Project description

In the first project phase of ALEE, we set ourselves the challenge of designing and developing an adaptive AI-based learning platform for individual support in business lessons. We created a prototype learning platform for a selected subject area with over 700 different tasks of varying complexity and empirically evaluated it in a pilot study in the real world of education.

Even after 3 years of project duration, digital support for teaching and learning in schools, universities and other educational contexts remains at the top of the political agenda, as it can concretely address the major challenges of the education system due to the substantial heterogeneity of learners and opens up new potential for teaching-learning processes at all levels of the education system (e.g. KMK 2021). Adaptive learning systems are an important building block for successful digitalization in education. Digital adaptive learning platforms enable a personalization of learning that teachers can hardly achieve in practice. They can offer individual learning paths with tasks that correspond to the individual performance level and thus also support teachers in the design of differentiated lessons. However, such systems have so far been developed primarily for mathematics and science subjects, not for the humanities or social sciences. We have begun to address this gap by researching and developing adaptivity at the interface between STEM and social sciences, specifically in economics education.

Further development of the prototype of our learning platform in a second project phase towards an applicable adaptive AI-based system in economic education is therefore very promising and necessary in order to promote adaptive learning in schools on a broad scale. The innovative character of this research project lies in particular in the interdisciplinary cooperation and the special content domain, which places further demands on the AI system. The research project therefore promises a gain in knowledge that can be transferred to similar content domains and can be applied in the future far beyond the research carried out.

Cooperations

• IÖB Oldenburg

• Leuphana Universität Lüneburg