En ai-genererad robothand över ett fysiskt textdokument på ett bord Photographer: AI-genererad av Sara Läthén
Seminar

Producing a doctoral thesis – creation, translation and prompting with genAI tools

Welcome to the second seminar in the seminar series on generative AI use in doctoral research.

Doctoral research takes many forms – fieldwork, experiments, archives, quantitative or qualitative data. But, at some point, the practical work of research must be transformed into a text, a thesis, that can be defended and examined. GenAI tools offer a range of assistance with this process of creating a text, from translation to brainstorming to revising to writing. How much can we use genAI tools to create a thesis and still be considered the author of the thesis? Who is responsible for the text in the case of queries – student, supervisor, institution or Co-pilot?

This seminar introduces three different perspectives around creation, prompting and translation in relation to production of dissertation text. Each of our speakers will have 15 minutes to put forward their perspective on the impact of these tools on the production of text, before we open the floor to debate!

This page will be updated with the specific venue. 

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About the speakers

Eva Medin, University of Borås

Eva Medin is a language specialist at the University of Borås, where she leads professional development in academic writing for researchers and doctoral students and coordinates English-language communication and translation. Her work focuses on academic communication in international settings and language policy, and she is particularly interested in the implications of generative AI for learning, as well as for scholarly writing and authorship. She has a background in applied linguistics and educational research, with publications on identity, language, and learning in educational contexts.

Anders Sonesson, Lund University

Anders Sonesson is a doctor in Plant Physiology and an associate professor (docent) in Educational Sciences. As senior lecturer at the Division for Higher Education Development (AHU) he has, among other things, for more than two decades taught on Lund University’s courses on doctoral supervision as well as served on boards and in working groups related to doctoral education. He has published several works on doctoral education and supervision, with a particular focus on the consequences of governance and the intersections between policy and practice. He is also engaged in the critical study of digital policy and the digitalization of education and has recently published on schoolteachers’ use and sensemaking of generative AI products. In an ongoing project, he studies academics’ ‘grey use’ of generative AI. He currently supervises two PhD-students.

Mert Can Yılmaz, Uppsala University

Mert Can Yılmaz is a Research Engineer and Senior Analyst at the Uppsala Conflict Data Program (UCDP), Uppsala University, where he develops automated tools and computational methods for large-scale conflict event data. He is co-founder of Yapay Gündem, a platform for Turkish-speaking audiences on the societal and ethical implications of emerging technologies. His research interests span conflict data infrastructure, measurement of political violence, AI ethics and governance, and misinformation ecosystems. He writes regularly on AI ethics & warfare and the politics of technology in both Turkish and English, contributing to public debate through opinion pieces, policy reports, and panel discussions. He holds two master's degrees from Uppsala University – in Peace and Conflict Studies and in Theology focussing on Religion in Peace and Conflict – and a BA in Political Science and International Relations from Boğaziçi University.

About the seminar

This seminar introduces three different perspectives around genAI tools for creation, prompting and translation in relation to production of dissertation text. To kick-start the discussion, our speakers have provided the following questions to prompt reflection – bring your thoughts and be ready to discuss.

  • What is lost in terms of learning, creativity and knowledge with the increased use of generative AI-products? And what difference does “prompting literacy” make to the process?
  • If AI can help us make our writing sound right, what happens to the process of figuring out whether we are right? And where is the line between language support and outsourced authorship?
  • What gets lost when we confuse the quality of someone's English with the quality of their thinking? Given the mediocrity of what LLMs actually produce in terms of novel thinking, AI tools don't simply hand their users brilliant new research ideas. But how might AI tools give you a fairer shot at expressing the brilliant ideas you already have?

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