Publications

@inproceedings{zhu_designing_2025,

title = {Designing VR Simulation System for Clinical Communication Training with LLMs-Based Embodied Conversational Agents},

author = {X. T. Zhu and H. Cheerman and M. Cheng and S. R. Kiami and L. Chukoskie and E. McGivney},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005754066&doi=10.1145%2f3706599.3719693&partnerID=40&md5=4468fbd54b43d6779259300afd08632e},

doi = {10.1145/3706599.3719693},

isbn = {979-840071395-8 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Conf Hum Fact Comput Syst Proc},

publisher = {Association for Computing Machinery},

abstract = {VR simulation in Health Professions (HP) education demonstrates huge potential, but fixed learning content with little customization limits its application beyond lab environments. To address these limitations in the context of VR for patient communication training, we conducted a user-centered study involving semi-structured interviews with advanced HP students to understand their challenges in clinical communication training and perceptions of VR-based solutions. From this, we derived design insights emphasizing the importance of realistic scenarios, simple interactions, and unpredictable dialogues. Building on these insights, we developed the Virtual AI Patient Simulator (VAPS), a novel VR system powered by Large Language Models (LLMs) and Embodied Conversational Agents (ECAs), supporting dynamic and customizable patient interactions for immersive learning. We also provided an example of how clinical professors could use user-friendly design forms to create personalized scenarios that align with course objectives in VAPS and discuss future implications of integrating AI-driven technologies into VR education. © 2025 Copyright held by the owner/author(s).},

keywords = {Clinical communications, Clinical Simulation, Communications training, Curricula, Embodied conversational agent, Embodied Conversational Agents, Health professions, Intelligent virtual agents, Language Model, Medical education, Model-based OPC, Patient simulators, Personnel training, Students, Teaching, User centered design, Virtual environments, Virtual Reality, VR simulation, VR simulation systems},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{harinee_elevating_2024,

title = {Elevating Medical Training: A Synergistic Fusion of AI and VR for Immersive Anatomy Learning and Practical Procedure Mastery},

author = {S. Harinee and R. Vimal Raja and E. Mugila and I. Govindharaj and V. Sanjaykumar and T. Ragavendhiran},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000334626&doi=10.1109%2fICSCAN62807.2024.10894451&partnerID=40&md5=100899b489c00335e0a652f2efd33e23},

doi = {10.1109/ICSCAN62807.2024.10894451},

isbn = {979-833151002-2 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Int. Conf. Syst., Comput., Autom. Netw., ICSCAN},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Virtual reality with its 3D visualization have brought an overwhelming change in the face of medical education, especially for courses like human anatomy. The proposed virtual reality system to bring massive improvements in the education received by a medical student studying for their degree courses. The project puts forward the text-to-speech and speech-to-text aligned system that simplifies the usage of a chatbot empowered by OpenAI GPT-4 and allows pupils to vocally speak with Avatar, the set virtual assistant. Contrary to the current methodologies, the setup of virtual reality is powered by avatars and thus covers an enhanced virtual assistant environment. Avatars offer students the set of repeated practicing of medical procedures on it, and the real uniqueness in the proposed product. The developed virtual reality environment is enhanced over other current training techniques where a student should interact and immerse in three-dimensional human organs for visualization in three dimensions and hence get better knowledge of the subjects in greater depth. A virtual assistant guides the whole process, giving insights and support to help the student bridge the gap from theory to practice. Then, the system is essentially Knowledge based and Analysis based approach. The combination of generative AI along with embodied virtual agents has great potential when it comes to customized virtual conversation assistant for much wider range of applications. The study brings out the value of acquiring hands-on skills through simulated medical procedures and opens new frontiers of research and development in AI, VR, and medical education. In addition to assessing the effectiveness of such novel functionalities, the study also explores user experience related dimensions such as usability, task loading, and the sense of presence in proposed virtual medical environment. © 2024 IEEE.},

keywords = {'current, Anatomy education, Anatomy educations, Computer interaction, Curricula, Embodied virtual assistant, Embodied virtual assistants, Generative AI, Human- Computer Interaction, Immersive, Intelligent virtual agents, Medical computing, Medical education, Medical procedure practice, Medical procedures, Medical training, Personnel training, Students, Teaching, Three dimensional computer graphics, Usability engineering, Virtual assistants, Virtual environments, Virtual Reality, Visualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bandara_generative-aicustom-trained_2024,

title = {Generative-AI(with Custom-Trained Meta's Llama2 LLM), Blockchain, NFT, Federated Learning and PBOM Enabled Data Security Architecture for Metaverse on 5G/6G Environment},

author = {E. Bandara and P. Foytik and S. Shetty and A. Hassanzadeh},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85210243120&doi=10.1109%2fMASS62177.2024.00026&partnerID=40&md5=70d21ac1e9c7b886da14825376919cac},

doi = {10.1109/MASS62177.2024.00026},

isbn = {979-835036399-9 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Int. Conf. Mob. Ad-Hoc Smart Syst., MASS},

pages = {118–124},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The Metaverse is an integrated network of 3D virtual worlds accessible through a virtual reality headset. Its impact on data privacy and security is increasingly recognized as a major concern. There is a growing interest in developing a reference architecture that describes the four core aspects of its data: acquisition, storage, sharing, and interoperability. Establishing a secure data architecture is imperative to manage users' personal data and facilitate trusted AR/VR and AI/ML solutions within the Metaverse. This paper details a reference architecture empowered by Generative-AI, Blockchain, Federated Learning, and Non-Fungible Tokens (NFTs). Within this archi-tecture, various resource providers collaborate via the blockchain network. Handling personal user data and resource provider identities is executed through a Self-Sovereign Identity-enabled privacy-preserving framework. AR/NR devices in the Metaverse are represented as NFT tokens available for user purchase. Software updates and supply-chain verification for these devices are managed using a Software Bill of Materials (SBOM) and a Pipeline Bill of Materials (PBOM) verification system. Moreover, a custom-trained Llama2 LLM from Meta has been integrated to generate PBOMs for AR/NR devices' software updates, thereby preventing malware intrusions and data breaches. This Llama2-13B LLM has been quantized and fine-tuned using Qlora to ensure optimal performance on consumer-grade hardware. The provenance of AI/ML models used in the Metaverse is encapsu-lated as Model Card objects, allowing external parties to audit and verify them, thus mitigating adversarial learning attacks within these models. To the best of our knowledge, this is the very first research effort aimed at standardizing PBOM schemas and integrating Language Model algorithms for the generation of PBOMs. Additionally, a proposed mechanism facilitates different AI/ML providers in training their machine learning models using a privacy-preserving federated learning approach. Authorization of communications among AR/VR devices in the Metaverse is conducted through a Zero-Trust security-enabled rule engine. A system testbed has been implemented within a 5G environment, utilizing Ericsson new Radio with Open5GS 5G core. © 2024 IEEE.},

keywords = {5G, 6G, Adversarial machine learning, Bill of materials, Block-chain, Blockchain, Curricula, Data privacy, Distance education, Federated learning, Generative adversarial networks, Generative-AI, Hardware security, Llama2, LLM, Medium access control, Metaverse, Metaverses, Network Security, Nft, Non-fungible token, Personnel training, Problem oriented languages, Reference architecture, Steganography},

pubstate = {published},

tppubtype = {inproceedings}

}