Pixel International Conferences

The competence to create knowledge independently is relevant for scientists and other academics in many professional fields and it will continue to gain importance in the context of shorter innovation cycles, increasing digitisation and agile, self-directed forms of work. Introducing students to scientific work is an essential task of academic teaching.
In view of large numbers of participants and limited personnel resources, many teachers have so far resorted to a mixture of classic lectures, group work on individual topics in the course and examinations in the form of individual written papers on individual aspects of scientific work.
These general conditions give rise to four challenges which we would like to address in the context of the developmental project.

• suboptimal teaching formats and insufficient feedback to students due to high numbers of participants
• educational potential of a change of perspective between the role of researcher and reviewer remains unused
• self-organized or random formation of working groups are not optimal for the learning process

The challenges presented here in short do not only arise within the framework of our module, but are typical for all events in which students are to learn scientific work. Our research pursues the goal of enabling students to learn to research in digital teams and thus enable them to receive scientific research and to become active in research themselves. For this purpose, the concept of Research-Based Learning in digital teams is to be implemented using a peer-review-based AI online tool. We will use a software that was developed in a joint university didactic project and which we will implement for the first time in a study context. We work closely together with the developers of the software and connect conceptually to the current scientific discourse. Our application context is first of all a new module for scientific work (BA course of studies "Economic Sciences", 4th semester ~70 students, 6 ECTS, first offered in summer semester 2019).

Keywords: artificial intelligence, research-based learning, knowledge creation.