Publications

@article{casas_holojig_2025,

title = {HoloJig: Interactive Spoken Prompt Specified Generative AI Environments},

author = {L. Casas and S. Hannah and K. Mitchell},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001182100&doi=10.1109%2fMCG.2025.3553780&partnerID=40&md5=ec5dc44023314b6f9221169357d81dcd},

doi = {10.1109/MCG.2025.3553780},

issn = {02721716 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Computer Graphics and Applications},

volume = {45},

number = {2},

pages = {69–77},

abstract = {HoloJig offers an interactive, speech-to-virtual reality (VR), VR experience that generates diverse environments in real time based on live spoken descriptions. Unlike traditional VR systems that rely on prebuilt assets, HoloJig dynamically creates personalized and immersive virtual spaces with depth-based parallax 3-D rendering, allowing users to define the characteristics of their immersive environment through verbal prompts. This generative approach opens up new possibilities for interactive experiences, including simulations, training, collaborative workspaces, and entertainment. In addition to speech-to-VR environment generation, a key innovation of HoloJig is its progressive visual transition mechanism, which smoothly dissolves between previously generated and newly requested environments, mitigating the delay caused by neural computations. This feature ensures a seamless and continuous user experience, even as new scenes are being rendered on remote servers. © 1981-2012 IEEE.},

keywords = {3-D rendering, Article, Collaborative workspace, customer experience, Economic and social effects, generative artificial intelligence, human, Immersive, Immersive environment, parallax, Real- time, simulation, Simulation training, speech, Time based, Virtual environments, Virtual Reality, Virtual reality experiences, Virtual spaces, VR systems},

pubstate = {published},

tppubtype = {article}

}

@article{li_xs_2025,

title = {X’s Day: Personality-Driven Virtual Human Behavior Generation},

author = {H. Li and Z. Wang and W. Liang and Y. Wang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003864932&doi=10.1109%2fTVCG.2025.3549574&partnerID=40&md5=a865bbd2b0fa964a4f0f4190955dc787},

doi = {10.1109/TVCG.2025.3549574},

issn = {10772626 (ISSN)},

year  = {2025},

date = {2025-01-01},

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

volume = {31},

number = {5},

pages = {3514–3524},

abstract = {Developing convincing and realistic virtual human behavior is essential for enhancing user experiences in virtual reality (VR) and augmented reality (AR) settings. This paper introduces a novel task focused on generating long-term behaviors for virtual agents, guided by specific personality traits and contextual elements within 3D environments. We present a comprehensive framework capable of autonomously producing daily activities autoregressively. By modeling the intricate connections between personality characteristics and observable activities, we establish a hierarchical structure of Needs, Task, and Activity levels. Integrating a Behavior Planner and a World State module allows for the dynamic sampling of behaviors using large language models (LLMs), ensuring that generated activities remain relevant and responsive to environmental changes. Extensive experiments validate the effectiveness and adaptability of our approach across diverse scenarios. This research makes a significant contribution to the field by establishing a new paradigm for personalized and context-aware interactions with virtual humans, ultimately enhancing user engagement in immersive applications. Our project website is at: https://behavior.agent-x.cn/. © 2025 IEEE. All rights reserved,},

keywords = {adult, Augmented Reality, Behavior Generation, Chatbots, Computer graphics, computer interface, Contextual Scene, female, human, Human behaviors, Humans, Long-term behavior, male, Novel task, Personality, Personality traits, Personality-driven Behavior, physiology, Social behavior, User-Computer Interface, Users' experiences, Virtual agent, Virtual environments, Virtual humans, Virtual Reality, Young Adult},

pubstate = {published},

tppubtype = {article}

}

@article{song_enhancing_2025,

title = {Enhancing Patient Acceptance of Robotic Ultrasound through Conversational Virtual Agent and Immersive Visualizations},

author = {T. Song and F. Pabst and U. Eck and N. Navab},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003687673&doi=10.1109%2fTVCG.2025.3549181&partnerID=40&md5=1d46569933582ecf5e967f0794aafc07},

doi = {10.1109/TVCG.2025.3549181},

issn = {10772626 (ISSN)},

year  = {2025},

date = {2025-01-01},

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

volume = {31},

number = {5},

pages = {2901–2911},

abstract = {Robotic ultrasound systems have the potential to improve medical diagnostics, but patient acceptance remains a key challenge. To address this, we propose a novel system that combines an AI-based virtual agent, powered by a large language model (LLM), with three mixed reality visualizations aimed at enhancing patient comfort and trust. The LLM enables the virtual assistant to engage in natural, conversational dialogue with patients, answering questions in any format and offering real-time reassurance, creating a more intelligent and reliable interaction. The virtual assistant is animated as controlling the ultrasound probe, giving the impression that the robot is guided by the assistant. The first visualization employs augmented reality (AR), allowing patients to see the real world and the robot with the virtual avatar superimposed. The second visualization is an augmented virtuality (AV) environment, where the real-world body part being scanned is visible, while a 3D Gaussian Splatting reconstruction of the room, excluding the robot, forms the virtual environment. The third is a fully immersive virtual reality (VR) experience, featuring the same 3D reconstruction but entirely virtual, where the patient sees a virtual representation of their body being scanned in a robot-free environment. In this case, the virtual ultrasound probe, mirrors the movement of the probe controlled by the robot, creating a synchronized experience as it touches and moves over the patient's virtual body. We conducted a comprehensive agent-guided robotic ultrasound study with all participants, comparing these visualizations against a standard robotic ultrasound procedure. Results showed significant improvements in patient trust, acceptance, and comfort. Based on these findings, we offer insights into designing future mixed reality visualizations and virtual agents to further enhance patient comfort and acceptance in autonomous medical procedures. © 1995-2012 IEEE.},

keywords = {3D reconstruction, adult, Augmented Reality, Computer graphics, computer interface, echography, female, human, Humans, Imaging, Intelligent robots, Intelligent virtual agents, Language Model, male, Medical robotics, Middle Aged, Mixed reality, Patient Acceptance of Health Care, patient attitude, Patient comfort, procedures, Real-world, Reality visualization, Robotic Ultrasound, Robotics, Three-Dimensional, three-dimensional imaging, Trust and Acceptance, Ultrasonic applications, Ultrasonic equipment, Ultrasonography, Ultrasound probes, User-Computer Interface, Virtual agent, Virtual assistants, Virtual environments, Virtual Reality, Visual languages, Visualization, Young Adult},

pubstate = {published},

tppubtype = {article}

}

@article{chen_llmer_2025,

title = {LLMER: Crafting Interactive Extended Reality Worlds with JSON Data Generated by Large Language Models},

author = {J. Chen and X. Wu and T. Lan and B. Li},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003825793&doi=10.1109%2fTVCG.2025.3549549&partnerID=40&md5=da4681d0714548e3a7e0c8c3295d2348},

doi = {10.1109/TVCG.2025.3549549},

issn = {10772626 (ISSN)},

year  = {2025},

date = {2025-01-01},

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

volume = {31},

number = {5},

pages = {2715–2724},

abstract = {The integration of Large Language Models (LLMs) like GPT-4 with Extended Reality (XR) technologies offers the potential to build truly immersive XR environments that interact with human users through natural language, e.g., generating and animating 3D scenes from audio inputs. However, the complexity of XR environments makes it difficult to accurately extract relevant contextual data and scene/object parameters from an overwhelming volume of XR artifacts. It leads to not only increased costs with pay-per-use models, but also elevated levels of generation errors. Moreover, existing approaches focusing on coding script generation are often prone to generation errors, resulting in flawed or invalid scripts, application crashes, and ultimately a degraded user experience. To overcome these challenges, we introduce LLMER, a novel framework that creates interactive XR worlds using JSON data generated by LLMs. Unlike prior approaches focusing on coding script generation, LLMER translates natural language inputs into JSON data, significantly reducing the likelihood of application crashes and processing latency. It employs a multi-stage strategy to supply only the essential contextual information adapted to the user's request and features multiple modules designed for various XR tasks. Our preliminary user study reveals the effectiveness of the proposed system, with over 80% reduction in consumed tokens and around 60% reduction in task completion time compared to state-of-the-art approaches. The analysis of users' feedback also illuminates a series of directions for further optimization. © 1995-2012 IEEE.},

keywords = {% reductions, 3D modeling, algorithm, Algorithms, Augmented Reality, Coding errors, Computer graphics, Computer interaction, computer interface, Computer simulation languages, Extended reality, generative artificial intelligence, human, Human users, human-computer interaction, Humans, Imaging, Immersive, Language, Language Model, Large language model, large language models, Metadata, Natural Language Processing, Natural language processing systems, Natural languages, procedures, Script generation, Spatio-temporal data, Three dimensional computer graphics, Three-Dimensional, three-dimensional imaging, User-Computer Interface, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{alibrahim_ai_2025,

title = {AI speechbots and 3D segmentations in virtual reality improve radiology on-call training in resource-limited settings},

author = {Y. Alibrahim and M. Ibrahim and D. Gurdayal and M. Munshi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001472313&doi=10.1016%2fj.ibmed.2025.100245&partnerID=40&md5=623a0ceaa07e5516a296420d25c3033b},

doi = {10.1016/j.ibmed.2025.100245},

issn = {26665212 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Intelligence-Based Medicine},

volume = {11},

abstract = {Objective: Evaluate the use of large-language model (LLM) speechbot tools and deep learning-assisted generation of 3D reconstructions when integrated in a virtual reality (VR) setting to teach radiology on-call topics to radiology residents. Methods: Three first year radiology residents in Guyana were enrolled in an 8-week radiology course that focused on preparation for on-call duties. The course, delivered via VR headsets with custom software integrating LLM-powered speechbots trained on imaging reports and 3D reconstructions segmented with the help of a deep learning model. Each session focused on a specific radiology area, employing a didactic and case-based learning approach, enhanced with 3D reconstructions and an LLM-powered speechbot. Post-session, residents reassessed their knowledge and provided feedback on their VR and LLM-powered speechbot experiences. Results/discussion: Residents found that the 3D reconstructions segmented semi-automatically by deep learning algorithms and AI-driven self-learning via speechbot was highly valuable. The 3D reconstructions, especially in the interventional radiology session, were helpful and the benefit is augmented by VR where navigating the models is seamless and perception of depth is pronounced. Residents also found conversing with the AI-speechbot seamless and was valuable in their post session self-learning. The major drawback of VR was motion sickness, which was mild and improved over time. Conclusion: AI-assisted VR radiology education could be used to develop new and accessible ways of teaching a variety of radiology topics in a seamless and cost-effective way. This could be especially useful in supporting radiology education remotely in regions which lack local radiology expertise. © 2025},

keywords = {3D segmentation, AI speechbots, Article, artificial intelligence chatbot, ChatGPT, computer assisted tomography, Deep learning, headache, human, Image segmentation, interventional radiology, Large language model, Likert scale, nausea, Proof of concept, prospective study, radiology, radiology on call training, resource limited setting, Teaching, Training, ultrasound, Virtual Reality, voice recognition},

pubstate = {published},

tppubtype = {article}

}

@article{afzal_next_2025,

title = {Next Generation XR Systems - Large Language Models Meet Augmented and Virtual Reality},

author = {M. Z. Afzal and S. K. A. Ali and D. Stricker and P. Eisert and A. Hilsmann and D. Perez-Marcos and M. Bianchi and S. Crottaz-Herbette and R. De Ioris and E. Mangina and M. Sanguineti and A. Salaberria and O. Lopez De Lacalle and A. Garcia-Pablos and M. Cuadros},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003598602&doi=10.1109%2fMCG.2025.3548554&partnerID=40&md5=b709a0c8cf47cc55a52cea73eb9ef15d},

doi = {10.1109/MCG.2025.3548554},

issn = {02721716 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {IEEE Computer Graphics and Applications},

volume = {45},

number = {1},

pages = {43–55},

abstract = {Extended reality (XR) is evolving rapidly, offering new paradigms for human-computer interaction. This position paper argues that integrating large language models (LLMs) with XR systems represents a fundamental shift toward more intelligent, context-aware, and adaptive mixed-reality experiences. We propose a structured framework built on three key pillars: first, perception and situational awareness, second, knowledge modeling and reasoning, and third, visualization and interaction. We believe leveraging LLMs within XR environments enables enhanced situational awareness, real-time knowledge retrieval, and dynamic user interaction, surpassing traditional XR capabilities. We highlight the potential of this integration in neurorehabilitation, safety training, and architectural design while underscoring ethical considerations, such as privacy, transparency, and inclusivity. This vision aims to spark discussion and drive research toward more intelligent, human-centric XR systems. © 2025 IEEE.},

keywords = {adult, Article, Augmented and virtual realities, Augmented Reality, Awareness, Context-Aware, human, Information Retrieval, Knowledge model, Knowledge reasoning, Knowledge retrieval, Language Model, Large language model, Mixed reality, neurorehabilitation, Position papers, privacy, Real- time, Reasoning, Situational awareness, Virtual environments, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{stacchio_minevra_2025,

title = {MineVRA: Exploring the Role of Generative AI-Driven Content Development in XR Environments through a Context-Aware Approach},

author = {L. Stacchio and E. Balloni and E. Frontoni and M. Paolanti and P. Zingaretti and R. Pierdicca},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003746367&doi=10.1109%2fTVCG.2025.3549160&partnerID=40&md5=70b162b574eebbb0cb71db871aa787e1},

doi = {10.1109/TVCG.2025.3549160},

issn = {10772626 (ISSN)},

year  = {2025},

date = {2025-01-01},

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

volume = {31},

number = {5},

pages = {3602–3612},

abstract = {The convergence of Artificial Intelligence (AI), Computer Vision (CV), Computer Graphics (CG), and Extended Reality (XR) is driving innovation in immersive environments. A key challenge in these environments is the creation of personalized 3D assets, traditionally achieved through manual modeling, a time-consuming process that often fails to meet individual user needs. More recently, Generative AI (GenAI) has emerged as a promising solution for automated, context-aware content generation. In this paper, we present MineVRA (Multimodal generative artificial iNtelligence for contExt-aware Virtual Reality Assets), a novel Human-In-The-Loop (HITL) XR framework that integrates GenAI to facilitate coherent and adaptive 3D content generation in immersive scenarios. To evaluate the effectiveness of this approach, we conducted a comparative user study analyzing the performance and user satisfaction of GenAI-generated 3D objects compared to those generated by Sketchfab in different immersive contexts. The results suggest that GenAI can significantly complement traditional 3D asset libraries, with valuable design implications for the development of human-centered XR environments. © 1995-2012 IEEE.},

keywords = {adult, Article, Artificial intelligence, Computer graphics, Computer vision, Content Development, Contents development, Context-Aware, Context-aware approaches, Extended reality, female, Generative adversarial networks, Generative AI, generative artificial intelligence, human, Human-in-the-loop, Immersive, Immersive environment, male, Multi-modal, User need, Virtual environments, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{hassoulas_pilot_2025,

title = {A pilot study investigating the efficacy of technology enhanced case based learning (CBL) in small group teaching},

author = {A. Hassoulas and O. Crawford and S. Hemrom and A. Almeida and M. J. Coffey and M. Hodgson and B. Leveridge and D. Karwa and A. Lethbridge and H. Williams and A. Voisey and K. Reed and S. Patel and K. Hart and H. Shaw},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004223025&doi=10.1038%2fs41598-025-99764-5&partnerID=40&md5=8588cac4c3ffe437e667ba4373e010ec},

doi = {10.1038/s41598-025-99764-5},

issn = {20452322 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Scientific Reports},

volume = {15},

number = {1},

abstract = {The recent paradigm shift in teaching provision within higher education, following the COVID-19 pandemic, has led to blended models of learning prevailing in the pedagogic literature and in education practice. This shift has also resulted in an abundance of tools and technologies coming to market. Whilst the value of integrating technology into teaching and assessment has been well-established in the literature, the magnitude of choice available to educators and to students can be overwhelming. The current pilot investigated the feasibility of integrating key technologies in delivering technology-enhanced learning (TEL) case-based learning (CBL) within a sample of year two medical students. The cohort was selected at random, as was the control group receiving conventional CBL. Both groups were matched on prior academic performance. The TEL-CBL group received (1) in-person tutorials delivered within an immersive learning suite, (2) access to 3D anatomy software to explore during their self-directed learning time, (3) virtual reality (VR) guided anatomy exploration during tutorials, (4) access to a generative AI-based simulated virtual patient repository to practice key skills such as communication and history taking, and (5) an immersive medical emergency simulation. Metrics assessed included formative academic performance, student learning experience, and confidence in relation to communication and clinical skills. The results revealed that the TEL-CBL group outperformed their peers in successive formative assessments (p < 0.05), engaged thoroughly with the technologies at their disposal, and reported that these technologies enhanced their learning experience. Furthermore, students reported that access to the GenAI-simulated virtual patient platform and the immersive medical emergency simulation improved their clinical confidence and gave them a useful insight into what they can expect during the clinical phase of their medical education. The results are discussed in relation to the advantages that key emerging technologies may play in enhancing student performance, experience and confidence. © The Author(s) 2025.},

keywords = {coronavirus disease 2019, Covid-19, epidemiology, female, human, Humans, Learning, male, Medical, Medical student, Pilot Projects, pilot study, problem based learning, Problem-Based Learning, procedures, SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2, Students, Teaching, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{pooryousef_collaborative_2024,

title = {Collaborative Forensic Autopsy Documentation and Supervised Report Generation using a Hybrid Mixed-Reality Environment and Generative AI},

author = {V. Pooryousef and M. Cordeil and L. Besançon and R. Bassed and T. Dwyer},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204066202&doi=10.1109%2fTVCG.2024.3456212&partnerID=40&md5=d1abaf1aaf3b033df21067ea34b8b98a},

doi = {10.1109/TVCG.2024.3456212},

issn = {10772626 (ISSN)},

year  = {2024},

date = {2024-01-01},

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

volume = {30},

number = {11},

pages = {7452–7462},

abstract = {—Forensic investigation is a complex procedure involving experts working together to establish cause of death and report findings to legal authorities. While new technologies are being developed to provide better post-mortem imaging capabilities—including mixed-reality (MR) tools to support 3D visualisation of such data—these tools do not integrate seamlessly into their existing collaborative workflow and report authoring process, requiring extra steps, e.g. to extract imagery from the MR tool and combine with physical autopsy findings for inclusion in the report. Therefore, in this work we design and evaluate a new forensic autopsy report generation workflow and present a novel documentation system using hybrid mixed-reality approaches to integrate visualisation, voice and hand interaction, as well as collaboration and procedure recording. Our preliminary findings indicate that this approach has the potential to improve data management, aid reviewability, and thus, achieve more robust standards. Further, it potentially streamlines report generation and minimise dependency on external tools and assistance, reducing autopsy time and related costs. This system also offers significant potential for education. A free copy of this paper and all supplemental materials are available at https://osf.io/ygfzx. © 2024 IEEE.},

keywords = {Artificial intelligence, Augmented Reality, Autopsy, Causes of death, Complex procedure, Computer graphics, computer interface, Data visualization, Digital forensics, Documentation, Forensic autopsy, Forensic engineering, Forensic investigation, forensic science, Forensic Sciences, Generative AI, human, Humans, Imaging, Information Management, Laws and legislation, Mixed reality, Mixed-reality environment, Post mortem imaging, procedures, Report generation, Three-Dimensional, three-dimensional imaging, User-Computer Interface, Visualization, Workflow},

pubstate = {published},

tppubtype = {article}

}

@article{sheehy_development_2024,

title = {Development and Initial Testing of an Artificial Intelligence-Based Virtual Reality Companion for People Living with Dementia in Long-Term Care},

author = {L. Sheehy and S. Bouchard and A. Kakkar and R. El Hakim and J. Lhoest and A. Frank},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205071099&doi=10.3390%2fjcm13185574&partnerID=40&md5=844732ff858a0d5feb0a95a54093ad4d},

doi = {10.3390/jcm13185574},

issn = {20770383 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Journal of Clinical Medicine},

volume = {13},

number = {18},

abstract = {Background/Objectives: Feelings of loneliness are common in people living with dementia (PLWD) in long-term care (LTC). The goals of this study were to describe the development of a novel virtual companion for PLWD living in LTC and assess its feasibility and acceptability. Methods: The computer-generated virtual companion, presented using a head-mounted virtual reality display, was developed in two stages. In Stage 1, the virtual companion asked questions designed to encourage conversation and reminiscence. In Stage 2, more powerful artificial intelligence tools allowed the virtual companion to engage users in nuanced discussions on any topic. PLWD in LTC tested the application at each stage to assess feasibility and acceptability. Results: Ten PLWD living in LTC participated in Stage 1 (4 men and 6 women; average 82 years old) and Stage 2 (2 men and 8 women; average 87 years old). Session lengths ranged from 0:00 to 5:30 min in Stage 1 and 0:00 to 53:50 min in Stage 2. Speech recognition issues and a limited repertoire of questions limited acceptance in Stage 1. Enhanced conversational ability in Stage 2 led to intimate and meaningful conversations with many participants. Many users found the head-mounted display heavy. There were no complaints of simulator sickness. The virtual companion was best suited to PLWD who could engage in reciprocal conversation. After Stage 2, response latency was identified as an opportunity for improvement in future versions. Conclusions: Virtual reality and artificial intelligence can be used to create a virtual companion that is acceptable and enjoyable to some PLWD living in LTC. Ongoing innovations in hardware and software will allow future iterations to provide more natural conversational interaction and an enhanced social experience. © 2024 by the authors.},

keywords = {aged, Article, Artificial intelligence, cognitive decline, cognitive impairment, compassion, conversation, Dementia, Elderly, female, human, large language models, long term care, long-term care, major clinical study, male, program acceptability, program feasibility, reaction time, reminiscence, speech discrimination, very elderly, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{hubal_rethinking_2024,

title = {Rethinking some Virtual Human Applications},

author = {R. Hubal},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215435480&partnerID=40&md5=4526a7d54606ef0f1cc6234099eb4aae},

issn = {15548716 (ISSN)},

year  = {2024},

date = {2024-01-01},

journal = {Annual Review of CyberTherapy and Telemedicine},

volume = {22},

pages = {28–33},

abstract = {Increasingly realistic virtual environments incorporating virtual characters have been used to train or assess actual behavior, such as of people at risk, and identify reasons to remediate or intervene. Technology has improved so rapidly that today’s capabilities to create situations to focus training and intervention outshine past efforts. To name just a few current examples, tools like Unreal’s MetaHuman Creator for creating characters, Midjourney for creating environments, OpenAI’s ChatGPT for scripting, and GIFT for tutoring have enormous potential, as these tools promise to reduce simulation costs and increase realism. This paper, in contrast, discusses some movement in the other direction: Recent efforts suggest that increased realism may not always have resulting cost-benefit for training and assessment. Lessons learned and recommendations are presented to guide future developers. © 2024, Interactive Media Institute. All rights reserved.},

keywords = {Article, Artificial intelligence, character and application fidelity, ChatGPT, Consequential conversations, conversation, Engagement, human, Large language model, Learning, responsibility, responsive virtual humans, social competence, telehealth, Virtual Reality},

pubstate = {published},

tppubtype = {article}

}

@article{vlasov_gala_2023,

title = {GALA Inspired by Klimt's Art: Text-to-image Processing with Implementation in Interaction and Perception Studies: Library and Case Examples},

author = {A. V. Vlasov},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182461798&partnerID=40&md5=0c3f5f4214a46db51f46f0092495eb2b},

issn = {15548716 (ISSN)},

year  = {2023},

date = {2023-01-01},

journal = {Annual Review of CyberTherapy and Telemedicine},

volume = {21},

pages = {200–205},

abstract = {Objectives: (a) to develop a library with AI generated content (AIGC) based on а combinatorial scheme of prompting for interaction and perception research; (b) to show examples of AIGC implementation. The result is a public library for applied research in the cyber-psychological community (CYPSY). The Generative Art Library Abstractions (GALA) include images (Figures 1-2) based on the text-image model and inspired by the artwork of Gustav Klimt. They can be used for comparative analysis (benchmarking), end-to-end evaluation, and advanced design. This allows experimentation with complex human-computer interaction (HCI) architectures and visual communication systems, and provides creative design support for experimenting. Examples include: interactive perception of positively colored generative images; HCI dialogues using visual language; generated moods in a VR environment; brain-computer interface for HCI. Respectfully, these visualization resources are a valuable example of AIGC for next-generation R&D. Any suggestions from the CYPSY community are welcome. © 2023, Interactive Media Institute. All rights reserved.},

keywords = {AIGC, applied research, art library, Article, Artificial intelligence, benchmarking, dataset, GALA, human, Human computer interaction, Image processing, Klimt, library, life satisfaction, neuropoem, Text-to-image, Virtual Reality, Wellbeing},

pubstate = {published},

tppubtype = {article}

}