Stefan Greuter

Context: Professional communication is a core competence for Swiss retail apprentices and is assessed in the final practical examination, yet role-play practice is hard to scale because it depends on trained actors and expert feedback. Generative AI and immersive XR offer a possible route to repeatable practice in routine classroom settings. Approach: We implemented an AI-supported customer role-play and reflective coaching system on XARA and evaluated it in a single immersive XR condition using Meta Quest 3. Nine Swiss retail apprentices completed one approximately 11-minute session, followed by a group interview analysed using reflexive thematic analysis. Findings: Apprentices described the XR experience as engaging and potentially useful for practising difficult interactions. They also identified limited conversational flexibility, insufficient situation-specific coaching feedback, and practical requirements for classroom deployment. Conclusions: The study provides early design insights into AI-supported communication training delivered in XR. Because no non-immersive condition or learning measures were included, the findings describe initial learner perceptions and do not establish effects of the XR modality, learning outcomes, or scalability.

Marine plastic and microplastic pollution can be difficult to communicate because its impacts unfold slowly, are geographically dispersed, and often remain imperceptible. We address this challenge through In Deep Water, a public-facing interactive installation that translates marine science research into collaborative exploration. The system combines a sculpted virtual marine biorama with custom-built physical controllers that allow players to navigate simulated waterways, locate discarded research beacons, and decode situated data from studies conducted in Aotearoa, New Zealand. The installation is developed in two complementary modes, one designed for open-ended exploration and the other structured around time-limited play, to investigate how different interaction conditions shape engagement and sense-making. Our contribution is a replicable design approach for turning environmental datasets into embodied, social experiences, supporting HCI researchers and practitioners designing data-driven public installations and educators and cultural institutions seeking accessible formats for research translation and environmental engagement.

Motion sickness (MS) is a prevalent condition that can significantly degrade user comfort and immersion, particularly in virtual reality (VR) environments. Accurate prediction models are essential for early detection and mitigation of MS symptoms, thereby improving the overall VR experience. Most existing approaches rely on bio-physiological data acquired through body-mounted sensors, which may restrict user mobility and diminish immersion. This study proposes a less intrusive alternative, leveraging head and torso kinematic data for MS prediction. We introduce a hybrid Convolutional–Recurrent Neural Network (C-RNN) designed to capture both spatial and temporal features for enhanced classification accuracy. Using a dataset of 40 participants, the proposed C-RNN outperformed traditional machine learning models—including Support Vector Machines (SVMs), k-Nearest Neighbors (KNN), Decision Trees (DT), and a baseline Recurrent Neural Network (RNN)—across multiple evaluation metrics. The C-RNN achieved 85.63% accuracy, surpassing SVM (60%), KNN (73.75%), DT (74.38%), and RNN (81.88%), with corresponding gains in precision, recall, F1-score, and ROC AUC. These results demonstrate that head–torso motion patterns provide sufficient predictive signal for accurate MS detection, offering a non-intrusive, efficient alternative to physiological sensing that supports improved comfort and sustained immersion in VR.

Drowsy driving is a major contributor to road accidents, as reduced vigilance degrades situational awareness and reaction control. Reliable assessment of alertness versus drowsiness can therefore support accident prevention. Key gaps remain in physiology-based detection, including robust identification of microsleep and transient vigilance shifts, sensitivity to fatigue-related changes, and resilience to motion-related signal artifacts; practical sensing solutions are also needed. Using Electroencephalogram (EEG) recordings from the MIT-BIH Polysomnography Database (18 records; >80 h of clinically annotated data), we framed wakefulness–drowsiness discrimination as a binary classification task. From each 30 s segment, we extracted 61 handcrafted features spanning linear, nonlinear, and frequency descriptors designed to be largely robust to signal-quality variations. Three classifiers were evaluated—k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT)—alongside a DT-based bagging ensemble. KNN achieved 99% training and 80.4% test accuracy; SVM reached 80.0% and 78.8%; and DT obtained 79.8% and 78.3%. Data standardization did not improve performance. The ensemble attained 100% training and 84.7% test accuracy. While these results indicate strong discriminative capability, the training–test gap suggests overfitting and underscores the need for validation on larger, more diverse cohorts to ensure generalizability. Overall, the findings demonstrate the potential of machine learning to identify vigilance states from EEG. We present an interpretable EEG-based classifier built on clinically scored polysomnography and discuss translation considerations; external validation in driving contexts is reserved for future work.

This paper presents a novel method for evaluating wayfinding within a public building to provide meaningful insights for stakeholders. Our approach features unique methods for both data collection and evaluation, with a holistic digital capture of the entire virtual environment experienced by participants, maintained in an interactive format for in-depth analysis. We also captured and output data in point cloud formats, raw data text files, and task-specific metrics, which support interactive replays of participants’ experiences. We developed algorithms to extract meaningful insights from the raw data based on assumptions about wayfinding characteristics. The contribution is a flexible framework that can be easily adapted for future projects with adjustable variables to suit specific applications.

Virtual Reality (VR) exhibitions are becoming increasingly popular for creating immersive and interactive experiences. One particular use case in research is death simulations in VR, where users can experience or explore the concept of death in a controlled environment. Death is a profound theme that offers opportunities for emotional growth, empathy, and deep reflection. This project presents Passing Electrical Storms, an innovative virtual reality art installation by Shaun Gladwell, designed to evoke the human experience of dying and an out-of-body journey through the universe. The installation combines VR, sensing technologies, and tactile feedback to create a visually stunning and emotionally immersive experience. Participants journey from a simulated cardiac arrest to brain death, moving through inner-body experiences and vast cosmic expanses, provoking contemplation on mortality and the sublime nature of the universe. This work demonstrates the potential of VR to engage users with complex emotional and existential themes, providing valuable insights for future VR design in the field of HCI.

Ingestible sensors have become smaller and more powerful and allow us to envisage new human-computer interactions and bodily play experiences inside our bodies. Users can swallow ingestible sensors, which facilitate interior body sensing functions that provide data on which play experiences can be built. We call bodily play that uses ingestible sensors as play technologies “ingestible play”, and we have adopted a research-through-design (RtD) approach to investigate three prototypes. For each prototype, we conducted a field study to understand the player experiences. Based upon these results and practical design experiences, we have developed a design framework for ingestible play. We hope this work can guide the future design of ingestible play; inspire the design of play technologies inside the human body to expand the current bodily play design space; and ultimately extend our understanding of how to design for the human body by considering the bodily experience of one’s interior body.

Human-Computer Integration (HInt) is an emerging new paradigm in the human-computer interaction (HCI) field. Its goal is to integrate the human body and the computational machine. This monograph presents two key dimensions of Human-Computer Integration (bodily agency and bodily ownership) and proposes a set of challenges that we believe need to be resolved in order to bring the paradigm forward. Ultimately, our work aims to facilitate a more structured investigation into human body and computational machine integration.