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Cited by 22Doctoral Education Challenges and SolutionsArtificial Intelligence in EducationEthics and Social Impacts of AI

The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index; none of the 44 sources its claims cite is checked for support yet.

Redefining the role of supervisors in the era of artificial intelligence: implications for hybrid postgraduate research governance

Open access
Omodan, Bunmi Isaiah·2025·Cogent Education
DOI 10.1080/2331186x.2025.2536534
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Abstract

The rapid integration of artificial intelligence (AI) into academic research is transforming the supervisory landscape in postgraduate studies, challenging the traditional mentor-apprentice model that has long-defined research governance. This article examines the implications of AI on supervisory roles, focusing on how algorithmic systems are reshaping mentorship, academic oversight, and ethical accountability. AI tools enhance efficiency but raise concerns about relational dynamics and ethical accountability, epistemic authority, and the potential erosion of human-centred mentoring qualities such as empathy, creativity, and intuition. Drawing on theoretical frameworks and recent case studies, this paper interrogates the power dynamics introduced by AI, exploring whether it democratises access to supervision or exacerbates hierarchical inequalities in knowledge production. It also evaluates the ethical and governance challenges posed by AI, including algorithmic biases, accountability gaps, and intellectual property dilemmas. The study proposes a hybrid model of supervision that combines AI-assisted tools with reflexive human oversight, ensuring ethical integrity and fostering critical engagement in research processes. This article advocates for the development of adaptive governance frameworks that integrate AI while preserving the pedagogical and ethical foundations of postgraduate education. It highlights AI’s potential as both a disruptor and enabler in shaping the future of academic mentorship and research governance.

Overview

CoChat Summary

TL;DR

This conceptual article argues that AI is reshaping postgraduate supervision and proposes a hybrid model combining AI-assisted tools with reflexive human oversight. It advocates adaptive governance frameworks to preserve ethical accountability, empathy, and critical engagement while leveraging AI's efficiency.

Key findings

  • AI integration creates a tension between efficiency gains (automated feedback, plagiarism detection, data analysis, project dashboards) and the erosion of human-centred mentorship qualities such as empathy, creativity, and intuition.
  • The paper proposes hybrid supervision models in which AI handles routine tasks (grammar checks, preliminary data analyses, reference management) while human supervisors provide emotional intelligence, ethical discernment, and contextual guidance.
  • A case study of a global university consortium using Turnitin for dissertation drafts found recurring false positives: text with proper citations was flagged as "high similarity" because it mirrored standard methodological terminology, causing student anxiety and prompting the institution to issue clearer guidelines requiring human review.
  • The paper identifies governance gaps—algorithmic bias, accountability gaps, data privacy, and intellectual property dilemmas—and recommends ethical/legal frameworks (adapting the Declaration of Helsinki and GDPR), reflexive oversight mechanisms including AI auditing committees, and mandatory AI literacy training.

Methods

  • Conceptual/theoretical article; the data availability statement confirms "no new data were created or analysed in this study."
  • Draws on three theoretical frameworks: Post-Humanism and AI Agency (Braidotti), Foucault's Power-Knowledge Dynamics, and Reflexivity for Ethical Accountability (Day).
  • Uses an illustrative case study of Turnitin deployment for dissertation draft checking to demonstrate AI's benefits and pitfalls in supervisory practice.

Limitations

  • The paper's own "Potential limitations" section notes that over-reliance on AI can erode student autonomy and critical judgment, that AI systems remain susceptible to training-data biases, and that supervisors may become complacent, substituting genuine mentorship with cursory reviews of AI-generated summaries.
  • Evident from the text: the article is entirely conceptual with no empirical data, sample, or primary research; the case study is presented illustratively without reported methodology or sample details.
  • The paper itself calls for future empirical studies and pilot studies to assess hybrid supervision models in real-world settings, acknowledging these have not yet been conducted.

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Integrity signals

Registry

Crossref · 10.1080/2331186x.2025.2536534

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 Valid · 3 Not valid · 40 Not checked · 117 Own results and interpretations

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  • Probably not a substantial claim. 122
  • It may state two separate claims as one. 48

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Citations

Cites

Show all 27 references

Research works printed but not linked: 1

  1. Kumar, A., & Nagar, D. K. (2024). AI-Based Language Translation and Interpretation Services: Improving accessibility for visually impaired students. In S. Ahmad, M. I. Hussain, & M. M. R. K. Kushwaha (Eds.), Transforming learning: The power of educational (pp. 178–190). Blue Rose.

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Metadata

Authors
Omodan, B. I.
Affiliations
University of the Free State (ZA)
Corresponding author
Omodan, Bunmi Isaiah
ORCID
Omodan, Bunmi Isaiah
Venue
Cogent Education
Publisher
Informa UK Limited
Published
2025-07-29
Volume
12
Issue
1
ISSN
2331-186X (ISSN-L)
DOI
10.1080/2331186x.2025.2536534
OpenAlex
W4412769555
Cited by
22
References
27
Citations by year
2026: 22
FWCI
17.85
Type
journal article
Language
en
Open access
Licence
Locations
  • Cogent Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Education › Doctoral Education Challenges and Solutions
  • Social Sciences › Education › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Keywords
artificial intelligence in academia, postgraduate supervision, research governance, academic mentorship, algorithmic bias, ethical accountability, intellectual property, epistemic authority, graduate education

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-11-13, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-11-13 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.