University Teaching in Social Science and Humanities 2026

Welcome to University Teaching in Social Science and Humanities

Description:

The course objective is to develop participants’ foundational capacity toteach in a university setting. Specifically, the course aims to develop PhD students’ teaching competencies equivalent to level 1 of the Danish framework for advancing university pedagogy (https://www.iaspbl.aau.dk/projects/danish-framework-for-advancing-university-pedagogy).

Requirements:

The basis of the course is the practice of the participants. Thus, it is an absolute requirement that participants hold teaching obligations the semester they follow the course.

Timeframe:

The course is spread out over more or less an entire semester to allow participants to experiment with their own teaching and receive feedback in the course.

The course opens with a full day (9-16). Followed by four half days (9-12.30) a month between each day

This opening day is placed before the beginning of the semester (Ultimo January/ Ultimo August). The day provides a basic introduction to higher education pedagogy. Opening day is held in Aalborg and is mandatory for all particpantns. Furthermore, participants are divided into small groups or professional learning communities (PLC’s) in which the participants will work the rest of the course. PLC’s will meet and discuss teaching experiences at every meeting. PLC meetings are held in Aalborg and Copenhagen respectively.

Overall Learning Objectives:

  • The overall learning objectives link to the Danish framework of advancing university pedagogy and ensure that participants under supervision are able to:
  • Plan and carry out individual teaching and supervision activities
  • Organize and carry out assessment
  • Evaluate their teaching and teaching activities

Further, the course ensures that participants can:

  • Create a constructive learning-oriented teaching environment
  • Participate in collaboration on teaching with peers
  • Identify resources for developing teaching quality

Teaching methods:

The course combines short lectures with student centred activities such as training exercises, group discussions, peer observation and feedback. The methods adopted will be discussed as exemplars of teaching strategies.

Description of teaching portfolio:

Participants are required to create a teaching portfolio. They will receive instructions in creating a portfolio and they will be required to give each other feedback on their respective portfolio. The portfolio can be in writing. However, participants’ will be encouraged to experiment with form and content. Furthermore, the course will discuss the requirements of a well-functioning portfolio in future job applications. 

Programme day one:

Structure:

Program Opening day (Only In Aalborg)

  • 9.00-10.00: Welcome, introduction to the program
  • 10.00-11.00: University teaching at a PBL university
  • 11.00-12.00: Group supervision
  • 12.00: Lunch
  • 13-14: Lecturing
  • 14:00: First PLC meeting
  • 15.30: wrapping up

Program PLC meetings:

Each PLC meeting is divided into two sections. In the first 1½ hours a theme is discussed. Typically, one of the conveners will open with a short presentation to be discussed by the group afterwards. The group is also required to read texts in relation to the theme of the day sent out before the PLC meeting. These texts will likewise be included in the conversation. Theme of the day is decided by the group on the prior meeting. Example of themes could be Lecturing, group examination, groups supervision and group conflict and more. The other half of the meeting takes place in the PLC’s in which participants discuss their own teaching experiences. These experiences are used as basis for collective reflections on teaching and learning. Finally, each day ends with a lunch (12-12.30). In this way the idea is that the course will not only provide participants with theoretical and practical knowledge about university teaching but also help them establish a social network across departments.

Peer observations and supervision:  

Between each PLC meeting participants are to engage in peer supervision, which involves observation of each other’s teaching followed by peer supervision. Participants will receive training in teaching observations and peer supervision prior to the first round of observations.

We are of course aware that not everybody will have teaching obligations from one month to the next but hopefully at least one will have some obligations between each meeting. Furthermore, other arrangements can be made. One time the group will be invited to observe one of the conveners teach and it is also possible to arrange observations of other (more or less) experienced colleagues.

  • 16.00: See you next time

Organiser:

Nikolaj Stegeager, Culture & Learning, SHARE-PBL


Lecturers:

  • Maria Hvid Stenalt, Culture & Learning, SHARE-PBL
  • Jes Lynning Harfeld, Culture & Learning, RECAST
  • Nikolaj Stegeager, Culture & Learning, SHARE-PBL

ECTS: 
3

Dates and time:

27/1:   9-15
24/2:   9-12.30
24/3:   9-12.30
21/4:   9-12.30

Place:

AAU Innovate, Thomas Manns Vej 25

AAU Campus Copenhagen, A.C. Meyers Vænge 15

Zip code:
9220

City:
Aalborg

Number of seats:
15

Deadline:
6 January 2026

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. 

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.



Generative AI in the PhD process 2026

Welcome to Generative AI in the PhD process

Description:

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 in the Humanities and Social Sciences is based on an exploration and reflection of the potentials and pitfalls of using GAI in participants’ individual PhD projects.

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.

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

Day 2

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

 

9.00 - 9.15

Welcome back and orientation

with Antonia Scholkmann and Kristine Bundgaard

 

 

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.

 

Participants will – in small groups – discuss their first ideas on their own GAI-strategies (prepared as first drafts in advance of the course); they will specifically be asked to reflect on and amend their strategies with the input from the opening session. The reflective exercise will close with a plenum discussion of key points.

9.15 – 10.00

AI and Doubt: A Method Workshop on the Literature Review Screening Phase – Part 1

Workshop with Tobias Tretow-Fish

 

The workshop introduces participants to a method that can i) develop and refine inclusion and exclusion criteria for literature reviews, and ii) identify studies (edge cases) that fall near the boundary of those criteria. Using a shared dataset as a starting point, participants work on identifying edge cases and adjusting selection criteria for their own research. The focus is on methodological awareness and reflection on the complexity of the screening process, rather than achieving a final selection. After the workshop, participants will be able to carry out the same process with their own data material.

 

11.15 – 11.30

Break

10.00 – 10.15

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.

 

 

10.15 – 11.15

AI and Doubt: A Method Workshop on the Literature Review Screening Phase – Part 2

 

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

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. 

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.

 

14.45 - 15.00

Break

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.

 

15.00 – 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.

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.

 

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.

 

 

Description of paper requirements:

There are two paper assignments attached to this course. 

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

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

         b) short draft of potential points to integrate into their GAI-strategy (approx. 700 words). This should specifically contain reflections on how GAI is already or might potentially be relevant to the                                 participant’s PhD project

2. Based on the work during the course, participants will be expected to produce a more comprehensive reflective paper (approx. 3,000 words) describing their GAI strategy for the PhD project. Drawing                  on relevant literature from the course and/or beyond, this should contain:

  • Aspects of GAI use that are relevant for the individual PhD project
  • Elaboration of why those points are relevant
  • Concrete actions to address those points
  • Reflections on why the PhD candidate considers those points relevant in light of topics such as AI ethics/AI research ethics, own learning goals and the balance between academic work pressure and academic integrity.

Organizer:

Antonia Scholkmann; IKL; CfU and IAS-PBL

Kristine Bundgaard; IKL; L-ILD

Lecturers:

Antonia Scholkmann, Associate Professor, IKL

Kristine Bundgaard, Associate Professor, IKL

Richard Watermeyer, Professor, University of Bristol

Jes Lynning Harfeld, Associate Professor, IKL

Tobias Tretow Fish, postdoc, IKL

ECTS: 2

Time: 22, 23 January 2026

Place: TBA


City: Aalborg

Number of seats: 30

Deadline: 15 december 2025


Literature:

Mandatory literature (to be read as preparation for the course)

Aalborg University Library. (n.d.). Topic: Generative AI. Aalborg University Library. https://www.en.aub.aau.dk/students/generative-ai/introduction-and-overview

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). Association for Computing Machinery.

Malthe Stavning Erslev and Tobias 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 Research Area Forum (ERA)

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

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

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

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

Scholkmann, A. (under preparation). Why ‘learning with AI’ must be of pedagogical and organizational concern. A conceptual elaboration of learning with AI under the lens of pedagogical-psychological key concepts. [This article is under preparation; either a version of the manuscript or a published version will be made available to the students before the beginning of the course.]

Scholkmann, A. (2025). Sådan bruger du ChatGPT uden at blive dummere. Læringsforskningens anbefalinger 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 Academia. Postdigital Science and Education, 6(2), 446–466. https://doi.org/10.1007/s42438-023-00440-6

Additional literature/suggested readings

Andrade-Hidalgo, G., Mio-Cango, P. & Iparraguirre-Villanueva, O. (2024) Exploring the Impact of Artificial Intelligence on Research Ethics - A Systematic Review. Journal of Academic Ethics. https://doi.org/10.1007/s10805-024-09579-8

Gardner, S. K. (2008). “What’s too much and what’s too little?”: The Process of Becoming an Independent Researcher in Doctoral Education. The Journal of Higher Education, 79(3), 326–350. https://doi.org/10.1080/00221546.2008.11772101

Lee, K. (2020). Autoethnography as an Authentic Learning Activity in Online Doctoral Education: An Integrated Approach to Authentic Learning. TechTrends, 64(4), 570–580. https://doi.org/10.1007/s11528-020-00508-1

Lindén, J., Ohlin, M., & Brodin, E. M. (2013). Mentorship, supervision and learning experience in PhD education. Studies in Higher Education, 38(5), 639–662. https://doi.org/10.1080/03075079.2011.596526

Nevala, K. (n.d.). The Nature of Learning with Helen Beetham (66) [Broadcast]. Retrieved 20 March 2025, from https://pondering-ai.transistor.fm/episodes/ep66

Oliveira, J., Murphy, T., Vaughn, G., Elfahim, S., & Carpenter, R. E. (2024). Exploring the Adoption Phenomenon 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

Sarrico, C. S. (2022). The expansion of doctoral education and the changing nature and purpose of the doctorate. Higher Education, 84(6), 1299–1315. https://doi.org/10.1007/s10734-022-00946-1

Shacham, M., & Od‐Cohen, Y. (2009). Rethinking PhD learning incorporating communities of practice. Innovations in Education and Teaching International, 46(3), 279–292. https://doi.org/10.1080/14703290903069019

Smith, S. M., Tate, M., Freeman, K., Walsh, A., Ballsun-Stanton, B., & Lane, M. (2025). A university framework for the responsible use of generative AI in research. Journal of Higher Education Policy and Management, 1–20. https://doi.org/10.1080/1360080X.2025.2509187

Watermeyer, R., Lanclos, D., Phipps, L., Shapiro, H., Guizzo, D., & Knight, C. (2024). Academics’ Weak(ening) Resistance to Generative AI: The Cause and Cost of Prestige? Postdigital Science and Education. https://doi.org/10.1007/s42438-024-00524-x

Zamani, S., & Sinha, R. (2024). Generative AI --- The End of Systematic Reviews in PhD Projects? ACM Inroads, 15(2), 48–50. https://doi.org/10.1145/3657304

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.

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.