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The Use of AI Research Assistants by Knowledge Management Postgraduate Students, Supervisors and Emerging Researchers – A Case Study

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#835

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unassigned (set during synthesis)
First seen
2026-07-27 13:13:53
Last seen
2026-07-27 13:13:53

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  • Semantic Scholar2026-07-27 13:13:17
    The Use of AI Research Assistants by Knowledge Management Postgraduate Students, Supervisors and Emerging Researchers – A Case Study

    The growing prevalence of artificial intelligence (AI), generative AI (GenAI), and AI-mediated research tools raise concerns around their implications for postgraduate research and education. Postgraduate students starting their research journey may believe that AI-powered research tools enhance efficiency and fluency. This could be the case, but there is a probability that its uninformed and indiscriminate use may exacerbate second-order articulation gaps. This may be evident during transitions from Honours to advanced postgraduate research. Whereas,  independent analytical and creative problem solving, conceptualisation, methodological justification, and original contribution are research skills required on postgraduate level, an over-reliance on generative AI and other AI research assistants  may deter this cognitive development and result in limited epistemic understanding. When combined with heuristic information behaviour, such as satisficing, superficial processing, cognitive overload, information avoidance, and confirmation bias of AI use may result in  compromised research quality, originality, and ethical engagement. Although articulation gaps are well theorised in relation to the school-to-university transition, limited research addresses their recurrence within higher education itself in South Africa, particularly between NQF 7–8 and NQF 9–10. These gaps are often misinterpreted as motivational deficits rather than systemic misalignments that privilege procedural competence over deep scholarly identity formation. This mixed-method case study investigates how postgraduate students and supervisors perceive and manage AI use. Findings aim to inform supervision practices, and student support that scaffold ethical AI integration, strengthen research literacies, and support authentic researcher identity development. The value of the study lies in adding new knowledge to an unexplored area and to propose steps to close the current gap. The study involves a case studies.