Generative Artificial Intelligence (GenAI) has created both opportunities and challenges for postgraduate supervision, requiring new approaches to evaluating doctoral learning and intellectual ownership. GenAI tools can support literature reviews, coding, data analysis, academic writing, and viva preparation, enhancing productivity and learning. Yet, concerns have been raised about cognitive engagement, ethical use, and students’ ability to understand, justify, and defend their research.
A systematic approach that incorporates responsible and transparent use of GenAI throughout the doctoral lifecycle may be the way forward, a tool for strengthening critical thinking rather than merely improving efficiency. Criteria focused on transparency, verification, attribution, and AI literacy for both students and supervisors are essential.
Effective research and supervision should move beyond simplistic notions of AI detection or prohibition, and instead position AI literacy, critical reflection, verification, and scholarly judgement at the centre of postgraduate research training. The measurement of developmental maturity levels that guide students from uncritical AI use toward reflexive and ethically grounded AI stewardship, while simultaneously recognising the need for supervisors themselves to develop AI capability and shared supervisory norms is important.
This special session delves into the African context of GenAI in research and supervision, providing a framework for teaching and learning in higher education and a practical model for navigating supervision and academic integrity in an increasingly AI-mediated academic environment. This framework is based on the continued development of the published guiding rubric for doctoral supervision in Statistics.