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DT & AI for AEC showcase

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Digital Twins,  AI Design Showcase-Higher Education explored emerging digital technologies and their impact on the AEC industry. The event was organized by the Cincinnati BIM User Group and held on April 16, at College of Design Arcgutectyre, Art, and Planning (DAAP), University of Cincinnati.

Hosted by Prof. Ming Tang at the DAAP, the showcase featured student work from the University of Cincinnati, Northern Kentucky University, and Cincinnati State. Attendees experienced digital twin, AI, and BIM technologies firsthand, highlighting how these student teams are helping shape the future of the AEC workforce.

The UC team presented several projects, including digital twins integrated with IoT, AI-based spatial computing with BIM (focusing on performance, sustainability, and wayfinding on the UC campus), and the SENSE + AI studio.

 

Photo by Nicholas Namyar. 04.16.2026

Special thanks to IMAGINiT Technologies for sponsoring this event.

Therapeutic Crisis Intervention Simulation, Phase 3

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<href=”http://ming3d.com/new/wp-content/uploads/2025/07/CCHMCP3-1.jpg”>We are excited to announce the launch of Phase 3 of the VR-Based Employee Safety Training: Therapeutic Crisis Intervention Simulation project, building on the success of the previous two phases. This interdisciplinary collaboration brings together the Immersive Learning Lab and the Employee Safety Learning Lab at Cincinnati Children’s Hospital Medical Center (CCHMC), in partnership with the Extended Reality Lab (XR-Lab) at the University of Cincinnati. 

This phase will focus on developing an advanced virtual hospital environment populated with digital patients to simulate a variety of real-world Therapeutic Crisis Intervention (TCI) scenarios. The digital twins encompass both the hospital setting and patient avatars. The project aims to design immersive training modules, capture user performance data, and conduct a rigorous evaluation of the effectiveness of VR-based training in enhancing employee safety and crisis response capabilities

Principal Investigator: Ming Tang. Funding Amount: $38,422. Project Period: April 1, 2025 – December 1, 2026

CCHMC Collaborators: Dr. Nancy Daraiseh, Dr. Maurizio Macaluso, Dr. Aaron Vaughn.

Research Domains: Virtual Reality, Safety Training, Therapeutic Crisis Intervention, Mental Health, Digital Twins, Digital Humans, Human Behavior Simulation.

We look forward to continuing this impactful work and advancing the role of immersive technologies in healthcare education and safety training

Concept of Digital Twin: Digital Patient + Digital Hospital.

paper on AI, XR, Metaverse, Digital Twins

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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

The paper is features in the Architecture journal cover page.

SMAT: Scalable Multi-Agent AI for DT

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SMAT: Scalable Multi-Agent Machine 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

Students: Anuj Gautam, Manish Aryal, Aayush Kumar, Ahmad Alrefai, Rohit Ramesh, Mikhail Nikolaenko, Bozhi Peng

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

Read more

paper in JMS & NAMRC

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Anuj Gautam, Manish Raj Aryal, Sourabh Deshpande, Shailesh Padalkar, Mikhail Nikolaenko, Ming Tang, Sam Anand, IIoT-enabled digital twin for legacy and smart factory machines with LLM integration, Journal of Manufacturing Systems, Volume 80, 2025, Pages 511-523, ISSN 0278-6125

The paper is also published in the NAMRC 2025 conference.

Anuj Gautam , Manish Raj Aryal, Sourabh Deshpande, Shailesh Padalkar, Mikhail Nikolaenko, Ming Tang, Sam Anand. IIoT-enabled Digital Twin for legacy and smart factory machines with LLM integration. 53rd SME North American Manufacturing Research Conference (NAMRC), Clemson Univ. 06/2025.

 

Abstract

The recent advancement in Large Language Models (LLMs) has significantly transformed the field of natural data interpretation, translation, and user training. However, a notable gap exists when LLMs are tasked to assist with real-time context-sensitive machine data. The paper presents a multi-agent LLM framework capable of accessing and interpreting real-time and historical data through an Industrial Internet of Things (IIoT) platform for evidence-based inferences. The real-time data is acquired from several legacy machine artifacts (such as seven-segment displays, toggle switches, and knobs), smart machines (such as 3D printers), and building data (such as sound sensors and temperature measurement devices) through MTConnect data streaming protocol. Further, a multi-agent LLM framework that consists of four specialized agents – a supervisor agent, a machine-expertise agent, a data visualization agent, and a fault-diagnostic agent is developed for context-specific manufacturing tasks. This LLM framework is then integrated into a digital twin to visualize the unstructured data in real time. The paper also explores how LLM-based digital twins can serve as real time virtual experts through an avatar, minimizing reliance on traditional manuals or supervisor-based expertise. To demonstrate the functionality and effectiveness of this framework, we present a case study consisting of legacy machine artifacts and modern machines. The results highlight the practical application of LLM to assist and infer real-time machine data in a digital twin environment.