ADAPTIVE LEARNING SYSTEMS FOR A COMPETENCE-ENHANCING HUMAN-MACHINE INTERACTION

J. Lemm, M. Loehrer, Y. Gloy, T. Gries

Institut für Textiltechnik der RWTH Aachen University (GERMANY)
The development towards industrial 4.0 is based primarily on modern production machines in conjunction with digital technologies. Following this trend, the operation and development of advanced machinery becomes more complex and requires complex skills of employees in various qualification phases. From the viewpoint of increasing diversity of the workforce, in particular the growth of the group of older employees with the described age-correlated changes in the textile industry, the differential-dynamic job design in textile production seems more relevant than ever. Adaptive Learning Systems (ALS) are not only among the aspects of occupational safety and accessibility a promising technology but also in terms of the extent to which they are able to support their functionality and their internal models a differential-dynamic job design and thus help to realize a load optimization for different people. The use of ALS allows an age-appropriate work and a qualification-specific training of staff. This work-integrated training allows employees to develop their vocational capacity.

The implementation of technologically complex systems, such as an introduction of new production techniques in the context of "industrial 4.0" solutions has to lead to work-integrated, socio-technical equipment and work systems and thus also provides vocational and academic education with new challenges. Especially the ever-changing interaction of employees, machinery, control systems and work organization systems must be considered from the point that technology should serve people, not vice versa.

Therefore the development of Adaptive Learning Systems for a competence-enhancing human-machine interaction in production processes is necessary.