Collaborative Multimodal XR-based Training Environments for Collocated Medical Teams

Collaborative Extended Reality (XR) systems hold growing promise as training platforms in domains that demand high levels of coordination, communication, and shared situational awareness. However, the current landscape of XR-based training tools remains predominantly focused on individual skill development, with limited support for realistic, synchronous collaboration among physically collocated teams. Furthermore, existing evaluation methods often rely on individual performance metrics and fail to capture the nuanced dynamics of teamwork. This dissertation addresses these critical gaps by proposing both design and evaluation frameworks tailored for collaborative XR training environments. The research is structured around two core research questions. First, it investigates how XR training systems can be designed to support realistic, high-fidelity collaboration among collocated professional teams. Through naturalistic observations, stakeholder interviews, and iterative prototyping, the study identifies key design factors and formulates a set of principles that inform the development of a task-driven XR training simulator. Second, it introduces a theoretically grounded, multimodal, user-centered evaluation evaluation based on the Distributed Cognition Theory. This framework integrates behavioral, communicative, and perceptual data to assess team-level performance in XR, extending beyond traditional task metrics to include communication flow, role coordination, and temporal organization. Together, the design and evaluation components contribute to a robust methodological pipeline for advancing Collaborative XR systems. The work not only bridges theoretical and practical gaps in XR training but also lays the groundwork for scalable, evidence-based tools that better reflect the realities of team-based performance in complex environments. Through these contributions, the dissertation advances the state of the art in collaborative immersive training and supports the development of next-generation XR platforms for real-world readiness.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and#38; Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2026

Author dc:creator

• Donekal Chandrashekar, Nikitha

Chair dc:contributor.committeechair

• Gracanin, Denis

Committee members dc:contributor.committeemember

• Lee, Sang Won
• David-John, Brendan Matthew
• Muniyandi, Manivannan
• Safford, Shawn D.

• Extended Reality
• Collaboration
• Medical Team Training
• Multimodality

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:45507

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/140613