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

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.

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.

Inertial sensors integrated into smartphones provide a unique opportunity for implicitly identifying users through their gait. However, researchers identified different external factors influencing the user's gait and consequently impact gait-based user identification algorithms. While these previous studies provide important insights, a holistic comparison of external factors influencing identification algorithms is still missing. In this explorative work, we conducted a focus group with participants from biometrics research to collect and classify these factors. Next, we recorded the gait of 12 participants walking regularly and being influenced by eleven different external factors (e.g., shoes and floor types) in two separate sessions. We used a Deep Learning (DL) identification algorithm for analysis and validated the analysis results using within- and between- sessions data. We propose a categorization of gait covariates based on users' control levels. Floor types have the most significant impact on recognition accuracy. Finally, between-session analysis shows less accurate yet more robust results than within-session validation and testing.

Modern ubiquitous computing environments are increasingly populated with smart devices that need to know the identity of users interacting with them. At the same time, the number of authentications that a user needs to perform increases, as nowadays devices such as smart TVs require authentication which was not the case in earlier times. Even for single-person households, the need to authenticate against present smart devices in the environment appears at regular intervals, ranging from TVs to voice assistants, to gaming consoles. To reduce the need for repeated authentication, we explore the concept of a system that allows the sharing of users’ authenticated identity information between smart devices, similar to the concept of Single-Sign-On on the internet. Following a preliminary field study, we show that such a system can decrease the number of necessary authentications in a ubiquitous computing environment by 84.4%, increasing usability and security.

Augmented Reality (AR) technology enables users to superpose virtual content onto their environments. However, interacting with virtual content while mobile often requires users to perform interactions in mid-air, resulting in a lack of haptic feedback. Hence, in this work, we present the ARm Haptics system, which is worn on the user's forearm and provides 3D-printed input modules, each representing well-known interaction components such as buttons, sliders, and rotary knobs. These modules can be changed quickly, thus allowing users to adapt them to their current use case. After an iterative development of our system, which involved a focus group with HCI researchers, we conducted a user study to compare the ARm Haptics system to hand-tracking-based interaction in mid-air (baseline). Our findings show that using our system results in significantly lower error rates for slider and rotary input. Moreover, use of the ARm Haptics system results in significantly higher pragmatic quality and lower effort, frustration, and physical demand. Following our findings, we discuss opportunities for haptics worn on the forearm.