AHCI RESEARCH GROUP

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OUR RESEARCH

Scientific Publications

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2025

Kim, Y.; Aamir, Z.; Singh, M.; Boorboor, S.; Mueller, K.; Kaufman, A. E.

Explainable XR: Understanding User Behaviors of XR Environments Using LLM-Assisted Analytics Framework Journal Article

In: IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 5, pp. 2756–2766, 2025, ISSN: 10772626 (ISSN).

Abstract | Links | BibTeX | Tags: adult, Agnostic, Article, Assistive, Cross Reality, Data Analytics, Data collection, data interpretation, Data recording, Data visualization, Extended reality, human, Language Model, Large language model, large language models, Multi-modal, Multimodal Data Collection, normal human, Personalized assistive technique, Personalized Assistive Techniques, recorder, Spatio-temporal data, therapy, user behavior, User behaviors, Virtual addresses, Virtual environments, Virtual Reality, Visual analytics, Visual languages

@article{kim_explainable_2025,

title = {Explainable XR: Understanding User Behaviors of XR Environments Using LLM-Assisted Analytics Framework},

author = {Y. Kim and Z. Aamir and M. Singh and S. Boorboor and K. Mueller and A. E. Kaufman},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003815583&doi=10.1109%2fTVCG.2025.3549537&partnerID=40&md5=1085b698db06656985f80418cb37b773},

doi = {10.1109/TVCG.2025.3549537},

issn = {10772626 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Transactions on Visualization and Computer Graphics},

volume = {31},

number = {5},

pages = {2756–2766},

abstract = {We present Explainable XR, an end-to-end framework for analyzing user behavior in diverse eXtended Reality (XR) environments by leveraging Large Language Models (LLMs) for data interpretation assistance. Existing XR user analytics frameworks face challenges in handling cross-virtuality - AR, VR, MR - transitions, multi-user collaborative application scenarios, and the complexity of multimodal data. Explainable XR addresses these challenges by providing a virtuality-agnostic solution for the collection, analysis, and visualization of immersive sessions. We propose three main components in our framework: (1) A novel user data recording schema, called User Action Descriptor (UAD), that can capture the users' multimodal actions, along with their intents and the contexts; (2) a platform-agnostic XR session recorder, and (3) a visual analytics interface that offers LLM-assisted insights tailored to the analysts' perspectives, facilitating the exploration and analysis of the recorded XR session data. We demonstrate the versatility of Explainable XR by demonstrating five use-case scenarios, in both individual and collaborative XR applications across virtualities. Our technical evaluation and user studies show that Explainable XR provides a highly usable analytics solution for understanding user actions and delivering multifaceted, actionable insights into user behaviors in immersive environments. © 1995-2012 IEEE.},

keywords = {adult, Agnostic, Article, Assistive, Cross Reality, Data Analytics, Data collection, data interpretation, Data recording, Data visualization, Extended reality, human, Language Model, Large language model, large language models, Multi-modal, Multimodal Data Collection, normal human, Personalized assistive technique, Personalized Assistive Techniques, recorder, Spatio-temporal data, therapy, user behavior, User behaviors, Virtual addresses, Virtual environments, Virtual Reality, Visual analytics, Visual languages},

pubstate = {published},

tppubtype = {article}

}