artificial intelligence (AI)

Demographic change is accompanied by both a growing demand for care and a shortage of qualified nursing staff. Consequently, AI-based technologies are increasingly becoming a focus of care-related innovations. Their aim is to reduce workload pressure, save time, and enhance the attractiveness of the nursing profession. Using the example of AI-supported documentation systems for admission interviews, this article examines to what extent such systems can contribute to improvements in work processes and care quality, focusing on the perspectives of nursing professionals and nursing experts. The results indicate potential for workload relief, enhanced documentation quality, and the reallocation of time resources toward direct patient care. However, realizing these potentials requires a human-centered and context-sensitive implementation approach.

The rapid development of artificial intelligence (AI) is constantly opening new opportunities, particularly in training for the factory of the future. For employees, this not only means a significant advantage in the actual manufacturing process, but also in the field of continuing education. This paper provides an overview of AI tutoring systems continuing education in the context of Industry 4.0 by presenting a categorization that discusses different approaches of AI tutoring systems by learning methods, application areas and their respective technologies. In addition, an outlook on the disruptive effect of generative AI on AI tutoring systems in Industry 4.0 is given.

In process engineering, one thinks of production operations that are controlled or regulated by sensors and actuators. And any realization of matter transformation is based on a physical substratum, which holds equally for living systems and their behaviour. The article distinguishes between three system levels: the functional level, the interface to the environment and the cognitive level of. Using these three levels, the learning cycle or the previous Cognitive Loop can be very well illustrated. If one compares with this way of distinction the Bio-Informatization of human intelligence with the technical development stages of mechanization, automation, regulation and deep learning, then the cybernetic-sociological term “operational closure” becomes understandable. It becomes obvious that in the context of a digitized culture of production and organization, we should be prepared for a new kind of cognitive loop based on silicon (SI), an intelligent system behavior via ...