolved objections, documented reasons, appeals and access differences. Review whether the process changes who can participate and who carries the burden. Make a dated decision to stop, revise or extend the inquiry.

Board decision brief

Present the local problem, source setting, evidence limits, intervention, responsible owner, total cost, measured changes and uncertainty. Separate staff perceptions, implementation measures and student learning outcomes.

Attribution and reuse

Ramadile Moletsane. AI inclusivity and the centrality of Africa: a systematic review of representation, innovation, and governance in global artificial intelligence. Frontiers in Artificial Intelligence (2026-08-28). https://doi.org/10.3389/frai.2026.1881299

Prepared 2026-11-07. Source authorship and DOI are preserved. The abstract and source passages are attributed extracts; this editorial guide is offered under the same CC BY 4.0 license.