Effective communication is essential in high-stress environments but stress often disrupts the flow of information and leads to miscommunication. While scenario-based training exercises are widely used, post-hoc reflection and analysis of verbal interactions remain challenging due to overlapping speech, limited analysis time, and the dynamic nature of these situations. This paper introduces ROGER, a novel visual analytics interface designed to support after-action reviews of communication during high-stress training scenarios. Developed in collaboration with police trainers through an iterative design study, ROGER integrates emotional voice metrics, heart rate variability, and spoken language content to provide a comprehensive analysis of team communication. The system enables a flexible in-depth exploration of communication patterns through motifs—repeated sequences or content elements—including those generated by a large language model (LLM) as well as predefined ones. Our approach addresses the limitations of existing tools, which focus primarily on content summarization or voice replays without incorporating emotional and stress-related voice data. We validated the utility through interviews with police trainers and conducted a workshop with medical first responders to investigate the potential for cross-domain applicability. Our findings provide preliminary evidence that ROGER supports effective team performance analysis in diverse high-stress environments. See also supplemental material at https://osf.io/pc6un

Creating Virtual Reality (VR) systems with high scenario fidelity is crucial, especially for scenario-based VR training. However, participatory VR design processes often require participants to have design knowledge, technical skills, or access to high-fidelity prototyping tools. Here, we present the development and evaluation of the co-design method CoLEBricks which uses building blocks for the co-creation of VR training scenarios for first responders. From the exploratory findings, we conclude that CoLEBricks supports end users in sharing needs and wishes regarding complex VR training scenarios, thereby ensuring the integration of relevant and realistic scenarios.

Abstract Virtual Reality (VR) training has become increasingly important for police first responders in recent years. Improving the training experience in such complex contexts requires ecological validity of virtual training. To achieve this, VR systems need to be capable of simulating the complex experiences of police officers ‘in the field.’ One way to do this is to add stressors into training simulations to induce stress similar to the stress experienced in real-life situations, particularly in situations where this is difficult (e.g., dangerous or resource-intensive) to achieve with traditional training. To include stressors in VR, this paper thus presents the concept of so-called ‘stress cues’ for operationalizing stressors to augment training in VR simulations for the context of police work. Considering the level of complexity of police work and training, a co-creation process that allows for creative collaboration and mitigation of power imbalances was chosen to access the police officers’ knowledge and experience. We assert that stress cues can improve the training experience from the trainer’s perspective as they provide novel interaction design possibilities for trainers to control the training experience. E.g., by actively intervening in training and dynamically changing the interaction space for trainees which also improves the trainee’s experience. Stress cues can also improve the trainee’s experience by enabling personalizable and customizable training based on real-time stress measurements and supplementing information for improved training feedback.