Virtual reality has proven to be a valuable addition in the tool belt of teachers. Immersive learning environments are applied in various settings, including, but not limited to the medical and nursing domain. In this study we present “We care in VR”, a simulation for practicing nursing tasks for care at home, a part of nursing that is currently underrepresented in available VR applications. We investigate how realistic interactions are perceived by end users compared to consistent usage of buttons on the controllers and how they affect the ease of use of the simulation. We conduct an empirical study with 50 participants from three vocational schools of nursing and a university of applied sciences. Results suggest that our simulation already works quite well and is accepted by the target group, but still needs improvement regarding ease of use, especially for users without any previous experience with VR applications.

In der jüngeren Vergangenheit wurde viel über die Auswirkungen der Digitalisierung auf Politik und Demokratie diskutiert. Gleichzeitig schwächt ein globaler Krisenkreislauf das Vertrauen in die politische Handlungsfähigkeit. Dieser Beitrag untersucht, welche Alternativen die digitale Transformation für die gesellschaftliche Willensbildung bieten könnten. Er identifiziert Handlungsfelder digitalisierter Politik auf regionaler wie globaler Ebene und mögliche Lösungsansätze. Technologien wie KI, Distributed-Ledger-Verfahren und Virtual Reality können die Informationsverarbeitung im Vorfeld politischer Entscheidungen und die Stärkung bürgerlicher Partizipation unterstützen. Ferner bergen sie das Potenzial, tiefgreifende Veränderungen in politischen Systemen zu bewirken, indem menschliche Aufgaben an technische Systeme delegiert werden. Dabei ergibt sich ein schmaler Grat zwischen Utopie und Dystopie, wenn Effizienzerwartungen auf Fragen nach demokratischer Legitimation treffen.

Driving simulators are essential for the development of vehicle sys-tems, as they enable safe and efficient user engagement. Their validity determines the extent to which the results from empirical user studies obtained in driving simulators can be transferred to real-world driving situations.

This study examines the gaze behavior of participants in a within-subject de-sign, both in a real vehicle and in a driving simulator with a digital road replica. Using gaze-point plots and expert ratings, we compare fixation patterns across three road sections (City Drive, Rural Drive, Highway). The results visually in-dicate a moderate to high similarity in gaze distributions, suggesting consistent fixation patterns in both environments, with some notable exceptions on an indi-vidual level and generally highest matches in the City Drive.

However, further statistical analyses are necessary to quantitatively confirm similarities and assess systematic differences.

Digital media have become an integral part of everyday life, education and work. However, intensive and problematic media use, and in particular problematic smartphone use has been shown to reliably predict reduced well-being and increased stress. Therefore, it is essential to investigate the factors that lead to problematic smartphone use and respective negative well-being outcomes and to develop interventions that effectively address these factors. Considering self-control and fear of missing out (FoMO) as key psychological factors promoting problematic smartphone use, we present a conceptual model explaining the emergence of digital stress due to problematic smartphone use, upon which we built a blended training intervention against digital stress. A controlled trial evaluation of the training intervention provided data at multiple time points for multilevel regression analyses on a sample of 175 university students. The results indicated that the intervention was effective in reducing FoMO (fear of missing out) and increasing self-control. Indirect effects suggested that both the reduction in FoMO and the gain in self-control effectively reduced emotional irritation and perceived stress via a reduction in problematic smartphone use. Conclusively, the findings identify key psychological factors that promote problematic smartphone use and demonstrate that these factors can be effectively addressed through appropriate psychological interventions.

This study examines how Large Language Models (LLMs) can reduce biases in text-to-image generation systems by modifying user prompts. We define bias as a model's unfair deviation from population statistics given neutral prompts. Our experiments with Stable Diffusion XL, 3.5 and Flux demonstrate that LLM-modified prompts significantly increase image diversity and reduce bias without the need to change the image generators themselves. While occasionally producing results that diverge from original user intent for elaborate prompts, this approach generally provides more varied interpretations of underspecified requests rather than superficial variations. The method works particularly well for less advanced image generators, though limitations persist for certain contexts like disability representation. All prompts and generated images are available at https://iisys-hof.github.io/llm-prompt-img-gen/