AI-supported assistance systems in enterprise learning processes - prospects and limitations

Industry 4.0 and Smart Factory are associated with major changes in the industrial world. The innovative forms of networking, communication and collaboration between people, machines and products have led to a new type of production system in which information and knowledge are exchanged more quickly and efficiently. As a result, among other things, the role of cognitive assistance systems and their supporting function for employees within production processes has gained in importance, both in the training phase and in the active work phase. Especially for on-the-job learning, these assistance systems open up a wide range of opportunities. This paper focuses on the opportunities and challenges associated with the use of cognitive assistance systems for workplace learning. We discuss current research on the potential uses of these assistance systems, particularly for process accomplishment as well as for supporting specific groups of employees. Furthermore, we address the limitations for the use of these systems. At the end of the paper, we identify three focus areas for the future development of AI in industrial processes and for research on this.

Familiarity and Complexity during a Takeover in Highly Automated Driving

This paper shows, how objective complexity and familiarity impact the subjective complexity and the time to make an action decision during the takeover task in a highly automated driving scenario. In the next generation of highly automated driving the driver remains as fallback and has to take over the driving task whenever the system reaches a limit. It is thus highly important to develop an assistance system that supports the individual driver based on information about the drivers’ current cognitive state. The impact of familiarity and complexity (objective and subjective) on the time to make an action decision during a takeover is investigated. To produce replicable driving scenarios and manipulate the independent variables situation familiarity and objective complexity, a driving simulator is used. Results show that the familiarity with a traffic situation as well as the objective complexity of the environment significantly influence the subjective complexity and the time to make an action decision. Furthermore, it is shown that the subjective complexity is a mediator variable between objective complexity/familiarity and the time to make an action decision. Complexity and familiarity are thus important parameters that have to be considered in the development of highly automated driving systems. Based on the presented mediation effect, the opportunity of gathering the drivers’ subjective complexity and adapting cognitive assistance systems accordingly is opened up. The results of this study provide a solid basis that enables an individualization of the takeover by implementing useful cognitive modeling to individualize cognitive assistance systems for highly automated driving.

Transformation of Cognitive Assistance Systems into Augmented Reality

The field of Augmented Reality (AR) has received increasing attention in recent years, as AR can be applied to a wide range of problems. The use of AR offers great potential, especially in the industrial sector. Among many applications in this area, one promising application is the augmentation of cognitive assistance systems. Much research has already been done on the development of augmented support systems but it is still lacking on how to transform existing assistance systems into an AR application. This paper focuses on this transformation and presents a process model that intends to support the migration or digitalization of existing support systems into an AR application. An experimental study validates the proposed approach and derives recommendations for action.