Generative AI and Digital Inequality in South African Higher Education: Students’ Experiences of Access and Meaningful Use at a Rural University

This study examined the conditions that enable or constrain undergraduate students’ access to, and meaningful use of, generative artificial intelligence (GenAI) at a rural South African university. Using an interpretivist qualitative case study design, researchers conducted semi-structured, face-to-face interviews with 12 undergraduate students with prior experience using GenAI for academic purposes. Data were analysed thematically following Braun and Clarke’s ix-phase approach. Four interconnected conditions shaped students’ experiences: device and connectivity reliability, the affordability of sustained data access, artificial intelligence (AI) literacy, and institutional infrastructure and training. Students consistently perceived GenAI as useful, but their capacity to realise that usefulness depended on resources and competencies that were unevenly distributed. The study uses this gap to extend TAM’s construct of perceived usefulness with a distinct construct of realised usefulness, and this study argues that generative AI introduces a literacy demand, critical evaluation of machine-generated, plausible-sounding but sometimes inaccurate output. The study contributes to digital-inequality scholarship by extending the access–skills–benefits distinction into the generative AI context and by identifying the gap between perceived and realised usefulness as a mechanism through which existing inequalities may be reproduced. The study recommends that institutional responses combine connectivity, affordable access, structured AI literacy instruction, and explicit academic guidance, rather than treating any one of these as sufficient on its own.