Software-supported and with state-of-the-art learning methods, a project from science and practice has provided information on when employees work most productively. […]
In competence assistance systems (KAS), different AI technologies are used, including machine learning and natural language dialogue systems (so-called chatbots). Future KAS must be able to recognize in which affective and cognitive states employees are. Such a project under the name Kern (=Develop competencies and use them correctly), which began in November 2018 under the direction of Karlsruhe Institute of Technology (KIT), has now been successfully completed.
The different approaches that were developed for the development and use of competencies were implemented in software – based prototypes during the course of the project and successfully tested in company learning and experimental rooms at the companies B. Braun Melsungen and Campusjäger from Karlsruhe.
“Using a data-based approach and the use of artificial intelligence methods, approaches for AI-based competence assistance systems were designed. The results from the experimental rooms confirm the great potential for employees and employers: Such a system can suggest further training and new tasks to employees – this is an effective combination of learning and working,“ project leader and Professor Alexander Mädche of Karlsruhe Institute of Technology (KIT) is convinced.
Kern focuses on the detection and processing of so-called flow states. Mädche defines flow as a ” state of concentration, of rising in an activity; in flow, requirements and abilities fit together. Flow is a desirable state that not only leads to better performance, but to a higher level of employee well-being.”In the project, KIT succeeded in developing an AI-based assistance system that automatically detects flow based on physiological data using machine learning methods and can thus intelligently support employees in digital competence development.
In total, KIT conducted more than 70 interviews with employees on digital competence models and AI – based KAS in the two learning and experimental rooms and collected more than 13.5 million physiological data points in two field experiments.
In the project, TÜV Rheinland has focused on the conception of digital competence models and associated training measures, among other things, TÜV has created five podcasts for managers and employees. As a technology partner, SAP SE designed the overarching software architecture and participated in the development of the AI-based competence assistance systems.
“The first impression of the service desk employees with regard to the competence assistant was very positive and the chatbot could help the employees to identify individual strengths and weaknesses,” says Mareike Schulte, Director Global IT Support at B. Braun Melsungen. And Jannik Keller from Campusjäger adds: “We see great potential for the coming years to coach employees and job seekers in real time, individually and automatically via such digital assistants for their careers and everyday work.“
The project was funded within the framework of a funding guideline of the Federal Ministry of Labour and Social Affairs (BMAS) on learning and experimental spaces under the umbrella of the Initiative Neue Qualität der Arbeit (INQA). KIT, Campusjäger, SAP, TÜV Rheinland, B. Braun Melsungen were involved in the project. Further information and results https://kern-kas.org/ and http://www.kompetenzbot.de/.
* Hans Königes is Head of Jobs & amp; Career and thus responsible for all topics relating to the labor market, jobs, professions, salaries, personnel management, recruiting and social media in professional life.