Cognitive integration through artificial intelligence in transdisciplinary sustainability research

For transdisciplinary sustainability research, the integration of knowledge is a central methodological challenge. Therefore, this exploratory pilot project analyzes the potential of recent developments in artificial intelligence (especially machine learning) to support knowledge integration in practical research.

Research approach

To achieve this goal, we will start by exploring suitable machine learning methods and their potential function for knowledge integration. Our focus will be on methods of so-called Deep Learning and their corresponding applications in the field of Natural Language Processing (NLP). In order to evaluate the potential contribution of these methods for knowledge integration, the methods will then be empirically connected to transdisciplinary research practice via an exemplary application case (e.g., from water or biodiversity research). For specific needs of knowledge integration arising in this application case, appropriate NLP applications will be set up and tested.

On the one hand, a general guideline for the use of NLP applications in transdisciplinary research practice will be developed. It will describe both the applied online resources and their use and will point out software components that may still need to be created. On the other hand, the selected application case will serve as a basis for evaluating the ways in which deep learning applications can already meaningfully support knowledge integration. This assessment will be made from both a research practical and a technology critical perspective. Here, the aim is to set the potential benefits against the ethical and ecological risks. Finally, a decision aid for the use of artificial intelligence in transdisciplinary research practice will be compiled.