Thesis Description & Objectives
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.
