Thought Leadership: Navigating the Challenges of Artificial Intelligence Integration in Global South Higher Education: A Technocritical Approach
Post by Eduvos, November 15, 2024.
By Dr Miné de Klerk and Dr Nyx McLean.
The rapid integration of Generative Artificial Intelligence (GenAI) into higher education presents both opportunities and challenges, especially in Global South contexts. The integration of AI in higher education has been met with optimism about its ability to support diverse learning needs. However, existing power imbalances between the Global North and South mean that GenAI tools, often developed in the North, may not adequately reflect the lived experiences of our students. This can result in forms of digital colonisation, where AI tools perpetuate Western-centric narratives and exclude contextually relevant content. This paper underscores the need for more critical engagement with AI, particularly in underrepresented contexts.
A key theoretical contribution of this paper is its advocacy for a technocritical approach, which builds on critical theory and technorealism. It challenges the reductive nature of techno-optimism and techno-pessimism, which often overlook the complexity of power relations in technology adoption. Technocriticality intentionally interrogates the socio-political dimensions of technology. GenAI tools are not neutral, but socially constructed and shaped by specific societal biases. By adopting this perspective, the paper reveals AI’s evolving design and usage as a “terrain of struggle,” where technologies developed primarily in the Global North influence the education of students in the Global South, often without accounting for local contexts. This approach calls for a participatory and collaborative approach to the development and adoption of emerging AI tools, especially in the Global South.
Using Eduvos, a South African private Higher Education Institution (HEI), as a case study, this research examines the institution’s response to these challenges. In 2024, Eduvos established an AI committee to provide guidelines on the responsible and critical use of GenAI. The committee facilitated virtual discussions to address lecturers’ and students’ concerns around GenAI’s use in teaching and learning. It also guided the ‘AI-responsive’ design of take-home assessments for 87 modules. A technocritical framework structured these conversations, guidelines, and reflections, prompting questions about how human-machine collaborative learning can both erode and enhance dialogic learning.
This study contributes both theoretically and empirically to the growing discourse on AI in higher education. Theoretically, it advocates for a technocritical stance foregrounding the ethical complexities and pedagogical implications of GenAI in Global South contexts. Empirically, it presents practical examples of how institutions can design assessments and curricula that incorporate GenAI in ways that support, rather than hinder, critical engagement and dialogic learning.
