Künstliche Intelligenz zur Studienindividualisierung

Abstract

Self-regulated learning is an important aspect of lifelong and individual learning. In higher education however, the question arises how individualisation of study can be enabled despite narrow curricular structures and to what extent technological support is appropriate to achieve this aim. The idea of supporting an individualisation of study is being pursued by the BMBF-funded project SIDDATA. SIDDATA is a study assistance system that uses intelligent and self-learning algorithms to support adaptive and individualised studying. The focus is on AI-supported modules that help students define individual interests and pursue them throughout their studies. This paper deals with the idea behind the SIDDATA module »Specialised Interests«, the AI algorithms used, the first experiences with testing and using this module as well as with the potentials and obstacles higher education may have to face when using 214 Maren Lübcke, Johannes Schrumpf, Funda Seyfeli-Özhizalan und Klaus Wannemacher AI to strengthen individual study approaches for their students. The results of the first test experiences with the module confirm that the use of AI in the digital study assistant SIDDATA offers opportunities to promote individualisation in studies and in this way contributes to discovering potentials of AI-supported higher education and development.