Doctoral Consortium Presentations

Room: Glasshaus

Designing Context-Sensitive Haptic Communication for Collaboration in Virtual Reality

ID: 1024
Presenter: Hatira, Amal

Abstract

Collaborative virtual environments (CVEs) allow users to work together in shared virtual spaces, but effective collaboration requires more than visual co-presence. Collaborators must coordinate time-critical actions, monitor each other’s state and availability, and feel that they are acting together. My PhD research examines how lightweight wearable haptic feedback can support these needs in VR. Using smartwatch-based feedback, my recent work showed that coordination cues improved efficiency, while awareness cues improved partner monitoring and social presence. Building on this, my dissertation develops design principles and adaptive strategies for wearable haptics in collaborative VR.

Designing Controller-Free Interaction Techniques for Selection and Accurate Manipulation in Extended Reality

ID: 1025
Presenter: Turkmen, Rumeysa

Abstract

Extended Reality (XR) applications are commonly used in domains such as surgical training, education, and rehabilitation. In many of these scenarios, handheld controllers are impractical or unavailable: users may need to preserve sterility, keep their hands occupied with instruments, or interact through optical hand tracking alone. My doctoral research investigates how controller-free XR interaction techniques can be designed to support effective selection and accurate manipulation under realistic interaction conditions. I structure this problem along three complementary dimensions: visibility, when targets are occluded; spatial context, when interaction distance changes; and control accuracy, when fine-grained object manipulation is required without physical support.

XR for Climate Change Communication: Designing for General Audiences Through Gamification and Co-Creation

ID: 1026
Presenter: Mrázová, Albína

Abstract

Extended Reality technologies - in the case of this doctoral research, mainly augmented reality (AR) and virtual reality (VR) - offer great opportunities for communicating climate change risks and preparedness to the general public audience. However, current research mainly focuses on specific target groups such as students and trained professionals, leaving general-public applications underexplored. A related gap is focusing on methodology: the use of gamification and serious games within AR and VR experiences lacks documented design rationale - existing work rarely records how gamification elements were selected or why, or how these choices account for the distinct affordances of the two forms of technology. This doctoral research is focused on how gamification elements can be selected and justified for AR and VR experiences aimed at general audiences, developing a co-creation-based method for this selection process. This research uses climate change communication as its applied domain and testbed. AR applications have gone on-site, communicating local climate risks in the places they affect, while VR has shown up in public spaces like museums and libraries - sometimes as part of local events - so the audience can access it without owning a headset themselves.

Intelligent Pedagogical Agents for Learning and Training in VR

ID: 1027
Presenter: Jimenez, Angela

Abstract

Extended reality (XR) offers a uniquely embodied medium for learning and training, yet most immersive systems follow a predetermined instructional path with limited flexibility to adapt to the individual learner. This research investigates how pedagogical agents can be designed to improve learning outcomes in virtual reality (VR) by combining two forms of interaction: conversational engagement with the learner through large language models (LLMs), and embodied interaction with the virtual environment through actions such as pointing, demonstrating, and manipulating objects. Building on prior work on agent interaction in VR, and a completed study of LLM-enhanced pedagogical agents in educational VR, this dissertation extends the inquiry to procedural training contexts (i.e., machinery operation) where a pedagogical agent must not only explain but also demonstrate. Three research questions guide this work: which agent design features drive learning outcomes, how LLM-mediated conversation affects learning gains, and how agent embodiment shapes the training experience. Together, these questions aim to establish design principles for embodied pedagogical agents across education and professional training in immersive environments.

Enhancing Visual Salience and Search Efficiency in 3D User Interfaces: Investigating the Role of Stereoscopic Depth through Autostereoscopic Displays

ID: 1028
Presenter: Tasliarmut, Melisa

Abstract

Stereoscopic three-dimensional displays offer a unique additional visual dimension that could serve as a salience attribute in information-dense three-dimensional user interfaces. However, it remains poorly understood how depth competes with established low-level salience attributes such as color and size, and whether depth-based salience translates into measurable search efficiency gains. This PhD project investigates the perceptual basis and practical utility of stereoscopic depth as a salience attribute in three-dimensional user interfaces, using an autostereoscopic display as the primary technology. The project spans three empirical work packages ranging from psychophysical threshold estimation to comparative device evaluations.

Gaze Guidance as Manipulation for Decision-Making in VR

ID: 1032
Presenter: Alabanza, G. Nikki

Abstract

Gaze guidance in virtual reality is used to direct user attention to specific locations within a virtual environment. This can aid with storytelling in a 360° environment, spatial navigation, or task guidance. However, directing attention also influences decision-making. Attention drives engagement and purchasing intent. By manipulating gaze, users can make purchases they would not have made otherwise. Existing work has highlighted forced attention in manipulative and deceptive design, but does not include methods for detection. This work aims to measure the influence of gaze guidance techniques on decision-making and to establish methods that detect when a user’s gaze is manipulated.

Asymmetric Augmented Reality Usage in Co-Located Collaborative Environments

ID: 1034
Presenter: Anderson, Michael

Abstract

Collaboration is a key application of augmented reality (AR) systems in both remote and co-located settings. Prior research on co-located collaboration has primarily focused on symmetrical scenarios in which all collaborators have access to AR devices and therefore receive equal access to the information and benefits provided by the technology. In practice, however, access to AR devices and content is often uneven due to device availability, cost constraints, or deliberate design decisions. This research plan suggests three studies in asymmetric scenarios of increasing levels of AR usage and complexity. We will investigate how these unequal constellations affect collaboration.

Evaluating Attentional Stability in Optical See-Through Augmented Reality Through Adaptation of Risk-Informed Visual Attention Metrics

ID: 1036
Presenter: Bright, Addison

Abstract

Visual attention plays a critical role in the effectiveness and safety of optical see-through augmented reality (OST-AR) systems, yet the field lacks standardized approaches for assessing attentional behavior. Existing AR evaluations typically emphasize performance, workload, and usability outcomes, providing limited insight into how users allocate attention between physical and virtual information sources. This dissertation explores whether visual attention metrics that have demonstrated relevance to safety and performance outcomes in driving can be adapted and validated for OST-AR environments. The first phase of this work consists of a systematic review and conceptual framework that identifies candidate metrics, including glance duration, transition-based, and entropy-based measures, and introduces attentional stability as a construct for understanding attention regulation in hybrid visual environments. Building upon this foundation, two planned empirical studies will evaluate the sensitivity of these metrics to changing task demands and their ability to predict performance outcomes under increasing levels of attentional competition. The anticipated outcome is a framework for evaluating visual attention in AR that is grounded in established attention research while addressing the unique demands of OST-AR systems. Through participation in the Doctoral Consortium, I seek feedback on the conceptualization of attentional stability, experimental design decisions, and approaches for validating attention metrics in AR.

Context-Aware Modality Adaptation for Robust Multimodal XR Interaction

ID: 1038
Presenter: Bashar, Mohammad Raihanul

Abstract

Extended Reality (XR) systems increasingly combine gaze, hand input, speech, controllers, and AI-mediated interaction. Yet many systems still use fixed modality mappings, leaving users to decide which modality to use, when to switch, and how to recover when sensing or interpretation fails. My doctoral research reframes multimodal XR interaction as an adaptive coordination problem: how can XR systems use task, spatial, perceptual, and confidence-related context to select, combine, and communicate modalities while preserving user agency? Through studies of multi-object selection, 3D selection, hand-tracking failure, creative interaction, and human–AI collaboration, I aim to develop empirically grounded design guidance for robust hands-free and minimally instrumented XR interaction.

Toward Object-Affordance-Aware Minimal AR for Adaptive Procedural Task Guidance

ID: 1039
Presenter: Yavari, Mona

Abstract

Augmented Reality (AR) can support industrial procedural tasks, but effective instruction authoring still requires decisions about which visual assets are needed. Minimal AR addresses this issue by providing only task-relevant information. Yet, before any visual asset is added, some information may already be perceptually available through the object’s visible properties and the operator’s prior experience. My doctoral research investigates this perceptual layer by focusing on object affordance in Minimal AR guidance. The thesis aims to develop and validate a rating framework that assesses when object-related information is already inferable and when additional AR support is justified for procedural tasks.

ID: 1041
Presenter: Palige, Selina

Abstract

This PhD project addresses two critical gaps in User Experience (UX) research for Social Virtual Reality (SVR) applications: the absence of comprehensive UX models that systematically incorporate social aspects, and the lack of validated measurement instruments for evaluating the overall user experience. The PhD project is structured into three phases. Phase 1 develops a theoretical UX model based on a literature review, integrating immersive and social dimensions specific to SVR. Phase 2 develops and validates a measurement instrument capturing the overall event experience, informed by expert input and empirical data. Phase 3 empirically tests and refines the model in a dyadic user study of an interactive SVR environment for club events, and examines how individual factors and interaction-design choices shape the user experience. The expected outcome is a validated framework for UX evaluation in SVR, providing researchers, developers, and practitioners with actionable insights and tools for designing socially rich immersive experiences.

Advancing Perceptual Training in Arts Education through Immersive Mixed Reality Systems

ID: 1046
Presenter: Rajput, Aayushi

Abstract

Perceptual training is central to observational drawing, but the assistive aids that support it are difficult to apply when drawing from real, three-dimensional subjects. This position paper describes ongoing doctoral research that addresses this gap using immersive mixed reality. The proposed system places the learner in a fully immersive virtual environment that displays a stereoscopic real-world subject, while a desk-level passthrough window preserves drawing on real paper with a real pencil. This selective boundary between the virtual subject and the real drawing surface is the system’s central mixed-reality contribution. Each exercise applies a predefined perceptual overlay, a design grounded in pilot evidence and in the pedagogy the system implements. This paper summarizes the research problem, the system, current progress, and questions for discussion at the doctoral consortium.

Exploring Techniques of Interaction and Remote Guidance in Virtual and Mixed Environments for Health Learning and Training Scenarios

ID: 1048
Presenter: Negrao, Matheus

Abstract

Virtual and Mixed Reality can offer efficient, immersive medical training, but most current collaborative systems focus on one-to-one mentoring, leaving multi-user, role-differentiated training underexplored. This thesis addresses this gap with an adaptive toolbox of guiding functionalities that adapts along key dimensions of remote collaboration: the number of trainees, their roles, the task and its complexity, the trainee’s agency over the task, and the symmetry and synchrony of the collaboration. The research follows five stages: exploring domain needs with medical experts, designing adaptive guidance tools, expanding to multi-user setups, implementing role-aware surgical team guidance, and integrating Mixed Reality haptics. This work shifts remote mentoring from ad hoc, dyadic demonstration toward persistent, spatially aware guidance across VR/MR training configurations.

ID: 1049
Presenter: Xia, Meng

Abstract

AI spatial models now mediate how people move through and make sense of physical space, from AR wayfinding overlays to adaptive exhibition systems that infer a visitor’s state and generate content in response. Such systems can operate in two modes. Annotation adds information while leaving a person’s own spatial reasoning intact. Substitution performs the spatial decision on the person’s behalf. This research argues that substitution displaces the cognitive process behind a spatial decision, not only the decision’s output, and that spatial agency lives in that process. The empirical core is a controlled AR wayfinding study with three conditions (unaided, annotation, and substitution with a deliberate conflict segment), using sketch-map distortion as a measurable proxy for cognitive-map formation. The framework is then applied to a curatorially constrained adaptive XR narrative system for cultural heritage exhibitions. The result is a transferable, evidence-grounded design principle, augment not substitute, for situated AI spatial models.

Between Reality and Virtuality: Cross Reality for Industrial Innovation

ID: 1050
Presenter: Marangelli, Luana

Abstract

Cross Reality (CR) enables seamless transitions and collaboration between systems with varying degrees of virtuality, including Augmented Reality (AR) and Virtual Reality (VR), offering new opportunities for industrial workspaces. While AR and VR have shown benefits for procedural guidance, training, collaboration, and simulation, their adoption remains limited by technical, spatial, and interaction-related constraints. CR may overcome these limitations by combining the contextual advantages of AR with the robustness and flexibility of VR. This research project investigates the potential of CR in industrial contexts by evaluating its advantages over standalone AR and VR. It aims to identify the sectors in which CR is most effective, with the ultimate goal of developing practical guidelines to support its implementation. The research adopts an approach based on a Systematic Literature Review and empirical studies. The review shows that, although interest in CR is increasing, mature industrial applications remain limited. However, existing studies suggest that CR can improve workflow efficiency, collaboration, workload, and usability. The first empirical study compared instructional metaphors during a mechanical disassembly task. Results indicate that CR instructions do not introduce significant disadvantages compared to AR approaches, positioning CR as a promising strategy for future industrial guidance.

Doctoral Consortium