PERSONALISED LEARNING WITH ARTIFICIAL INTELLIGENCE IN SOUTH AFRICA: A QUALITATIVE SYNTHESIS OF OPPORTUNITIES AND BARRIERS

In South Africa, a developing nation marked by deep inequalities, educational disparities remain one of the country’s most urgent challenges. Although digital access has expanded, with approximately 50.8 million internet users (79% penetration) recorded in early 2025, the rural–urban gap persists: only 10% of rural households have internet access compared to 66% in urban areas. This digital divide is compounded by infrastructural shortcomings, limited digital literacy, and systemic inequalities that affect marginalised learners. Artificial intelligence and adaptive learning technologies offer promising pathways towards more personalised, inclusive, and effective education. This paper adopts a qualitative, secondary data analysis of policy reports, government statistics, and scholarly literature to examine how AI-enabled adaptive learning systems might address South Africa’s entrenched inequities, particularly in low-income and rural areas. Guided by critical pedagogy and connectivism, the analysis explores both opportunities and risks, including algorithmic bias, surveillance, affordability, and cultural exclusion. Findings reveal four key patterns. First, adaptive learning systems show potential to individualise instruction and support multilingual learning, which is crucial in South Africa’s linguistically diverse education system. Second, these technologies could reduce lecturer workload in ODeL settings and enhance student retention if integrated responsibly. Third, risks persist where commercial AI tools are unaffordable, poorly contextualised, or reliant on urban infrastructure, which excludes rural learners. Fourth, without deliberate policy safeguards, AI may reinforce existing biases, widen class and gender inequalities, and undermine local pedagogical traditions. The study recommends targeted infrastructure investment in rural provinces such as the Eastern Cape and Limpopo; embedding digital literacy and AI awareness into school and community curricula; developing locally trained AI models that integrate South African languages; and fostering partnerships between government, universities, and private technology firms. Crucially, recommendations call for aligning AI adoption with South Africa’s constitutional commitments to equity, inclusion, and social justice, ensuring that adaptive learning is not just technologically efficient but socially transformative. The paper contributes by situating technological innovation within the lived educational and cultural realities of the Global South.

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Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-13
DOI
https://doi.org/10.5281/zenodo.19008426
Primary Topic
E-Learning and COVID-19
Type
article
Field-Weighted Citation Impact
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article

PERSONALISED LEARNING WITH ARTIFICIAL INTELLIGENCE IN SOUTH AFRICA: A QUALITATIVE SYNTHESIS OF OPPORTUNITIES AND BARRIERS

Tandiwe Mdlungu
Zenodo (CERN European Organization for Nuclear Research)
E-Learning and COVID-19
article

PERSONALISED LEARNING WITH ARTIFICIAL INTELLIGENCE IN SOUTH AFRICA: A QUALITATIVE SYNTHESIS OF OPPORTUNITIES AND BARRIERS

Tandiwe Mdlungu
article en

Abstract

In South Africa, a developing nation marked by deep inequalities, educational disparities remain one of the country’s most urgent challenges. Although digital access has expanded, with approximately 50.8 million internet users (79% penetration) recorded in early 2025, the rural–urban gap persists: only 10% of rural households have internet access compared to 66% in urban areas. This digital divide is compounded by infrastructural shortcomings, limited digital literacy, and systemic inequalities that affect marginalised learners. Artificial intelligence and adaptive learning technologies offer promising pathways towards more personalised, inclusive, and effective education. This paper adopts a qualitative, secondary data analysis of policy reports, government statistics, and scholarly literature to examine how AI-enabled adaptive learning systems might address South Africa’s entrenched inequities, particularly in low-income and rural areas. Guided by critical pedagogy and connectivism, the analysis explores both opportunities and risks, including algorithmic bias, surveillance, affordability, and cultural exclusion. Findings reveal four key patterns. First, adaptive learning systems show potential to individualise instruction and support multilingual learning, which is crucial in South Africa’s linguistically diverse education system. Second, these technologies could reduce lecturer workload in ODeL settings and enhance student retention if integrated responsibly. Third, risks persist where commercial AI tools are unaffordable, poorly contextualised, or reliant on urban infrastructure, which excludes rural learners. Fourth, without deliberate policy safeguards, AI may reinforce existing biases, widen class and gender inequalities, and undermine local pedagogical traditions. The study recommends targeted infrastructure investment in rural provinces such as the Eastern Cape and Limpopo; embedding digital literacy and AI awareness into school and community curricula; developing locally trained AI models that integrate South African languages; and fostering partnerships between government, universities, and private technology firms. Crucially, recommendations call for aligning AI adoption with South Africa’s constitutional commitments to equity, inclusion, and social justice, ensuring that adaptive learning is not just technologically efficient but socially transformative. The paper contributes by situating technological innovation within the lived educational and cultural realities of the Global South.

Zenodo (CERN European Organization for Nuclear Research)
University of South Africa (ZA)
Quality Education
Openalex Percentile: Top 22%
E-Learning and COVID-19
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