<b>AI in Rural Classrooms: Challenges and Perspectives from South African Educators</b>
Despite growing interest in artificial intelligence (AI) in South African education, limited research has examined how rural educators perceive and navigate AI integration. This study explores educators' perspectives, adaptive strategies, and lived realities in under-resourced rural schools. Eight educators from Eastern Cape, Limpopo, Mpumalanga, and North-West provinces were purposefully selected. Data were collected through written responses and semi-structured online interviews, and were analyzed thematically. Ethical safeguards included informed consent, pseudonyms, and confidentiality. Findings reveal that AI integration is hindered by inadequate digital infrastructure, unreliable connectivity, and limited access to devices. Educators also face insufficient digital literacy and a lack of professional development, leaving them underprepared for AI-supported teaching. Weak institutional support and gaps between policy and practice further constrain adoption. Moreover, AI tools often remain linguistically and culturally misaligned, reducing learner engagement. Equity and ethical concerns—access, data privacy, and algorithmic bias—raise the risk of exacerbating educational inequalities rather than reducing them. This study underscores the need for targeted investment in digital infrastructure, contextualized teacher training, and inclusive AI design that reflects local languages and cultures. The findings extend beyond South Africa, contributing to global debates on equitable AI adoption in education across the South.
Authors
- Oratilwe Penwell Mokoena (ORCID: https://orcid.org/0000-0002-0746-1198)
- Solly Matshonisa Seeletse (ORCID: https://orcid.org/0000-0001-7728-3748)
Institutions
- Tshwane University of Technology (ZA)
- Sefako Makgatho Health Sciences University (ZA)
Publication Details
- Journal
- International Journal of Current Educational Studies
- Published
- 2025-09-03
- DOI
- https://doi.org/10.46328/ijces.199
- Citations
- 4
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 6.77