Generative AI in the PhD process (2027)

Welcome to Generative AI in the PhD process

Generative artificial intelligence (GAI) is becoming ingrained in research processes to an ever-evolving degree. Recently, GAI has recently become both the object for increased research, used as a research method, e.g. in the form of AI-assisted qualitative data analysis, and as a writing aid in the preparation of academic manuscripts, among other things in developing research applications. Recent research shows that at least half of academics report using GAI tools, and as much as 83% expect to use GAI more in the future (Watermeyer et al. 2024). This calls for discussions of responsible uses of GAI in research, and not least emergent researchers should develop their own GAI strategy, as well as lay foundations for continuous development and reflection of their use of GAI as they progress in their research careers.

Doctoral education (also known as the PhD phase or the PhD process) has been shown to be the crucial point in time when emergent researchers undergo enculturation into the academic community and "learn the trade" of being a researcher (Gardner, 2008). However, with an ever-increasing diversity in models and aims of doctoral education (Sarrico, 2022), the need also arises for PhD students to develop their own strategies of how and to what means to curate their PhD trajectories. GAI accelerates this need, as this highly versatile technology can be applied at various stages, to various ends and with various levels of success throughout the PhD process (e.g., Zamani & Sinha, 2024). Recent research into GAI use in the PhD phase has called for "a balanced approach to AI adoption [depending] on the development of comprehensive strategies that are informed by a deep understanding of both the technological capabilities and the human factors involved" (Oliveira et al., 2024).

This generic PhD course for emergent researchers across disciplines is based on an exploration and reflection of the potentials and pitfalls of using GAI in participants' individual PhD projects.

Learning objectives:

Its objectives are that participants:

• gain insights into the various aspects of and approaches to the integration of GAI in the conduct of research; specifically: i) GAI as a tool for learning and development; ii) GAI as a tool for writing; iii) GAI and research ethics; iv) GAI's application as a research method

• expand their understanding of the implications of GAI integration into (PhD) research processes

• reflect and draft their own GAI strategy (including research ethics) for their PhD project

The course strives for a nuanced elaboration of GAI; for that, it will be combining critical-reflective and proactive perspectives of how to think about and engage with GAI. Its pedagogical-didactical underpinnings are rooted in state-of-the-art knowledge about PhD students benefitting from cutting-edge knowledge input in combination with opportunities for reflective and collaborative processes (Lee, 2020) (Lindén et al., 2013), which will be also integrated into the course.

This course will be held over two days. The first day will be dedicated to the topic of understanding GAI as a tool for learning, development and the conduct of academic work; as well as the ethics and implications of GAI use in the PhD process. The second day will provide participants with the opportunity to explore the possibilities and challenges of integrating GAI as a research tool and to reflect further on the question of GAI ethics.

Please note that this course addresses the practices of using generative AI in the PhD process as a tool in academic life and production across disciplines. PhD students who work on generative AI as their PhD topic are welcome and we are looking forward to discussing your insights; however, there will be limited space to discuss your topic in detail, as the focus will be on the general questions of GenAI integration into your academic process.

Teaching methods:

This course will combine lectures from experts on the various aspects of GAI research integration, plenary discussions and a set of reflective and hands-on exercises with integrated peer feedback.

Programme outline:

Day 1

10.00-10.30 Welcome, running through the program and topical opening
With Antonia Scholkmann and Kristine Bundgaard

Scholkmann and Bundgaard will briefly introduce the course's program and will frame its overarching topic; presentation of GAI under its various "roles" in the PhD process (e.g., as a writing and learning process tool, as method and as topic in its own right); short introduction to some of the overarching principles for the use of AI in research.

10.30-11.15 Reflective exercise in groups
Facilitated by Antonia Scholkmann and Kristine Bundgaard

First ideas on participants' own GAI strategies (drawing on principles) + discussion in plenum.

11.15-11.30 Break

11.30-12.15 GAI as a technology for learning in the PhD process
Lecture and discussion with Antonia Scholkmann

As the PhD process must be seen under a learning perspective (including the enculturation into the academic community), we need to ask what role GAI as a new technology can and will inevitably play in this. In this talk, Scholkmann will outline the potentialities and challenges of GAI as a sociotechnical technology allowing PhD students to "learn from the experience of others" (Farrel et al, 2025). Based on recent work on this topic, participants will be invited to reflect on how pedagogical-psychological concepts such as intelligence, deep learning and immediate feedback built into the technology can inevitably come to interact own practices of exploring problems, building knowledge and thinking creatively.

12.15-13.15 Lunch

13.15-14.45 Refusing generative AI: A lesson in rights, responsibility and reactionaries in the era of research automation
Lecture and discussion with Prof. Richard Watermeyer, University of Bristol

[This session will be held hybrid, with Watermeyer presenting remotely with Scholkmann and Bundgaard facilitating on-site in Aalborg]

In this talk, Richard Watermeyer will explore data from recent qualitative surveying of UK academics and perspectives on the why and why not of GenAI use for work purposes. Discussion will focus on concerns around academics' deagentification, and the degradation of scholarly craft linked to the proliferation (and invisibilisation) of automative tools into scholarly infrastructures, and in corollary, an escalation of technological dependency contrived to satisfy a productivity mania endemic to universities. It will appraise the implications of a weak(ening) capacity for technological refusal among academics to the value proposition of the university during a contemporary period marked by the severity of permacrisis and threat of financial oblivion.

14.45-15.00 Break

15.00-16.15 GAI as a writing tool in the PhD process
Lecture and discussion with Kristine Bundgaard

The talk will address the use of GAI for different writing purposes and in different phases of the PhD. Among other things, we will discuss writing as learning. The session will include an experiment with human detection of AI-generated text with the purpose of discussing trust-building in AI-generated text.

16.15-17.00 Walk and talk + discussion in plenum

Participants will be assigned in pairs to conduct a walk and talk discussion of the day's input in light of their own GAI strategies. Insights and open questions will be discussed in the plenum afterwards.

17.00-18.30 Break, check-in hotels, transport etc.

18.30 Self-paid dinner at a restaurant in town

Day 2

9.00-9.15 Welcome back and orientation
With Antonia Scholkmann and Kristine Bundgaard

9.15-10.00 Uncertainty and AI: A Case Study in Screening Literature Review, Part 1
Workshop with Tobias Tretow-Fish

Using a real case from his own research, Tobias Tretow-Fish walks participants through what happened when GPT-4o was used to screen titles and abstracts for a systematic literature review, including the "fringe cases" that landed near the boundary between inclusion and exclusion. From there, the workshop opens up a critical discussion of what it means to treat generative AI as part of a scientific method: what transparency a literature review needs to remain trustworthy, and what epistemic risks follow from letting an LLM do classification work it has no concern for the truth of. Through a series of short discussion exercises, participants test these questions against their own review work and leave with space to sketch how (or whether) they would build GenAI into their own research design.

10.00-10.15 Break

10.15-11.15 Uncertainty and AI: A Case Study in Screening Literature Review, Part 2
Workshop with Tobias Tretow-Fish

11.15-12.15 Responsible and ethical use of GAI in the PhD process
Lecture with Jes Lynning Harfeld

As generative AI tools become increasingly accessible in academic settings, it's vital that we reflect on how these technologies are used in the PhD writing process. This talk will focus on the responsible and ethical use of AI in research, grounded in updated national and international guidelines on this topic. We'll explore the importance of transparency, clearly declaring when, how, and why AI tools are used, especially in generating text, graphics, or data. Researchers must ensure that AI-generated data is reproducible and thoroughly validated to avoid bias, inaccuracies, and errors. We'll also discuss the need for institutional policies on authorship and the declaration of AI use, and emphasize that, regardless of the tools employed, the responsibility for research integrity always lies with the researcher.

12.15-13.15 Lunch

13.15-15.00 Using GAI for analysis of qualitative data
Workshop facilitated by Kristine Bundgaard and Antonia Scholkmann

This workshop focuses on the use of GAI for analysis of qualitative data. Following a short presentation, participants will work with GAI-based qualitative data analysis in 3 iterations: 1) participants will do a manual thematic coding of an interview excerpts, 2) participants will contrast the manual coding with inductive AI-based coding of the same excerpt, 3) participants will do an AI-based coding of the whole interview, prompting with the initially developed themes. The three iterations will be following by a discussion of the pros and cons of AI-assisted qualitative analysis. Depending on participants' interest, the workshop can include using AI to quantify qualitative data.

15.00-15.30 Discussion of participants' own GAI strategies
Facilitated by Kristine Bundgaard and Antonia Scholkmann

Final sessions for participants to re-visit and discuss their amended GAI-strategies in groups.

15.30-16.00 Summing up the course and oral evaluation
Facilitated by Kristine Bundgaard and Antonia Scholkmann

Short plenum discussion to sum up on open points from students' discussions. Wrapping up the course and oral evaluation.

Paper requirements:

There are two paper assignments attached to this course.

In advance of the course, participants will be expected to produce:

• A brief description of their PhD research (approx. 300 words) and

• A short draft of potential points of relevance regarding their approach to using of GAI in their PhD project (approx. 700 words). This should specifically contain reflections on how GAI already is or might potentially be relevant to the participant's PhD project.

Based on the work during the course, participants will be expected to produce a more comprehensive paper (2,500-3,000 words), reflecting their extended perspective on their approach towards using GAI in their PhD project, drawing on relevant literature from the course and/or beyond. Participants should convey how they can develop and nuance their own approaches towards GenAI use in light of topics such as AI ethics/AI research ethics, own learning and achievements goals during the PhD phase and epistemic practices between daily work pressure and academic integrity. A detailed structure for this assignment will be provided during the course.

Literature: See file below

Program: See program below

Organizer: Antonia Scholkmann, IKK, CfU and IAS-PBL; Kristine Bundgaard, IKK, L-ILD

Lecturers:

Antonia Scholkmann, Associate Professor, IKK
Kristine Bundgaard, Associate Professor, IKK
Richard Watermeyer, Professor, University of Bristol
Jes Lynning Harfeld, Associate Professor, IKK
Tobias Tretow Fish, Assistant Professor, IKK

ECTS: 2

Date:

Place:

City: Aalborg University, Campus Aalborg

Number of seats: 15-30

Open for enrolment:

Deadline for enrolment:

Important information concerning PhD courses:

There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

You may find more information in our FAQ: https://phd.moodle.aau.dk/local/page/faq

For inquiries regarding registration, cancellation or waiting list, please contact the PhD administration at phdcourses@adm.aau.dk. When contacting us please state the course title and course period. Thank you.

Self-enrolment

Thank you for your enrolment.
Your preliminary enrolment has been received. Final confirmation will be issued after the enrolment deadline has passed.
Best regards
AAU PhD
phdcourses@adm.aau.dk