INTERNATIONAL WOMEN'S DAY 2026
Designing the Future:
The Role of AI and Digital Skills for Women in Science
Give to Gain: Investing in Women's Digital Skills Returns Equitable Science for All
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Figure 1. The Give-to-Gain investment logic for women in AI-driven science. Source: Framework informed by UNESCO, WEF, OECD, and GSMA, 2024–2025.
FRAMING THE MOMENT
FRAMING THE MOMENT
She types one sentence into a prompt box, and an idea that once took months of number-crunching appears in minutes. Across clinics, classrooms, and field stations, that same prompt-driven speed is changing what research teams can do, and who gets to lead. If women gain the digital skills to write the prompts, audit the outputs, and steer the ethics, they won't just use the future of science: they'll design it.
Artificial intelligence (AI) is reshaping scientific discovery at extraordinary speed. Today, roughly 84 percent of researchers use AI tools in their workflow1. Across Africa, adoption is accelerating even as the continent receives a relatively small share of global AI investment23. This gap between uptake and investment holds both urgent risk and remarkable opportunity. Unless women are equipped to participate at the frontier of AI-driven science, a generation of breakthroughs will be built without the perspectives, contexts, and communities that matter most.
International Women's Day 2026 anchors this moment in a powerful frame: Give to Gain. When institutions, governments, and communities invest in women's digital and AI capabilities, the returns, in innovation, health outcomes, and economic resilience, flow back to everyone.
THE DATA REALITY: REPRESENTATION, ACCESS, AND SKILLS
Progress toward gender parity in science has been real but slow. Women make up roughly 31 percent of researchers globally and approximately 33 percent in sub-Saharan Africa4. In leadership roles and advanced research positions, representation drops further, widening a gap that shapes which questions science asks and whose problems get solved.
The digital divide compounds these disparities. In Africa, 43 percent of men access the internet compared with 31 percent of women5. Women in sub-Saharan Africa are about 19 percent less likely than men to use mobile internet6. Among youth, basic digital literacy remains limited, constraining pathways into AI-enabled research careers7. The infrastructure for scientific participation, connectivity, devices, and training, is distributed unequally. Addressing that inequality is not a charitable act. It is a strategic one.
Table 1. Key statistics on women, digital access, and science (2023–2025)
Sources: UNESCO Institute for Statistics (2024); ITU (2024); GSMA (2024); WEF (2023); AfDB (2024).
Figure 2. Women in research: Representation gap compared with gender parity target. Source: UNESCO Institute for Statistics, 2024.
Figure 3. The digital access gap: Internet usage and mobile internet gender gap in Africa. Sources: ITU, 2024; GSMA, 2024.
AI AS A SCIENTIFIC MULTIPLIER
AI AS A SCIENTIFIC MULTIPLIER
AI is transforming what science can accomplish. Machine learning helps epidemiologists forecast outbreaks faster, assists climate scientists in interpreting complex environmental datasets, and powers precision tools in agriculture and diagnostics8. When women participate in designing these tools, the results are more inclusive: language technologies that honor local dialects, diagnostic devices calibrated to diverse physiologies, and predictive platforms tuned to under-resourced settings9.
Across Africa, the evidence is visible in the work of individual scientists and entrepreneurs. Pelonomi Moiloa co-founded an AI firm in South Africa creating language tools that understand and respond in African languages, expanding technology access for millions10. In Tanzania, Dr. Neema Mduma built BakiShule, a machine-learning platform that identifies students at risk of dropping out before it happens, improving educational outcomes10. In Nigeria, Kemisola Bolarinwa's AI-powered breast-health screening device is reaching women who lack access to conventional diagnostic services11. These women are not waiting at the margins of AI. They are designing its applications.
"When women lead technology projects, outcomes better reflect community needs and produce tangible social benefits."
THE SKILLS MIX: TECHNICAL PLUS HUMAN
THE SKILLS MIX: TECHNICAL PLUS HUMAN
Thriving in an AI-driven research environment requires both technical and human-centered competencies. Technical foundations, data literacy, machine learning basics, prompt engineering, and AI auditing, form the entry point12. New roles, including AI ethics officers and algorithm auditors, demand confidence across both code and governance13.
Human-centered competencies are equally critical. Communication, creativity, ethical reasoning, and strategic thinking are not soft additions to technical training, they are core to effective science leadership14. Evidence indicates that blended training models, combining hands-on technical learning with mentorship, ethics modules, and peer networks, consistently produce stronger outcomes than technical instruction alone1415. Effective capacity building teaches women how to create, critique, and govern AI systems, not merely use them.
GENDERED RISKS AND THE NEED FOR ETHICAL SAFEGUARDS
GENDERED RISKS AND THE NEED FOR ETHICAL SAFEGUARDS
AI systems mirror their training data; where women are underrepresented or stereotyped, outputs can marginalize their needs. A UNESCO study found that popular generative AI tools consistently associate women with domestic and caring roles, while linking men with leadership and professional positions16. Investigations have revealed that some public-service AI tools downplay women's health concerns because of biased and unbalanced datasets17. These distortions are not abstractions, they affect healthcare decisions, hiring outcomes, and research funding allocations.
Global frameworks provide practical guardrails. UNESCO's Recommendation on the Ethics of Artificial Intelligence emphasizes human rights, non-discrimination, and inclusive data governance throughout the AI lifecycle18. The OECD AI Principles call for fairness, accountability, and inclusive growth19. Embedding these standards into research protocols, procurement criteria, and funding decisions helps prevent AI from amplifying structural inequalities. This is part of the "give" that produces systemic gain.
Figure 4. Skills framework for women in AI-driven science — from foundational digital literacy to leadership and ethics governance. Sources: IWPR, 2025; Pham & Suresh, 2025; Mutuma et al., 2025.
GENDERED RISKS AND THE NEED FOR ETHICAL SAFEGUARDS
GENDERED RISKS AND THE NEED FOR ETHICAL SAFEGUARDS
AI systems mirror their training data; where women are underrepresented or stereotyped, outputs can marginalize their needs. A UNESCO study found that popular generative AI tools consistently associate women with domestic and caring roles, while linking men with leadership and professional positions16. Investigations have revealed that some public-service AI tools downplay women's health concerns because of biased and unbalanced datasets17. These distortions are not abstractions, they affect healthcare decisions, hiring outcomes, and research funding allocations.
Global frameworks provide practical guardrails. UNESCO's Recommendation on the Ethics of Artificial Intelligence emphasizes human rights, non-discrimination, and inclusive data governance throughout the AI lifecycle18. The OECD AI Principles call for fairness, accountability, and inclusive growth19. Embedding these standards into research protocols, procurement criteria, and funding decisions helps prevent AI from amplifying structural inequalities. This is part of the "give" that produces systemic gain.
BUILDING INCLUSIVE PATHWAYS
BUILDING INCLUSIVE PATHWAYS
Closing the gender gap requires systemic reforms across access, training, and governance. Evidence points to several high-impact interventions:
Digital access and devices: Subsidized device programs and community internet nodes directed at women in peri-urban and rural areas reduce the foundational access barrier.56
Blended upskilling tracks: Modular cohorts combining digital literacy, AI primers, ethics modules, and mentorship tied to local universities and labs produce measurable career advances.12
Return-to-work fellowships: Technical refreshers and micro-internships for women returning from caregiving breaks prevent skill atrophy and signal institutional commitment.
Algorithmic audits and data inclusion: Partnerships between civil society and universities to audit datasets used in research and public services build accountability into AI pipelines.18
Gender-responsive policy: National strategies with measurable targets drive change at scale. Nigeria's NITDA Strategic Roadmap 2.0 mandates 40 percent women's participation across all capacity-building initiatives.20
Regional hubs and networks: Seeding women-led tech hubs that link training to entrepreneurship and research pipelines sustains momentum beyond individual programs.21
FROM PARTICIPATION TO LEADERSHIP: THE FORWARD AGENDA
FROM PARTICIPATION TO LEADERSHIP: THE FORWARD AGENDA
AI will not merely augment science, it will shape career tracks, funding priorities, and research agendas for the next decade. Moving from participation to leadership requires systemic commitment: investing in regional tech hubs, supporting women at transition points from education to employment, reforming institutional incentives to reward non-linear career paths, and adopting gender-responsive governance for AI systems.
To governments:
establish gender-responsive AI strategies with measurable targets and independent review mechanisms.
To institutions:
move from awareness-raising to structural reform in grant-making, board composition, and governance.
To individuals:
mentor, sponsor, and amplify the women already doing this work.
When women have access to devices, blended training, ethical oversight, and sustained institutional support, they do more than participate: they design the future of science. Ensuring that AI contributes to equitable scientific progress depends on closing access gaps, delivering high-quality skills training, and embedding ethical safeguards so the next generation of discovery reflects the full diversity of human perspectives. The returns belong to everyone.
"She is already at the prompt box. The question is whether the systems around her have been designed to support what comes next."
Meet the Women Driving the Vision
Meet the Women Driving the Vision
This International Women's Day, meet the women driving the mission at The Nenis Foundation, starting with the authors of today’s insights.
Explore the faces, voices, and empowering quotes of the incredible women leading our charge for digital equity.
REFERENCES
REFERENCES
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Neuravox Journal. (2025). African AI adoption trends: Investment growth and research ecosystem development. https://journal.neuravox.org/p/african-ai-adoption-trends-2025
Tech in Africa. (2025). Africa receives a small fraction of global AI funding despite rising adoption. https://www.techinafrica.com/
UNESCO Institute for Statistics. (2024). Women in science: Fact sheet. https://uis.unesco.org/en/topic/women-science
International Telecommunication Union. (2024). Measuring digital development: Facts and figures 2024. ITU Publications. https://www.itu.int/itu-d/reports/statistics/facts-figures-2024/
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World Economic Forum. (2023). The Future of Jobs Report 2023. https://www.weforum.org/reports/the-future-of-jobs-report-2023/
Leal Filho, W., & Gbaguidi, G. J. (2024). Using artificial intelligence in support of climate change adaptation in Africa: Potentials and risks. Humanities and Social Sciences Communications, 11, Article 1657. https://www.nature.com/articles/s41599-024-04223-7
Science for Africa Foundation. (2026, February 11). Women at the heart of Africa's science future, from girls in STEM to systems leadership. https://scienceforafrica.foundation/media-center/women-heart-africas-science-future-girls-stem-systems-leadership
Leading Ladies Africa. (2025). Women in AI: Profiles of African women driving innovation. https://www.leadingladiesafrica.org/
Science Nigeria. (2025). AI-powered device targets early breast health screening in Nigeria. https://sciencenigeria.com/
Institute for Women's Policy Research. (2025). Building skills to ensure women can compete in the generative AI era. https://iwpr.org/building-skills-to-ensure-women-can-compete-in-the-generative-ai-era/
Pham, T., & Suresh, T. (2025). Women and future jobs. LinkedIn–UN Women Report. https://economicgraph.linkedin.com/blog/from-ai-insights-to-impact-our-work-with-un-women-to-support-a-gender-equal-world-of-work
Mutuma, B. G., Ouma, M., & Kanyiri, B. (2025). Skill evolution in the age of AI—utilizing text analytics for skill gap analysis to prepare women for leadership roles. Communications of the IIMA, 23(1), Article 9. https://doi.org/10.58729/1941-6687.1475
Stewart, K. R. (2026). The recognition gap: How women's technical abilities remain invisible in the AI age. Journal of Computer Science and Technology Studies, 8(4). https://doi.org/10.32996/jcsts.2026.8.4.6
UNESCO. (2024). Generative AI and gender stereotypes: Study reveals alarming evidence. https://www.unesco.org/sdg4education2030/en/articles/generative-ai-unesco-study-reveals-alarming-evidence-regressive-gender-stereotypes
The Guardian. (2025, August 11). AI tools used by English councils downplay women's health issues, study finds. https://www.theguardian.com/technology/2025/aug/11/ai-tools-used-by-english-councils-downplay-womens-health-issues-study-finds
UNESCO. (2021). Recommendation on the ethics of artificial intelligence. https://www.unesco.org/en/legal-affairs/recommendation-ethics-artificial-intelligence
OECD. (2025). Policy guide for implementing transformative AI policy recommendations. OECD Publishing. https://oecd.ai/en/ai-principles
National Information Technology Development Agency (NITDA). (2024). Strategic roadmap and action plan (SRAP 2.0) 2024–2027. https://nitda.gov.ng/wp-content/uploads/2024/02/SRAP-2.O.pdf
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