FUNDED PROJECTS
RMT
Rethinking the Master's thesis: Integrating Generative AI and Practice-Based Partnerships. The proposed project’s aim is to establish collaborative learning network on the master’s thesis, starting from three universities.
Category: Incubator Grant
University:
Ghent University, University of Groningen, University of Tartu
Period:
2025-2027
Developments in the domain of GenAI create both challenges and opportunities for student-led research projects and call for an evaluation of the master’s thesis. The proposed project’s aim is to establish collaborative learning network on the master’s thesis, starting from three universities. This could take the form of a database of practice-oriented research questions or a showcase of best practices in the master’s thesis, that serve as inspiration for students and supervisors of all ENLIGHT partners. This can cover all stages of the thesis process, including the development of the research question, the selection of the methodology, the writing process, and the final product.
Personal highlights:
Quality assurance challenge: Safeguarding core academic competencies (critical thinking, argumentation, transparent communication) becomes more difficult when GenAI can generate substantial portions of a student’s work.
Positive shift toward impact: Universities are aware of the importance of developing research that aligns with current societal and labor-market-relevant challenges.
Supportive role of practice partners: Involving practice-oriented partners in the master’s thesis process can provide support and help reduce potential challenges associated with the use of generative AI.
Participants and Stakeholders
- Coordinator: Prof. Dr. Bertel De Groote & Prof. Dr. Evelien Opdecam, Department of Accounting, Corporate Finance and Taxation, Faculty of Economics and Business Administration, Ghent University
- Other Partner Institutions: University of Groningen, University of Tartu
- Team Composition:
University of Groningen: Oskar Roemeling, Eveline Hage, Martijn Keizer, Kristian Peter & Feikje van der Hoek
University of Tartu: Aune Valk, Helen Hint, Heiko Paablo
Objectives
Defining shared academic competences: We will jointly formulate a framework of essential academic competences for a future-proof master's thesis. This will be co-designed with the input of academic staff and students.
Gathering online material: We will gather learning material to support students and supervisors in GenAI and practice-oriented research. These materials can then be distributed through the online learning system of the three institutions.
Gathering new assessment approaches: Since societal reality changes (i.e., labor market demands) and GenAI hugely impacts the conception of a masters’ thesis, these modified contexts should be incorporated into what and how we assess in order to keep the evaluation of required competences and skills valid and doable while meeting relevant societal expectations of higher education.
Development of a professional network: The collaboration will enable the development of a sustainable network among ENLIGHT partners and their internal and external stakeholders. This platform should facilitate the organization and dissemination of events, foster research collaboration, and support the sharing of experiences and best practices regarding the master’s thesis. It can host both physical and virtual activities and should also inspire joint supervision and other forms of collaborative engagement.
Contact
Prof. Dr. Bertel De Groote,
Additional information
By rethinking the master’s thesis in light of rapidly evolving technologies and growing expectations for socially relevant research, the project aims to explore how the thesis can better function as a bridge between academia and professional practice. The ambition is to contribute to the future development of the concept and implementation of the master’s thesis. The project seeks to support the educational value of the master’s thesis by helping students strengthen future-oriented academic competences, including the critical use of GenAI tools. It may also offer supervisors a possible framework and practical tools—drawing on experiences from partner institutions and adapted to the changing context—to guide and assess master’s theses. In addition, the project hopes to increase awareness of the added value of collaborating with practice on practice-oriented research questions. Over time, the inter-university collaboration may evolve into a more sustained network around supervising and supporting master’s theses.
To support a strong start and potential long-term relevance, the project will focus on institutional commitment, stakeholder engagement, and structural integration across the partner universities. All institutions face similar challenges regarding the changing role of GenAI in the master’s thesis, and this shared awareness can provide a foundation for ongoing collaboration. The project will work on the technical integration of learning materials into each university’s digital learning environment, as well as the pilot testing of a future-oriented assessment instrument.