Roberta Macaluso

The increasing adoption of immersive technologies such as Augmented Reality (AR) and Virtual Reality (VR) is enabling new forms of remote and distributed collaboration. In many emerging scenarios, users operate across different realities—for instance, one participant interacting through AR while another is immersed in VR. These cross-reality settings introduce inherent asymmetries, as users may have access to different visual cues, perspectives, and interaction capabilities. Such differences can significantly influence how individuals coordinate, communicate, and contribute to shared tasks.
This thesis aims to investigate the emergence of leadership in cross-reality collaborative environments. In particular, the work will explore whether leadership dynamics are primarily shaped by the technological medium (e.g., AR vs. VR) or by the level of mutual awareness between users, often referred to as co-presence awareness. Understanding these factors is essential for identifying how control, initiative, and decision-making are distributed in asymmetric collaborative settings.
The student will design and implement experimental scenarios in which an AR user collaborates with a VR user on shared tasks. The experimental design will systematically vary the level of awareness cues available to participants, such as visibility of actions, gaze direction, or attention indicators. User interactions will be analyzed to assess leadership behaviors, including initiative taking, guidance, and task control. Both qualitative and quantitative methods will be employed to evaluate how different configurations impact collaboration dynamics.
The expected outcome of this research is a deeper understanding of how immersive systems can unintentionally introduce imbalances in collaborative interactions. The findings will contribute to the design of cross-reality experiences that promote fair participation, balanced decision-making, and effective collaboration, regardless of the underlying technology. More broadly, the work may inform guidelines and design principles for developing equitable and user-centered XR collaborative systems.
References:
1. The Effects of Sharing Awareness Cues in Collaborative Mixed Reality
https://pmc.ncbi.nlm.nih.gov/articles/PMC7805624/
2. Empowerment and embodiment for collaborative MR systems
https://doi.org/10.1002/cav.1838
3. Who Owns What? Psychological Ownership in Shared AR
https://www.sciencedirect.com/science/article/abs/pii/S107158192100029X
4. Exploring User Behaviour in Asymmetric Collaborative Mixed Reality
https://dl.acm.org/doi/abs/10.1145/3562939.3565630
5. Persuasive Vibrations: Effects of Speech-Based Vibrations on Persuasion, Leadership, and Co-Presence During Verbal Communication in VR
https://ieeexplore.ieee.org/abstract/document/10108422
6. Towards Cross-Reality Interaction and Collaboration: A Comparative Study of Object Selection and Manipulation in Reality and Virtuality
https://ieeexplore.ieee.org/abstract/document/10108776

Organizations increasingly operate in volatile, uncertain, and information-dense environments where effective decision-making depends on the ability to rapidly synthesize large volumes of data, align diverse perspectives, and coordinate action across distributed teams. Traditional decision-support systems, however, remain largely static and non-immersive, offering limited mechanisms for real-time collaboration, strategic alignment, and cognitive workload management. Despite significant advances in Artificial Intelligence, Virtual Reality, and Augmented Reality, the integration of XR technologies with adaptive management tools capable of actively supporting collaborative decision-making processes remains largely unexplored.
This thesis aims to investigate how intelligent XR environments, integrating AI, VR, and AR, can enhance collaborative decision-making, strategic coordination, and sensemaking in complex organizational contexts. The research will explore how adaptive immersive systems can dynamically personalize information presentation, simulation scenarios, and communication strategies to improve team coordination, reduce cognitive overload, and facilitate consensus-building and negotiation among decision-makers.
The student will design and develop a collaborative XR platform that combines immersive analytics, virtual agents, and AI-driven adaptive interfaces. The system will leverage multimodal behavioral signals, including gaze patterns, spatial movement, interaction dynamics, and response timing, to continuously assess collaboration quality, cognitive load, and individual engagement. Based on these inferences, the platform will dynamically personalize visualizations, recommendations, and decision-support strategies in real time, enabling teams to navigate complex information spaces more effectively. Particular attention will be devoted to the design of adaptive mechanisms for group sensemaking, the modeling of coordination signals, and the integration of human–agent and human–human decision loops within immersive environments.
The developed solutions will be evaluated through user studies combining objective metrics, such as decision quality, coordination efficiency, task completion time, and interaction patterns, with subjective assessments of cognitive load, strategic alignment, perceived collaboration effectiveness, and user experience within the XR environment.
The expected outcome of this research is the identification of design principles and adaptive strategies for XR-based decision-support systems that actively enhance collaborative intelligence in organizational settings. The findings are expected to advance the fields of Human-AI collaboration, immersive analytics, and strategic teamwork, informing the development of next-generation decision environments in which intelligent agents and adaptive interfaces play a central role in supporting complex, high-stakes organizational decisions.
References:
1. B. Han et al., “Exploring Mediation by an Embodied Virtual Agent in Immersive Triadic Collaborative Decision-Making,” in IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2026.3679103.
2. D. Garkov et al., “Collaborative Problem Solving in Mixed Reality: A Study on Visual Graph Analysis,” in IEEE Transactions on Visualization and Computer Graphics, doi: 10.1109/TVCG.2026.3671472.

Extended Reality (XR) technologies are creating new opportunities for social interaction and collaboration by enabling shared immersive environments where users and virtual agents can communicate and work together. These environments have the potential to reduce social friction, awkwardness, and stress (especially among unfamiliar users) by mediating interaction through intelligent interfaces. However, current XR systems rarely incorporate adaptive or personalized strategies to actively support social dynamics in real time, limiting their effectiveness in fostering natural and engaging communication.
This thesis aims to explore how adaptive and intelligent virtual agents, together with social augmentation interfaces, can enhance communication and collaboration in XR environments. The focus will be on both human–human and human–agent interactions, investigating how real-time adaptation can reduce social discomfort while improving coordination, engagement, trust, and mutual understanding among participants.
The student will design and develop XR scenarios in which virtual agents and social interfaces act as companions, mediators, or teammates within collaborative tasks. The system will leverage multimodal signal (such as gaze behavior, posture, movement, and interaction dynamics) to continuously infer the state of the interaction and adapt communication strategies accordingly. This may include modifying agent behavior, expressivity, timing, and social roles to better support users and facilitate smoother interactions. Particular attention will be devoted to the design of adaptive mechanisms, the modeling of social signals, and the integration of human–agent and human–human interaction loops. The research will be conducted in collaboration with the Adelaide University, specifically with the Empathic Computing Lab, fostering an interdisciplinary and international research context.
The developed solutions will be evaluated through user studies combining objective metrics (such as coordination efficiency, response timing, and interaction patterns) with subjective assessments of social presence, communication quality, trust, and perceived collaboration effectiveness.
The expected outcome of this research is the identification of design principles and adaptive strategies for XR systems that actively support and enhance social interaction. The findings are expected to inform the development of next-generation immersive environments in which intelligent agents and interfaces play an active role in improving teamwork, communication, and overall user experience.
References:
A. Visconti, D. Calandra, F. Giorgione and F. Lamberti, “Enhancing Social Experiences in Immersive Virtual Reality with Artificial Facial Mimicry,” in IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 5, pp. 3325-3335, May 2025, doi: 10.1109/TVCG.2025.3549163.

Socially immersive Virtual Reality environments allow users to interact through avatars, yet current systems often animate only the head and hands, while the rest of the body relies on basic Inverse Kinematics (IK). This can result in unnatural gestures and physical interactions, reducing social presence, engagement, and the overall quality of multi-user experiences.
This thesis aims to enhance avatar-based social interaction animation by making movements more realistic, expressive, and responsive to interpersonal exchanges. The goal is to support natural social gestures such as handshakes, high-fives, hugs, or other forms of embodied communication, improving the sense of presence and fostering richer collaborative and social experiences in VR.
Machine Learning techniques may be used to refine avatar animations beyond standard IK solutions, but the primary focus is on the quality and naturalness of social interactions, rather than on the algorithms themselves. Other key aspects include adaptive management of avatar morphology, real-time collision handling, and the development of a multi-user VR system optimized for seamless, context-aware social interactions.
The expected outcome is a more immersive and socially engaging VR experience, where users can interact naturally through avatars that behave intuitively and expressively, enhancing both social presence and collaboration without compromising system performance.
References:
1. Visconti, Alessandro; Macaluso, Roberta; Di Bartolomei, Gabriele; Calandra, Davide; … (2026)
Improving fidelity of close social interaction animations in social VR with a machine learning-based refinement framework. In: 38th International Conference on Computer Animation and Social Agents (CASA 2025), https://dx.doi.org/10.1007/978-981-95-0100-7_13