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The Council on Higher Education (CHE)’s report on the doctoral programme review states   that offering postgraduate programmes seems to be a challenge to some South African universities. Amongst others, the report expresses that postgraduate supervision at universities suffers from many challenges, some of which result in students being delayed or not completing their studies. Underpinned by Archer’s (1998) theory of the interplay between structure, culture, and agency (support programmes), the study seeks to examine factors that hinder postgraduate students’ completion of their dissertations and theses. The study adopted narrative research where the qualitative design was used. Postgraduate students (Master and Doctoral) who have registered for these programmes repeatedly in the university served as participants in the study. The purposive sample was used to sample eight postgraduate students who have frequently registered for more than the required residency period. Semi-structured questions were formulated to guide data collection. Data was collected through a focus group of eight Master and Doctoral students. Collected data were analysed using the content analysis method. The study found that the university does not have adequate (or weak) supporting structures for students to complete their degrees in record time.

The increasing use of artificial intelligence (AI) tools in higher education has reshaped how students engage with academic research, particularly at Honours level, where students transition from structured undergraduate learning to independent postgraduate research. Guided by Self‑Regulated Learning (SRL) theory, this study examines how reliance on AI by Honours research students influences the supervisor-supervisee relationship, the quality of research produced, and the extent of knowledge gained through the research process. SRL theory emphasises learners’ ability to plan, monitor, and reflect on their own learning, which is central to successful postgraduate research development. This study adopts a conceptual and literature‑based review methodology, drawing on peer‑reviewed studies on AI use in higher education, literature on postgraduate supervision, and academic integrity and research skills development frameworks, with specific attention to Honours‑level contexts. The documents were analysed using thematic content analysis, focusing on self‑regulatory behaviours, supervisory interaction, research skill development, and learning depth. The findings suggest that limited and transparent use of AI can support honours students by reducing surface‑level cognitive load and assisting with language clarity. However, excessive reliance on AI may undermine key self‑regulation processes, weaken critical thinking, and reduce meaningful engagement with supervisors. Supervisors may struggle to accurately assess students’ understanding, potentially straining trust and feedback processes. The review further indicates that over‑reliance on AI risks producing research that is technically adequate but conceptually shallow, with reduced long‑term knowledge acquisition. The study concludes that clear supervision practices and explicit guidance on responsible AI use are necessary to ensure that AI supports, rather than replaces, the self‑regulated learning essential for high‑quality Honours‑level research.

The increasing use of artificial intelligence (AI) tools in higher education has reshaped how students engage with academic research, particularly at Honours level, where students transition from structured undergraduate learning to independent postgraduate research. Guided by Self‑Regulated Learning (SRL) theory, this study examines how reliance on AI by Honours research students influences the supervisor-supervisee relationship, the quality of research produced, and the extent of knowledge gained through the research process. SRL theory emphasises learners’ ability to plan, monitor, and reflect on their own learning, which is central to successful postgraduate research development. This study adopts a conceptual and literature‑based review methodology, drawing on peer‑reviewed studies on AI use in higher education, literature on postgraduate supervision, and academic integrity and research skills development frameworks, with specific attention to Honours‑level contexts. The documents were analysed using thematic content analysis, focusing on self‑regulatory behaviours, supervisory interaction, research skill development, and learning depth. The findings suggest that limited and transparent use of AI can support honours students by reducing surface‑level cognitive load and assisting with language clarity. However, excessive reliance on AI may undermine key self‑regulation processes, weaken critical thinking, and reduce meaningful engagement with supervisors. Supervisors may struggle to accurately assess students’ understanding, potentially straining trust and feedback processes. The review further indicates that over‑reliance on AI risks producing research that is technically adequate but conceptually shallow, with reduced long‑term knowledge acquisition. The study concludes that clear supervision practices and explicit guidance on responsible AI use are necessary to ensure that AI supports, rather than replaces, the self‑regulated learning essential for high‑quality Honours‑level research.

The Council on Higher Education (CHE)’s report on the doctoral programme review states   that offering postgraduate programmes seems to be a challenge to some South African universities. Amongst others, the report expresses that postgraduate supervision at universities suffers from many challenges, some of which result in students being delayed or not completing their studies. Underpinned by Archer’s (1998) theory of the interplay between structure, culture, and agency (support programmes), the study seeks to examine factors that hinder postgraduate students’ completion of their dissertations and theses. The study adopted narrative research where the qualitative design was used. Postgraduate students (Master and Doctoral) who have registered for these programmes repeatedly in the university served as participants in the study. The purposive sample was used to sample eight postgraduate students who have frequently registered for more than the required residency period. Semi-structured questions were formulated to guide data collection. Data was collected through a focus group of eight Master and Doctoral students. Collected data were analysed using the content analysis method. The study found that the university does not have adequate (or weak) supporting structures for students to complete their degrees in record time.