Course detail

Welcome to PhD supervision and generative AI

Description:

This course for PhD supervisors intends to increase this group’s awareness and competences regarding PhD supervision under the perspective of GenAI. GenAI can take many forms and “roles” in students’ PhD projects (such as for example a tool for research, a writing assistant, a study-buddy); therefore, participants will be encouraged to reflect on their supervision strategies in light of what is relevant in their stu-dents’ specific disciplines, fields and research topics.

Intended outcomes are that participants:

• Know about the fundamental principles of GenAI as a sociocultural technology and the challenges (e.g., biases, cheating) that come with its use.
• Understand and reflect on the role of GenAI in the PhD process as a tool for learning, writing and enculturation into academia and academic career paths, regarding the challenges attached to this.
• Reflect on their ethical supervision strategies for GenAI use with their PhD students.

The course will be held in a combination of lectures, reflective talks in groups and exchange of thoughts in plenum

Program outline:

08.30-08.45 Welcome & introduction: Relevance of topic and organization of course

08.45-9.00  Talk in small groups (breakout rooms): “What are my concerns regarding GenAI in the PhD process?”; documentation of key points on a digital board.

9.00-9.15   Discussion in plenum

9.15-9.45   Lecture 1: The workings of GenAI (underpinning stochastic and probabilistic logics; GenAI as a sociocultural technology; issues like ethics and biases; future points for the course)                          

9.45-10.00  Coffee break

10.00-10.30 Lecture 2: The role and challenges of GenAI in the PhD phase (GenAI and learning, GenAI and writing, GenAI as changing academic work and epistemic practices)

10.30-10.45 Discussion in plenum: “How do those points resonate with your concerns and future strategies for supervising PhD-students?”

10.45-11.00 Coffee break

11.00-11.30 Lecture 3: Supervision of ethical and responsible GenAI-use (guest lecture with Jes L. Harfeld).

11.30-11.45 Talk in small groups (breakout rooms): “What are my main takeaways from today? What will I do in my supervision practice regarding GenAI?”

11.45-12.00 Wrap-up in plenum

Organizer:  Kristine Bundgaard and Antonia Scholkmann

Lecturers:  Kristine Bundgaard, Antonia Scholkmann, and Jes Harfeld

Date: 23 November 2026

Time: 8.30 – 12.00

Place: Online

Deadline: 19 November 2026

Key literature:

Bender et al. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT’21) (pp. 610-623). As-sociation for Computing Machinery

Erslev and Tretow-Fish(2025). From Bullshit to Cognition: Computing Within the Epistemic Crisis of Large Language Models in Systematic Literature Review. In The sixth decennial Aarhus conference: Computing X Crisis (AAR 2025), August 18–22, 2025, Aarhus N, Denmark. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3744169.3744195

European Union (2024) Guidelines on the responsible use of generative AI in research. European Re-search Area Forum (ERA)

Farrell, H., Gopnik, A., Shalizi, C., & Evans, J. (2025). Large AI models are cultural and social technolo-gies. Science, 387(6739), 1153–1156. https://doi.org/10.1126/science.adt9819

Jakesch, M., Hancock, J. T. & Naaman, M. (2023). Human heuristics for AI-generated language are flawed. PNAS 120, 11.

Møgelmose, A. (2024): Module 1. In: Introduction to Generative AI and ChatGPT. AAU Micro. https://www.en.micro.aau.dk/available-aau-micros/introduction-to-generative-ai-chatgpt

Oliveira, J., Murphy, T., Vaughn, G., Elfahim, S., & Carpenter, R. E. (2024). Exploring the Adoption Phe-nomenon of Artificial Intelligence by Doctoral Students Within Doctoral Education. New Horizons in Adult Education and Human Resource Development, 36(4), 248–262. https://doi.org/10.1177/19394225241287032 

Salles, A., Evers, K. & Farisco, M. 2022. Anthropomorphism in AI. AJOB Neuroscience, 11(2), pp. 88-95.

Scholkmann, A. (2025). Sådan bruger du ChatGPT uden at blive dummere. Læringsforskningens anbefa-linger til dig, der benytter ChatGPT til opgaver i gymnasiet eller på universitetet. Videnskab.Dk. https://videnskab.dk/teknologi/saadan-bruger-du-chatgpt-uden-at-blive-dummere/

Watermeyer, R., Phipps, L., Lanclos, D., & Knight, C. (2024). Generative AI and the Automating of Aca-demia. Postdigital Science and Education, 6(2), 446–466. https://doi.org/10.1007/s42438-023-00440-6

Suggested literature:

Ferrara, E. (2023). Should ChatGPT be biased? Challenges and Risks of Bias in Large Language Models, https://arxiv.org/abs/2304.03738.

Perfors, A. (2026). All about AI [Youtube Video Series].