Augmented Reality (AR) is promising in providing individual learning support for students.However, it is currently unknown which display technology is appropriate to use in classroom settings.In this work, we investigate different AR displays and their usability

This paper presents a virtual reality (VR) study that examines how multiple road users jointly interact with an autonomous vehicle (AV) in complex traffic scenarios. Moving beyond dyadic studies (e.g., AV-pedestrian or AV-passenger), our multi-user setup simulates ambiguous all-way stop intersections involving a pedestrian, a human driver in a conventional vehicle, and a passenger in an AV, all interacting simultaneously with the AV. We investigated how users perceive and respond to two distinct types of AV behaviors: an efficient AV that proceeds as soon as it is safe to do so, and a prosocial AV that yields to others before entering the intersection. Sixteen groups of three participants (N=48) took part in the study, with each group interacting with a single AV type across four ambiguous traffic scenarios. Our findings show that even simple AV behavior logics can meaningfully shape crossing negotiation dynamics and highlight how trust and perception can vary across different user roles. We conclude by discussing how our methods and insights can inform the research and design of AV interactions in complex multi-agent traffic environments.

Gait is a distinctive behavioral trait, yet its vulnerability against imitation remains underexplored in immersive environments. We present a study investigating how real-time visual feedback in virtual reality (VR) influences a person’s ability to mimic another’s gait. Through two experiments, we first identify the most usable feedback design (N = 8), then evaluate its impact on imitation performance compared to a baseline without feedback (N = 18). We analyze positional and rotational similarity between participants and target avatars, examining the influence of avatar–user gender matching and repeated practice. Our findings reveal that visual feedback significantly improves rotational alignment and that practice leads to measurable improvements in mimicry accuracy. We discuss implications for avatar embodiment, personalization in VR applications, and potential considerations for behavioral biometric systems. We also contribute a publicly available dataset of gait mimicry in VR, supporting further research on motion learning and imitation.

Spatial Augmented Reality (SAR) can enhance learning experiences through interactive, real-time digital information overlays. Using SAR, content can be projected directly onto physical paper to provide students with in situ task feedback. Our work explores how students perceive different types of SAR feedback. We first identified feedback methods and dimensions from a literature review. We then conducted an expert focus group (N = 5) of professionals who had backgrounds in education and teaching experience. With the focus group, we aimed to expand on the literature review results to identify feedback modalities and dimensions commonly used in classrooms today. Next, we performed a field study (N = 16) with high school students in which we compared the perception of different feedback modalities (text, image, video) and familiarity (neutral, unfamiliar, familiar) in a classroom setting. Our results revealed that perceived user distraction and novelty are significantly affected by feedback modality through a large effect, with videos being perceived as more distracting and more novel. Familiar, trusted individuals best deliver positive feedback, whereas negative feedback from people should be avoided. We discuss the usage of feedback modalities in various contexts, providing a foundation for future use of SAR feedback in education.

Behavioral Biometrics in Virtual Reality (VR) allow for implicit user identification, as the head- and hand-movements that can be captured from the head-mounted display and the controllers are highly descriptive of the user’s true identity. Such body movements have been explored in the past; however, to date, it is unclear how they perform in settings where more than one person interacts in a shared virtual environment. In this work, we explored through a user study (N=40) how behavioral biometrics in VR change when one or more persons interact with each other in a shared virtual environment and whether this is influenced by the nature of the interaction itself. We find that user identification is possible with up to 83.38 % by applying deep learning models, and that particularly cooperative interactions between multiple VR users lead to highly identifiable body movements. Our results help in advancing behavioral biometrics for seamless user identification in VR, as a viable alternative to using PINs and passwords.

The design of self-driving vehicles requires an understanding of the social interactions between drivers in resolving vague encounters, such as at un-signalized intersections. In this paper, we make the case for social situation awareness as a model for understanding everyday driving interaction. Using a dual-participant VR driving simulator, we collected data from driving encounter scenarios to understand how (N=170) participant drivers behave with respect to one another. Using a social situation awareness questionnaire we developed, we assessed the participants’ social awareness of other driver’s direction of approach to the intersection, and also logged signaling, speed and speed change, and heading of the vehicle. Drawing upon the statistically significant relationships in the variables in the study data, we propose a Social Situation Awareness model based on the approach, speed, change of speed, heading and explicit signaling from drivers.

In automotive user interface design, testing often starts with lab-based driving simulators and migrates toward on-road studies to mitigate risks. Mixed reality (XR) helps translate virtual study designs to the real road to increase ecological validity. However, researchers rarely run the same study in both in-lab and on-road simulators due to the challenges of replicating studies in both physical and virtual worlds. To provide a common infrastructure to port in-lab study designs on-road, we built a platform-portable infrastructure, Portobello, to enable us to run twinned physical-virtual studies. As a proof-of-concept, we extended the on-road simulator XR-OOM with Portobello. We ran a within-subjects, autonomous-vehicle crosswalk cooperation study (N=32) both in-lab and on-road to investigate study design portability and platform-driven influences on study outcomes. To our knowledge, this is the first system that enables the twinning of studies originally designed for in-lab simulators to be carried out in an on-road platform.

Remote tutoring has gained significant traction due to technological advances, primarily relying on video-conferencing tools. However, these tools are not specifically designed for tutoring. Positive tutoring experiences rely on interaction with and immediate feedback from the tutor. Moreover, traditional methods like writing, reading, and drawing in physical spaces enhance learning outcomes. Integrating these methods with physical materials and digital remote guidance could improve remote learning experiences. A technology that enables this integration is spatial augmented reality (SAR), which utilizes projection to integrate digital content into the physical world. This work introduces a Spatial Augmented Reality (SAR) remote tutoring tool that enables augmented annotations and projected video streams. We conducted a between-subject lab study (N=18) comparing learning experiences in remote tutoring between standard video conferencing tools and the introduced Spatial Augmented Reality (SAR) tool. Qualitative analysis revealed the Spatial Augmented Reality (SAR) system's benefits over video conferencing, specifically immediate feedback, in-situ annotations, improved interactivity, and enhanced social presence.

Drones (micro unmanned aerial vehicles) are becoming more prevalent in applications that bring them into close human spaces. This is made possible in part by clear drone-to-human communication strategies. However, current auditory and visual communication methods only work with strict environmental settings. To continue expanding the possibilities for drones to be useful in human spaces, we explore ways to overcome these limitations through physical touch. We present a new application for drones--physical instructive feedback. To do this we designed three different physical interaction modes for a drone. We then conducted a user study (N=12) to answer fundamental questions of where and how people want to physically interact with drones, and what people naturally infer the physical touch is communicating. We then used these insights to conduct a second user study (N=14) to understand the best way for a drone to communicate instructions to a human in a movement task. We found that continuous physical feedback is both the preferred mode and is more effective at providing instruction than incremental feedback.

High-fidelity driving simulators can act as testbeds for designing in-vehicle interfaces or validating the safety of novel driver assistance features. In this system paper, we develop and validate the safety of a mixed reality driving simulator system that enables us to superimpose virtual objects and events into the view of participants engaging in real-world driving in unmodified vehicles. To this end, we have validated the mixed reality system for basic driver cockpit and low-speed driving tasks, comparing the use of the system with non-headset and with the headset driving conditions, to ensure that participants behave and perform similarly using this system as they would otherwise. This paper outlines the operational procedures and protocols for using such systems for cockpit tasks (like using the parking brake, reading the instrument panel, and turn signaling) as well as basic low-speed driving exercises (such as steering around corners, weaving around obstacles, and stopping at a fixed line) in ways that are safe, effective, and lead to accurate, repeatable data collection about behavioral responses in real-world driving tasks.

A growing number of studies use a "ghost-driver" vehicle driven by a person in a car seat costume to simulate an autonomous vehicle. Using a hidden-driver vehicle in a field study in the Netherlands, Study 1 (N = 130) confirmed that the ghostdriver methodology is valid in Europe and confirmed that European pedestrians change their behavior when encountering a hidden-driver vehicle. As an important extension to past research, we find pedestrian group size is associated with their behavior: groups look longer than singletons when encountering an autonomous vehicle, but look for less time than singletons when encountering a normal vehicle. Study 2 (N = 101) adapted and extended the hidden-driver method to test whether it is believable as online video stimuli and whether car characteristics and participant feelings are related to the beliefs and behavior of pedestrians who see hidden-driver vehicles. As expected, belief rates were lower for hidden-driver vehicles seen in videos compared to in a field study. Importantly, we found noticing no driver was the only significant predictor of belief in car autonomy, which reinforces prior justification for the use of the ghostdriver method. Our contributions are a replication of the hidden-driver method in Europe and comparisons with past US and Mexico data; an extension and evaluation of the ghostdriver method in video form; evidence of the necessity of the hidden driver in creating the illusion of vehicle autonomy; and an extended analysis of how pedestrian group size and feelings relate to pedestrian behavior when encountering a hidden-driver vehicle.

Top-down simulations of autonomous intersections neglect considerations for the human experience of being in cars driving through these autonomous intersections. To understand the impact that perspective has on perception of autonomous intersections, we conducted a driving simulator experiment and studied the experience in terms of perception, feelings, and pleasure. Based on this data, we discuss experiential factors of autonomous intersections that are perceived as beneficial or detrimental for the future driver. Furthermore, we present what the change of perspective implies for designing intersection models, future in-car interfaces and simulation techniques.

Researchers and designers of in-vehicle interactions and interfaces currently have to choose between performing evaluation and human factors experiments in laboratory driving simulators or on-road experiments. To enjoy the benefit of customizable course design in controlled experiments with the immediacy and rich sensations of on-road driving, we have developed a new method and tools to enable VR driving simulation in a vehicle as it travels on a road. In this paper, we describe how the cost-effective and flexible implementation of this platform allows for rapid prototyping. A preliminary pilot test (N = 6), centered on an autonomous driving scenario, yields promising results, illustrating proof of concept and indicating that a basic implementation of the system can invoke genuine responses from test participants.