AI methods for specific challenges in education

The hope of a revolution in education through digital technologies has been around for a long time. Despite the ubiquity of digital technologies, the basic teaching methods, particularly in dealing with the increasing heterogeneity of the student body, have remained largely unchanged. Individual support by teachers often reaches its limits. Adaptive intelligent systems can provide support here, as has been shown empirically. They can provide individually adapted tasks and assistance for pupils and thus enable effective individual support. However, it is crucial for the successful use of these systems that they are integrated into joint lessons and that teachers are trained accordingly. 

The successful implementation and continuous improvement of such systems requires the joint efforts of politics, science, business and educational practice. Politicians are called upon to support approaches that promote close interaction between science, publishers, EdTech companies and educational practice. This also includes ensuring sufficient funding for

adaptive educational media and the sensible use of educational data for AI methods. The use of individual learner data for AI in education requires a careful balance between data protection, ethical concerns and the significant benefits such as personalized learning. This data should be made available in a transparent form for adaptive learning systems and anonymized for research in order to increase the quality and efficiency of teaching.

The optimization of teaching is a key step towards overcoming central challenges of the school system, in particular the successful acquisition of basic skills by a heterogeneous student body.

 Read the entire Facts & Findings here as a PDF. Please note, the publication is only available in German.