Functions and consequences of artificial intelligence
in the science and higher education organization
Innovation analysis and transfer development
THE GROUP
New approaches in research
The scientific system in the age of artificial intelligence
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Innovative research group funded by the German Federal Government
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Reflexive, interdisciplinary perspective on the science system
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Proprietary AI technologies and big-data analysis
With the launch of the ChatGPT application at the end of 2022, a decisive step in the development of (generative) artificial intelligence was taken. At present, it remains unclear how far-reaching the consequences will be for professional knowledge production and knowledge representation. What is evident, however, is that the spread of artificial intelligence poses challenges to all areas of scientific practice. For the central knowledge institutions of our society—the expert systems of universities, higher education institutions, and research organizations—various scenarios are under discussion, ranging from high expectations of optimization to pessimistic concerns about a creeping devaluation of knowledge. Ethical issues, as well as questions concerning the profession of researchers, add further dimensions.
Successful Research Proposal within a Collaborative Framework
The KIWIT research group is dedicated to the study of artificial intelligence specifically within the context of science—its organizational and work processes. The group began its work in early summer 2025 as an interdisciplinary consortium of the Universities of Oldenburg and Bielefeld and the Northern Business School in Hamburg. Its foundation followed a competitive call for proposals by the Federal Ministry of Research, Technology, and Space for the funding of research groups in the field of science and higher education studies (WiHo). A joint project proposal was successful. As one of two consortia, KIWIT will initially receive funding until 2029, with a second funding phase extending to 2033 as the goal. Given today’s pace of technological progress, this is a comparatively long period—and thus a stimulating challenge for the research group.
The team will investigate the socio-technical diffusion of artificial intelligence across the core domains of research, study and teaching, and university organization, within six subprojects. Both information science and social science research methods (qualitative and quantitative) will be applied. Notably, artificial intelligence will not only be the object of investigation but will also be actively implemented through in-house programming and big-data analysis. This creates a methodological-reflexive orientation. Some projects will specifically address how artificial intelligence can be combined with social science methods.
Key Collaborative Competences
The three partner institutions bring complementary expertise to the consortium. The University of Oldenburg contributes a longstanding focus on educational management and higher education research; Bielefeld University provides expertise in organizational sociology; and the Northern Business School in Hamburg represents the computer science focus and provides AI infrastructure, including server management, for the group. The members of the research group and their networks represent disciplinary breadth, spanning computer science, sociology, educational and natural sciences, philosophy, and the theory of science.
The project leadership is jointly held by educational researcher Prof. Dr. Heinke Röbken (consortium coordination), sociologists and organizational scholars Prof. Dr. Stefan Kühl and Prof. Dr. Marcel Schütz, as well as computer scientist and AI expert Prof. Dipl.-Ing. Ernst Reinking. An advisory board of distinguished scholars accompanies the work of the research group.
Updated: July 28, 2025
Developments in generative AI will have a lasting impact on knowledge-intensive fields of work. How will sophisticated algorithmic assistance systems and human cognitive performance relate to each other in the future? What consequences can be expected for human-machine interaction? These are the questions that interdisciplinary AI research addresses.
Generative AI is already being used in a variety of ways in individual and collaborative learning and work processes during university studies. This has implications for knowledge acquisition, the internalization of knowledge content, and performance assessment.
An overview of the research group's overall scientific scope (click to enlarge). [Image of research group's overall scientific scope]