Graduate Student Supervision and Artificial Intelligence: Findings from Academic Faculty Interviews Across Disciplines

Start Date

3-18-2026 3:30 PM

End Date

3-18-2026 4:00 PM

Author(s) Bio

Kari D. Weaver (she/her) is the Program Manager, Artificial Intelligence and Machine Learning at the Ontario Council of University Libraries and Learning, Teaching and Instructional Design Librarian at the University of Waterloo Libraries. She holds her MLIS from the University of Rhode Island and EdD in Curriculum and Instruction from the University of South Carolina. Her wide-ranging research background includes study of graduate student perceptions of research, accessibility for online learning, information literacy, academic integrity, misinformation, citation, attribution, and disclosure practices for artificial intelligence. Nadine Fladd (she/her) has held the role of Manager of Graduate Student and Postdoctoral Fellow Programs at the Writing and Communication Centre at the University of Waterloo since 2015. In her work, she supports graduate students, postdocs throughout all stages of the writing process through individual consultation, workshops, and intensive support programs like Dissertation Boot Camp. She holds a PhD in English from Western University with a focus on collaborative editing practices in Canadian fiction and now conducts research about academic writing support and writing processes.

Keywords

Generative Artificial Intelligence, Graduate Supervision Practices, Phenomenology

Description of Proposal

There is widespread discussion in higher education about the potential implications of GenAI for research and writing tasks (George, 2023). Many graduate students are under intense pressure to finish their degree milestones in a timely manner, and these rapidly evolving technologies offer the potential to optimize and automate much of the research and writing process. But the ethical, developmental, and career implications of graduate students – both masters’ and doctoral – using these technologies requires particularly careful consideration, as these students are simultaneously learners, scholars, and, sometimes, educators. Although emerging research about GenAI use in higher education has examined the perceptions and practices of students as researchers and writers (Wright, 2024), there currently exists limited information on the impact of graduate student supervisory approaches to graduate student use of GenAI.

This presentation reports on an interview-based study of faculty across research disciplines at a large Canadian research university. The study examines how graduate supervisors make decisions about students’ use of GenAI tools in academic work. This issue is pressing: graduate students face high-stakes choices in contexts where policies are unclear and supervisory practices vary widely. Using an interpretive phenomenological approach (Smith et al., 2009), we will share the themes that emerged from our study. By exploring faculty perspectives, questions, and concerns, we aim to illuminate the challenges and opportunities of integrating GenAI into graduate supervision.

The study sought to answer several key research questions, including:

  • To what extent do supervisors permit or encourage graduate students to use GenAI tools in research and writing?
  • What factors influence their decisions to approve or restrict such use?
  • How do supervisors’ own confidence and familiarity with GenAI shape their guidance?
  • What strategies do supervisors employ when mentoring students on responsible GenAI use?

Ultimately, this study lays the groundwork for future collaborations, including toolkits, workshops, and training programs for supervisors. Libraries and partner academic support units like writing centers are uniquely positioned to champion these efforts to ensure graduate students and their mentors can navigate the evolving landscape of AI with confidence and clarity.

References

George, A. S. (2023). The Potential of Generative AI to Reform Graduate Education. https://doi.org/10.5281/ZENODO.10421475

Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative phenomenological analysis: theory, method and research. SAGE.

Wright, A. (2024). Postgraduate Supervision in a ChatGPT World: What’s Next? 10th International Conference on Higher Education Advances (HEAd’24), 1–8. https://doi.org/10.4995/HEAd24.2024.17244

What takeaways will attendees learn from your session?

1. Supervisory practices are critical in shaping use of generative artificial intelligence

2. There is a need for institutional support and training for graduate students and their supervisors on generative artificial intelligence in research

3. Graduate supervisors' own experiences and confidence with generative artificial intelligence impact their approach to teaching their graduate students about its use.

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Mar 18th, 3:30 PM Mar 18th, 4:00 PM

Graduate Student Supervision and Artificial Intelligence: Findings from Academic Faculty Interviews Across Disciplines

There is widespread discussion in higher education about the potential implications of GenAI for research and writing tasks (George, 2023). Many graduate students are under intense pressure to finish their degree milestones in a timely manner, and these rapidly evolving technologies offer the potential to optimize and automate much of the research and writing process. But the ethical, developmental, and career implications of graduate students – both masters’ and doctoral – using these technologies requires particularly careful consideration, as these students are simultaneously learners, scholars, and, sometimes, educators. Although emerging research about GenAI use in higher education has examined the perceptions and practices of students as researchers and writers (Wright, 2024), there currently exists limited information on the impact of graduate student supervisory approaches to graduate student use of GenAI.

This presentation reports on an interview-based study of faculty across research disciplines at a large Canadian research university. The study examines how graduate supervisors make decisions about students’ use of GenAI tools in academic work. This issue is pressing: graduate students face high-stakes choices in contexts where policies are unclear and supervisory practices vary widely. Using an interpretive phenomenological approach (Smith et al., 2009), we will share the themes that emerged from our study. By exploring faculty perspectives, questions, and concerns, we aim to illuminate the challenges and opportunities of integrating GenAI into graduate supervision.

The study sought to answer several key research questions, including:

  • To what extent do supervisors permit or encourage graduate students to use GenAI tools in research and writing?
  • What factors influence their decisions to approve or restrict such use?
  • How do supervisors’ own confidence and familiarity with GenAI shape their guidance?
  • What strategies do supervisors employ when mentoring students on responsible GenAI use?

Ultimately, this study lays the groundwork for future collaborations, including toolkits, workshops, and training programs for supervisors. Libraries and partner academic support units like writing centers are uniquely positioned to champion these efforts to ensure graduate students and their mentors can navigate the evolving landscape of AI with confidence and clarity.

References

George, A. S. (2023). The Potential of Generative AI to Reform Graduate Education. https://doi.org/10.5281/ZENODO.10421475

Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative phenomenological analysis: theory, method and research. SAGE.

Wright, A. (2024). Postgraduate Supervision in a ChatGPT World: What’s Next? 10th International Conference on Higher Education Advances (HEAd’24), 1–8. https://doi.org/10.4995/HEAd24.2024.17244