AI Usage Gap Between Developed and Developing Countries in Education
- Brainz Magazine
- Aug 19
- 6 min read
Danisa Abiel is well known for her practical solutions to teaching and learning in the advancing fields of Science, Technology, Engineering, and Mathematics (STEM). She is the founder of International Teaching Learning Assessment Consultants and Online Schools (ITLACO). She has authored 20 editions of her newsletter, "The Educator's Diaries," on LinkedIn.
Based on the most recent findings, the gap in AI usage between developed and developing countries is significant, primarily due to disparities in investment, infrastructure, talent, and expertise. Developed nations, particularly the US and China, lead in AI research, development, and adoption. In contrast, many developing countries face challenges in accessing resources and skilled personnel needed to leverage AI's potential. What are the key factors that are leading to the gap in AI development?
Key dimensions that influence the gap
1. Automation & job exposure
In advanced economies, around 60% of jobs face high exposure to AI-driven transformation, compared to 42% in emerging markets and just 26% in low-income countries, Centre For Global Development.
Paradoxically, while only 5.5% of jobs in developing countries are at risk of automation, the figure is much higher, 26.6%, in developed economies, according to IEEE Technology and Society.
This indicates not just lower adoption but also differences in sectoral structure and resilience.
2. Technology diffusion & global value chains
AI R&D and infrastructure (like advanced chips and cloud systems) are dominated by developed nations, keeping developing countries primarily in lower-value roles within the global value chain, according to the World Economic Forum.
Studies show diffusion rates of AI remain significantly lower in low and middle-income countries, though some narrowing of the gap is occurring.
3. Skills & labour market challenges
Developing countries experience a "double vulnerability": heavy concentration of low-skill jobs coupled with higher automation risk and lower AI readiness.
Skill gaps, brain drain, and fewer AI-trained professionals (e.g., Brazil had only 4,429 specialists as of 2019) exacerbate the divide.
4. Infrastructure & digital divide
Unequal access to reliable internet, electricity, and digital tools, especially in rural areas, hampers AI deployment, particularly in sectors like agriculture and education, Wikipedia.
5. Governance & global inclusion
Many developing nations are underrepresented in AI governance; around 118 countries, mainly from the Global South, are not present in key AI regulatory discussions at the UN Trade and Development (UNCTAD).
UN-led efforts aim to bridge this gap, promoting equal access to AI tools and encouraging inclusive AI collaboration (apnews.com).
AI’s potential in developing regions
Despite the challenges, AI has enabled creative and impactful applications across developing regions.
In Africa, tools are being developed for tailored education, medical diagnostics, weather forecasting, and crop disease detection. Examples include Google's Project Relate, which helps people with non-standard speech in Ghana; Kenyan farmers benefit from AI-powered crop disease identification, Financial Times. Nonprofits like Education Above All, in collaboration with MIT, Harvard, and UNDP, have launched AI literacy programs (e.g., Digi‑Wise) and culturally adapted learning tools like Ferby, reaching millions in India, Business Insider.
Summary comparison: AI use between developed and developing countries
Dimension | Developed Countries | Underdeveloped / Developing Countries |
AI Exposure | High (60% of jobs exposed) | Lower (26%) but in more vulnerable sectors |
R&D & Infrastructure | Concentrated; leads in AI tech and hardware | Dependent; limited innovation capacity |
Skills & Talent | Stronger workforce with an AI-ready talent pool | Scarcity of specialists; significant brain drain |
Infrastructure | Robust digital infrastructure | Inadequate internet and electricity, especially in rural areas |
AI Applications | Broad enterprise and consumer-level use | Often localised and niche, but showing growth |
Policy & Governance | Actively shaping global AI norms | Often excluded from policymaking, increasing international support |
The gap in AI usage in education between developed and developing countries is significant, with developed nations leading in AI integration and developing nations facing numerous barriers. Developed countries are actively integrating AI into higher education for personalised learning, virtual labs, and support systems, while developing countries often lack the necessary infrastructure, expertise, and financial resources. This disparity is further exacerbated by the digital divide, with many schools in developing countries lacking basic internet access and resources, hindering their ability to adopt AI technologies.
Let us look at an education-focused AI gap, spotlighting Africa, Asia, and South America. Though, as shown by the graph above, Africa has the largest gap.
AI in education by region
Africa
Shortage of Teachers
Africa is facing a severe teacher shortage, particularly in sub-Saharan Africa, with an estimated 15 million additional teachers needed by 2030. This shortage is driven by a combination of factors, including increased student enrolment, lack of resources, slow teacher recruitment, high attrition rates (due to brain drain), and poor working conditions.
Innovative AI tools to bridge gaps:
Kwame for Science, a bilingual AI teaching assistant, delivers curated lessons and past exam content across West Africa. Deployed to 750 users in 32 out of 54 countries, it achieved 87% accuracy in delivering helpful answers, according to Business Insider.
Research tools like SuaCode, AutoGrad, and Brilla AI offer coding support and automated grading, facilitating scalable learning despite infrastructure challenges.
AI chatbots outperform web searches: In Sierra Leone, teachers using an AI chatbot via messaging apps found it more relevant and cost-effective than web searches. AI responses consumed 87% less data.
Policy and capacity building: Programs emphasise mentoring and AI leadership development to nurture a skilled workforce aligned with Africa's development goals.
Risks and infrastructure hurdles: Limited internet, electricity, teacher training, and policy frameworks pose ongoing challenges, according to Brookings.
Asia
Diverse development: Countries such as India and Thailand are using AI to identify at-risk students and enhance language learning, but wide gaps persist between rural and urban access, according to Brookings.
Nonprofit initiatives for inclusiveness: The Digi-Wise program, developed by Education Above All, MIT, Harvard, and UNDP, includes Ferby, a generative AI chatbot delivering localised, offline-first learning resources to millions in India. An effort to combat linguistic and infrastructural barriers, Business Insider.
South America
Infrastructure bridging in schools: Colombia’s “Computadores Para Educar” initiative equips rural teachers with technology and training to integrate ICT into education, significantly boosting adoption and digital capacity in underserved areas.
Summary table: AI in education by region
Region | Key AI Education Initiatives | Main Challenges |
Africa | Kwame for Science, AI teaching tools (e.g., SuaCode), chatbots for teachers, leadership training | Power/internet gaps, teacher shortages, funding, policy frameworks |
Asia | AI-based tutoring, risk detection, Digi Wise with Ferby for local, offline learning | Language diversity, rural disparity, access to tech |
South America | Teacher ICT training via national programs (e.g., Colombia) | Infrastructure inequality, sectoral access, and sustained digital access |
Final thoughts & opportunities
Bridging this gap generally calls for:
Investment in skills, education, and infrastructure: Particularly in developing countries.
Inclusive global governance: This should ensure all nations have a voice in AI policy and benefit from technological leaps.
Support for locally tailored AI solutions: This should include language adaptation and cultural contextualisation.
Collaborative initiatives: From international bodies, NGOs, and tech firms. This is to foster equitable AI adoption.
Education in the Global South and East stands to benefit significantly from AI, but realising that potential requires:
Localised tools (e.g., bilingual AI assistants, offline-capable platforms).
Teacher training and systemic support, not just tech introduction.
Reduced technology barriers through low‑bandwidth and SMS-based delivery.
Inclusive governance and regional leadership, so AI in schools reflects local needs and languages.
Read more from Danisa Abiel
Danisa Abiel, Teaching and Learning Consultant and Founder
Danisa Abiel is a passionate leader and educator of Biology, Physics, and Chemistry with over thirty years of experience. Witnessing firsthand how students find science subjects challenging, she founded an online school to support students in different situations to improve and excel in science. Her greatest appreciation is to have all children receiving the best science education regardless of where they are in the world.
References:
Education Above All. (2025, July). AI models aren't made equal. Some nonprofits are creating their own tools instead. Business Insider.
Financial Times. (2024, October). Can AI help Africa close the development gap?
Brookings Institution. (2023, July). AI in the Global South: Opportunities and challenges towards more inclusive governance.
World Bank. (2023). Tipping the scales: AI’s dual impact on developing nations. Digital Development Blog.
Arxiv.org. (2023). Kwame for Science: A bilingual AI education assistant.
Arxiv.org. (2024). Scaling up AI for education in the Global South: A multi-platform deployment approach.
Arxiv.org. (2025). Evaluating the effectiveness of chatbot-based teacher support in Sierra Leone.
Euclid University. (2023). Developing AI leadership capacity in Africa: Exploring the role of education, training, and mentorship programs. International Research and Policy Journal.
Wikipedia contributors. (2024). Digital divide in Colombia. In Wikipedia, The Free Encyclopedia.
