Posts

paper in Architecture Journal

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Metaverse and Digital Twins in the Age of AI and Extended Reality

Tang, Ming, Mikhail Nikolaenko, Ahmad Alrefai, and Aayush Kumar. 2025. “Metaverse and Digital Twins in the Age of AI and Extended Reality” Architecture 5, no. 2: 36. https://doi.org/10.3390/architecture5020036

This paper explores the evolving relationship between Digital Twins (DT) and the Metaverse, two foundational yet often conflated digital paradigms in digital architecture. While DTs function as mirrored models of real-world systems—integrating IoT, BIM, and real-time analytics to support decision-making—Metaverses are typically fictional, immersive, multi-user environments shaped by social, cultural, and speculative narratives. Through several research projects, the team investigate the divergence between DTs and Metaverses through the lens of their purpose, data structure, immersion, and interactivity, while highlighting areas of convergence driven by emerging technologies in Artificial Intelligence (AI) and Extended Reality (XR).This study aims to investigate the convergence of DTs and the Metaverse in digital architecture, examining how emerging technologies—such as AI, XR, and Large Language Models (LLMs)—are blurring their traditional boundaries. By analyzing their divergent purposes, data structures, and interactivity modes, as well as hybrid applications (e.g., data-integrated virtual environments and AI-driven collaboration), this study seeks to define the opportunities and challenges of this integration for architectural design, decision-making, and immersive user experiences. Our research spans multiple projects utilizing XR and AI to develop DT and the Metaverse. The team assess the capabilities of AI in DT environments, such as reality capture and smart building management. Concurrently, the team evaluates metaverse platforms for online collaboration and architectural education, focusing on features facilitating multi-user engagement. The paper presents evaluations of various virtual environment development pipelines, comparing traditional BIM+IoT workflows with novel approaches such as Gaussian Splatting and generative AI for content creation. The team further explores the integration of Large Language Models (LLMs) in both domains, such as virtual agents or LLM-powered Non-Player-Controlled Characters (NPC), enabling autonomous interaction and enhancing user engagement within spatial environments. Finally, the paper argues that DTs and Metaverse’s once-distinct boundaries are becoming increasingly porous. Hybrid digital spaces—such as virtual buildings with data-integrated twins and immersive, social metaverses—demonstrate this convergence. As digital environments mature, architects are uniquely positioned to shape these dual-purpose ecosystems, leveraging AI, XR, and spatial computing to fuse data-driven models with immersive and user-centered experiences.
 
Keywords:  metaverse; digital twin; extended reality; AI

SMART-DT

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SMART-DT: Scalable Multi-Agent Reinforcement Learning and Collaborative AI for Digital Twin Platform of Infrastructure and Facility Operations.

Principal Investigators:

  • Prof. Sam Anand, Department of Mechanical Engineering, CEAS
  • Prof. Ming Tang, Extended Reality Lab, Digital Futures, DAAP

Grant: $40,000. UC Industry 4.0/5.0 Institute Consortium Research Project: 03.2025-01.2026

In this phase, we will develop a scalable digital twin that integrates machine, factory, and city-level data with AI-driven real-time decision-making. The key questions we aim to answer are:

  • Can a high-fidelity Digital Twin (DT) be efficiently built using only image and video data?
  • How can multiple specialized Large Language Model (LLM) agents—at machine, factory, and city levels—collaborate to generate relevant insights?
  • How effective is synthetic data from a Digital Twin for object detection and process recognition?
  • Does combining traditional Machine Learning (ML) with Large Language Models (LLMs) improve decision-making in complex manufacturing operations?

The project’s primary goal is to create a scalable, cloud-based digital twin that enhances operational efficiency through AI-driven insights. Additional technical objectives include:

  • Using advanced reality capture techniques (e.g., Gaussian Splatting) to build a Digital Twin from images and videos and simulate fault scenarios at factory and data center levels.
  • Integrating an IIoT data framework to track material flow, process handling, operational metrics, and equipment status for seamless cloud-based analysis.
  • Developing a synthetic data capture system using a simulated drone within the Digital Twin to train reinforcement learning models for fault prediction.
  • Designing a multi-agent AI system combining LLMs, machine learning, and reinforcement learning to enable dynamic communication, prediction, and diagnostics in the factory.

 

last year’s project: IIOT for legacy and intelligent factory machines with XR and LLM feedback with a Digital Twin demonstration of real-time IOT for architecture/building applications using Omniverse.

P&G VISM

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Project VISM (Virtual Interactive Simulation for Material customization)
Interactive Visualization with User-Controlled, Procedural-Based, and Physical-Based Material Customization.

PI. Ming Tang.

Sponsor: P&G. 12/01/2024 – 5/31/2025

 

 

 

paper on XR conference

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Two papers were presented and published at the 2024 International Conference on eXtended Reality. XR Salento 2024.

Tang, Ming, Mikhail Nikolaenko, Evv Boerwinkle, Samuel Obafisoye, Aayush Kumar, Mohsen Rezayat, Sven Lehmann, and Tamara Lorenz. “Evaluation of the Effectiveness of Traditional Training vs. Immersive Training: A Case Study of Building Safety & Emergency Training.” Paper presented at the International Conference on eXtended Reality (XR SALENTO 2024), Lecce, Italy, September 4-9, 2024. The paper is published in the Springer Link proceeding book. 

Virtual Reality (VR) has revolutionized training across healthcare, manufacturing, and service sectors by offering realistic simulations that enhance engagement and knowledge retention. However, assessments that allow for evaluation of the effectiveness of VR training are still sparse. Therefore, we examine VR’s effectiveness in emergency preparedness and building safety, comparing it to traditional training methods. The goal is to evaluate the impact of the unique opportunities VR enables on skill and knowledge development, using digital replicas of building layouts for immersive training experiences. To that end, the research evaluates VR training’s advantages and develops performance metrics by comparing virtual performance with actions in physical reality, using wearable tech for performance data collection and surveys for insights. Participants, split into VR and online groups, underwent a virtual fire drill to test emergency response skills. Findings indicate that VR training boosts urgency and realism perception despite similar knowledge and skill acquisition after more traditional lecture-style training. VR participants reported higher stress and greater effectiveness, highlighting VR’s immersive benefits. The study supports previous notions of VR’s potential in training while also emphasizing the need for careful consideration of its cognitive load and technological demands.

 

Tang, M., Nored, J., Anthony, M., Eschmann, J., Williams, J., Dunseath, L. (2024). VR-Based Empathy Experience for Nonprofessional Caregiver Training. In: De Paolis, L.T., Arpaia, P., Sacco, M. (eds) Extended Reality. XR Salento 2024. Lecture Notes in Computer Science, vol 15028. Springer, Cham. https://doi.org/10.1007/978-3-031-71704-8_28 

This paper presents the development of a virtual reality (VR) system designed to simulate various caregiver training scenarios, with the aim of fostering empathy by providing visual and emotional representations of the caregiver’s experience. The COVID-19 pandemic has increased the need for family members to assume caregiving roles, particularly for older adults who are at high risk for severe complications and death. This has led to a significant reduction in the availability of qualified home health workers. More than six million people aged 65 and older require long-term care, and two-thirds of these individuals receive all their care exclusively from family caregivers. Many caregivers are unprepared for the physical and emotional demands of caregiving, often exhibiting clinical signs of depression and higher stress levels.

The VR system, EVRTalk, developed by a multi-institutional team, addresses this gap by providing immersive training experiences. It incorporates theories of empathy and enables caregivers to switch roles with care recipients, navigating common scenarios such as medication management, hallucinations, incontinence, end-of-life conversations, and caregiver burnout. Research demonstrates that VR can enhance empathy, understanding, and communication skills among caregivers. The development process included creating believable virtual characters and interactive scenarios to foster empathy and improve caregiving practices. Initial evaluations using surveys showed positive feedback, indicating that VR training can reduce stress and anxiety for caregivers and improve care quality.

Future steps involve using biofeedback to measure physiological responses and further investigating the ethical implications of VR in caregiving training. The ultimate goal is to deploy VR training in homes, providing family caregivers with the tools and knowledge to manage caregiving responsibilities more effectively, thereby enhancing the quality of life for both caregivers and care recipients.

 

Poster in AHFE conference

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Nancy Daraiseh, Aaron Vaughn, Ming Tang, Mikhail Nikolaenko, Madeline Aeschbury, Alycia Bachtel, Camryn Backman, Chunyan Liu, Maurizio Macaluso . Using Virtual Reality to Enhance Behavioral Staff Training for Interacting with Aggressive Psychiatric Patients. Poster. The 15th International Conference on Applied Human Factors and Ergonomics (AHFE 2024). Nice, France, July 24-27, 2024.

Objective: To conduct a pilot study to enhance staff training and confidence when interacting with aggressive psychiatric patients using a virtual reality (VR) training module depicting an escalating patient scenario.

Significance: Dysregulated emotional outbursts, reactive aggression, and self-injurious behaviors are common in psychiatrically hospitalized patients. These behaviors result in aggressive patient interactions (APIs) which are associated with increased risk of harm to the patient and staff. Minimal research has examined interventions for successful training to effectively reduce or prevent API events and subsequent harm. Despite intensive, standardized trainings in crisis de-escalation protocols, staff continue to experience high rates of API injuries. More realistic training and competency in a safe environment to practice implementation and utilization of de-escalation strategies to avoid APIs and patient harm are needed.

Methods Using a pre – post, quasi-experimental design, 40 Behavioral Health Specialists and Registered Nurses at a pediatric psychiatric facility will participate in VR training depicting a commonly experienced scenario when interacting with an aggressive patient. Participants are stratified by job experience, sex, and VR experience. Study aims are to: i) assess the feasibility and usability of VR training among this population and ii) obtain measures of learner satisfaction and performance. Surveys measure usability, learner satisfaction, and coping with patient aggression. Pre- and post-performance in training will be compared and assessed by percent correct answers on the first attempt; time to correct answer; and the number of successful and unsuccessful attempts.

Preliminary Results (full analyses in progress): Preliminary survey results (N=14) show that 64% perceived the VR experience to be consistent with their real-world experiences: 87% agree that the VR training would help with interactions with aggressive patients: 71% reported the training was effective in identifying de-escalation strategies: 79% stated the training was effective in recognizing stages of patient crisis; training included important skills used in their job; and would recommend the training. Finally, 100% would participate in future VR trainings.

Anticipated Conclusions: We plan to show that using VR to supplement in-place training programs for high-risk situations can improve users’ understanding of essential de-escalation and crisis techniques. We anticipate results will show an enhanced ability and confidence when interacting with aggressive patients. Future studies will expand on results and examine implications on staff and patient harm. 

Check more information on the  VR-based Employee Safety Training. Therapeutic Crisis Intervention Simulation