Serious games, particularly board games, have long been employed in production management education to teach various concepts.While they have demonstrated educational effectiveness, their integration with emerging Industry 4.0 technologies remains limited.Furthermore, there is a lack of empirical research on how industry practitioners apply these digitization technologies in the workplace.To bridge this gap, we designed a course that integrates digital technologies into a traditional board game.We conducted two studies to evaluate both knowledge gains within the classroom and knowledge transfer back into the manufacturing industry.Our results show an improved understanding of the synergies between production management principles and Industry 4.0 technologies, as well as the real-world challenges students face when attempting to transfer this knowledge.Our work contributes pedagogical and practical perspectives on how technology-enhanced serious games can extend learning in and beyond the classroom.

Numerous prior studies have investigated real-time assembly instructions using Augmented Reality (AR). However, most such experiments were conducted in laboratory settings with simplistic assembly tasks, failing to represent real-world industrial conditions. To ascertain to what extent results obtained in a laboratory environment may differ from studies in actual industrial environments, we carried out a user study with 32 manufacturing apprentices. We compared assembly task execution results in two settings, a classroom and an industrial workshop environment. To facilitate the experiments, we developed AR-guided manual assembly systems for simple and more complex assets. Our findings reveal a significantly improved task performance in the industrial workshop, reflected in faster task completion times, fewer errors, and subjectively perceived higher flow. This contradicted participants' subjective ratings, as they expected to perform better in the classroom environment. Our results suggest that the actual manufacturing environment is critical in evaluating AR systems for real-world industrial applications.

Data driven approaches for decision making applied to automated driving require appropriate generalization strategies, to ensure applicability to the world's variability. Current approaches either do not generalize well beyond the training data or are not capable to consider a variable number of traffic participants. Therefore we propose an invariant environment representation from the perspective of the ego vehicle. The representation encodes all necessary information for safe decision making. To assess the generalization capabilities of the novel environment representation, we train our agents on a small subset of scenarios and evaluate on the entire diverse set of scenarios. Here we show that the agents are capable to generalize successfully to unseen scenarios, due to the abstraction. In addition we present a simple occlusion model that enables our agents to navigate intersections with occlusions without a significant change in performance.