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Postgraduate research supervision constitutes a cornerstone of academic scholarship, catalyzing epistemological advancement, innovation, and socio-economic transformation. However, systemic challenges—including inconsistent supervisory practices, elongated completion timelines, and limited formal training for supervisors—demand innovative and contextually responsive pedagogical interventions. This study critically examines the integration of Generative Artificial Intelligence (GenAI) into doctoral supervision training within South African higher education, with a specific focus on the University of KwaZulu-Natal. Employing a qualitative methodological framework, the research integrates discourse analysis, a comprehensive critical review of 450 peer-reviewed scholarly sources, and empirical data from focus group engagements involving 50 experienced academics during a doctoral supervision workshop convened in October 2024. The findings illuminate key supervisory competencies such as global academic engagement, reflexivity, and the development of both research and professional capabilities. Concept mapping of the supervisory process identified five interdependent thematic clusters: supervisor expertise, supervision as pedagogical praxis, learner epistemological orientation, psychosocial support structures, and collaborative knowledge construction. Additionally, the study highlights supervisory cognitive styles, best practices, and the imperative for quality assurance frameworks to underpin supervisory excellence. Virtual supervision is shown to be a viable model when operationalized through structured GenAI-supported protocols and interactive digital tools. The study proposes a GenAI-informed framework to standardize supervision practices, enhance quality assurance, and optimize doctoral mentorship. Policy recommendations emphasize AI-driven training, inclusive digital strategies, and alignment with national research priorities. Ultimately, the integration of GenAI into doctoral supervision offers an ethically grounded, future-focused approach to advancing excellence, equity, and innovation within postgraduate education.

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Generative AI Applications to Doctoral Research Supervision Training at a South African Higher Education Institution

  • 会议论文

  • 发表日期：

    2026年

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  • G. Kabanda

摘要

Postgraduate research supervision constitutes a cornerstone of academic scholarship, catalyzing epistemological advancement, innovation, and socio-economic transformation. However, systemic challenges—including inconsistent supervisory practices, elongated completion timelines, and limited formal training for supervisors—demand innovative and contextually responsive pedagogical interventions. This study critically examines the integration of Generative Artificial Intelligence (GenAI) into doctoral supervision training within South African higher education, with a specific focus on the University of KwaZulu-Natal. Employing a qualitative methodological framework, the research integrates discourse analysis, a comprehensive critical review of 450 peer-reviewed scholarly sources, and empirical data from focus group engagements involving 50 experienced academics during a doctoral supervision workshop convened in October 2024. The findings illuminate key supervisory competencies such as global academic engagement, reflexivity, and the development of both research and professional capabilities. Concept mapping of the supervisory process identified five interdependent thematic clusters: supervisor expertise, supervision as pedagogical praxis, learner epistemological orientation, psychosocial support structures, and collaborative knowledge construction. Additionally, the study highlights supervisory cognitive styles, best practices, and the imperative for quality assurance frameworks to underpin supervisory excellence. Virtual supervision is shown to be a viable model when operationalized through structured GenAI-supported protocols and interactive digital tools. The study proposes a GenAI-informed framework to standardize supervision practices, enhance quality assurance, and optimize doctoral mentorship. Policy recommendations emphasize AI-driven training, inclusive digital strategies, and alignment with national research priorities. Ultimately, the integration of GenAI into doctoral supervision offers an ethically grounded, future-focused approach to advancing excellence, equity, and innovation within postgraduate education.