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Global South

● high · created 2026-08-18 · updated 2026-08-20 · global-south, equity-in-ai-education, generative-ai, higher-ed, k-12, culturally-relevant-pedagogy, ai-literacy

The Global South refers to countries in Africa, Asia, Latin America, and Oceania that are often economically, politically, and historically marginalized relative to the Global North. In AI in education research, Global South contexts are increasingly recognized as underrepresented in the evidence base, yet they raise distinctive questions about equity, cultural relevance, resource constraints, and the epistemic dominance of Western, Anglophone training data.

Why It Matters for AIED

Mainstream AI and educational-technology research has historically been dominated by Western, English-language datasets and institutional contexts. This creates two problems: (1) AI systems trained on such data may underperform or misrepresent learners in Global South settings, and (2) evaluation benchmarks built in the Global North may not reflect the educational realities, languages, or knowledge traditions of other regions. Research from Global South contexts in this wiki spans culturally grounded datasets, benchmarks, and technology-adoption studies, with implications for AI Literacy and higher and K-12 education.

Applications in the Wiki

  • Culturally grounded data and benchmarks: IKS-Instruct provides a multilingual Indian Knowledge Systems instruction dataset; NSMQ Riddles introduces a Ghana-based STEM benchmark, one of the first Global South educational evaluation datasets.
  • Contextual adoption: Asag & Al Mamun model GenAI adoption among Bangladeshi engineering students, and ConnectED deploys a curriculum-aligned lesson-planning system for Vietnamese education.
  • Epistemic marginalization: Tali-Otmani argues that Western-centric training data marginalizes non-Western and disability-centered knowledges — connecting Global South concerns to Equity In AI Education and Culturally Relevant Pedagogy.

Implications

Attending to Global South contexts requires moving beyond assuming Western models and benchmarks transfer directly. It calls for locally grounded datasets, culturally relevant Pedagogy, community-centered evaluation standards, and research that treats learners' lived and community epistemologies as authoritative — aligning with frameworks like community-based AI learning and technology-acceptance research adapted to local conditions.

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