Artificial intelligence as a reflexive collaborator in graduate studies supervision

Transformation in Higher Education

 
 
Field Value
 
Title Artificial intelligence as a reflexive collaborator in graduate studies supervision
 
Creator Brown, Anthony Rossouw, Jane
 
Subject Education; Sociology; Computer Literacy AI-augmented supervision; doctoral research; experiential learning; ethical AI integration; graduate studies; augmented experiential learning
Description The incorporation of generative artificial intelligence (AI) in doctoral supervision signifies a transformative evolution in higher education. This has been significant, particularly within intricate and emotionally complex research such as sexuality studies. This reflective, collaborative autoethnographic study investigates the experiences of a doctoral student and her supervisor. They explored AI generative tools to enhance research processes, quality of supervision and intellectual inquiry. Anchored in Kolb’s Experiential Learning Theory and reconceptualised through an augmented experiential learning framework, the study elucidates how AI tools like ChatGPT encourage critical thinking. These tools were also used to foster methodological innovation and facilitate ethical reflexivity. Through iterative engagements, AI supported the formulation of sophisticated research questions and bolstered academic writing. It also aided emotional resilience in traversing heteronormative and interdisciplinary landscapes. The study highlights the evolving role of supervisors, not as gatekeepers but as collaborators in AI-informed learning. Significant emphasis was placed on prompt engineering, scholarly scrutiny and academic integrity. Ethical guidelines and rigorous documentation practices ensured a responsible AI application without sacrificing originality.Contribution: The findings reveal that AI-augmented supervision promotes deeper theoretical engagement and enhances self-directed learning. It also introduces new pedagogical possibilities for complex research endeavours. Nonetheless, the study also underscores the challenges of bias, overreliance and contextual insensitivity inherent in AI outputs. By suggesting actionable strategies for ethical integration, this paper contributes to emerging global discussions on AI in higher education. It presents a framework for inclusive, transformative and contextually aware supervision practices.
 
Publisher AOSIS
 
Contributor
Date 2026-02-07
 
Type info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion — Critical Reflexivity
Format text/html application/epub+zip text/xml application/pdf
Identifier 10.4102/the.v11i0.657
 
Source Transformation in Higher Education; Vol 11 (2026); 8 pages 2519-5638 2415-0991
 
Language eng
 
Relation
The following web links (URLs) may trigger a file download or direct you to an alternative webpage to gain access to a publication file format of the published article:

https://thejournal.org.za/index.php/thejournal/article/view/657/1122 https://thejournal.org.za/index.php/thejournal/article/view/657/1123 https://thejournal.org.za/index.php/thejournal/article/view/657/1124 https://thejournal.org.za/index.php/thejournal/article/view/657/1125
 
Coverage South Africa — —
Rights Copyright (c) 2026 Anthony Brown, Jane Rossouw https://creativecommons.org/licenses/by/4.0
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