AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can use the tag cloud to select only the papers dealing with specific research topics.
You can expand the Abstract, Links and BibTex record for each paper.
2025
Liu, G.; Du, H.; Wang, J.; Niyato, D.; Kim, D. I.
Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse Journal Article
In: IEEE Transactions on Mobile Computing, 2025, ISSN: 15361233 (ISSN).
Abstract | Links | BibTeX | Tags: Contest Theory, Deep learning, Deep reinforcement learning, Diffusion Model, Generative adversarial networks, Generative AI, High quality, Image generation, Image generations, Immersive technologies, Metaverses, Mobile edge computing, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Semantic data, Virtual addresses, Virtual environments, Virtual Reality
@article{liu_contract-inspired_2025,
title = {Contract-Inspired Contest Theory for Controllable Image Generation in Mobile Edge Metaverse},
author = {G. Liu and H. Du and J. Wang and D. Niyato and D. I. Kim},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000066834&doi=10.1109%2fTMC.2025.3550815&partnerID=40&md5=3cb5a2143b9ce4ca7f931a60f1bf239c},
doi = {10.1109/TMC.2025.3550815},
issn = {15361233 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Mobile Computing},
abstract = {The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.},
keywords = {Contest Theory, Deep learning, Deep reinforcement learning, Diffusion Model, Generative adversarial networks, Generative AI, High quality, Image generation, Image generations, Immersive technologies, Metaverses, Mobile edge computing, Reinforcement Learning, Reinforcement learnings, Resource allocation, Resources allocation, Semantic data, Virtual addresses, Virtual environments, Virtual Reality},
pubstate = {published},
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The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2002-2012 IEEE.
Mekki, Y. M.; Simon, L. V.; Freeman, W. D.; Qadir, J.
Medical Education Metaverses (MedEd Metaverses): Opportunities, Use Case, and Guidelines Journal Article
In: Computer, vol. 58, no. 3, pp. 60–70, 2025, ISSN: 00189162 (ISSN).
Abstract | Links | BibTeX | Tags: Adaptive feedback, Augmented Reality, Immersive learning, Medical education, Metaverses, Performance tracking, Remote resources, Remote training, Resource efficiencies, Training efficiency, Virtual environments
@article{mekki_medical_2025,
title = {Medical Education Metaverses (MedEd Metaverses): Opportunities, Use Case, and Guidelines},
author = {Y. M. Mekki and L. V. Simon and W. D. Freeman and J. Qadir},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218631349&doi=10.1109%2fMC.2024.3474033&partnerID=40&md5=65f46cf9b8d98eaf0fcd6843b9ebc41e},
doi = {10.1109/MC.2024.3474033},
issn = {00189162 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Computer},
volume = {58},
number = {3},
pages = {60–70},
abstract = {This article explores how artificial intelligence (AI), particularly generative AI (GenAI), can enhance extended reality (XR) applications in medical education (MedEd) metaverses. We compare traditional augmented reality/virtual reality methods with AI-enabled XR metaverses, highlighting improvements in immersive learning, adaptive feedback, personalized performance tracking, remote training, and resource efficiency. © 1970-2012 IEEE.},
keywords = {Adaptive feedback, Augmented Reality, Immersive learning, Medical education, Metaverses, Performance tracking, Remote resources, Remote training, Resource efficiencies, Training efficiency, Virtual environments},
pubstate = {published},
tppubtype = {article}
}
This article explores how artificial intelligence (AI), particularly generative AI (GenAI), can enhance extended reality (XR) applications in medical education (MedEd) metaverses. We compare traditional augmented reality/virtual reality methods with AI-enabled XR metaverses, highlighting improvements in immersive learning, adaptive feedback, personalized performance tracking, remote training, and resource efficiency. © 1970-2012 IEEE.
Koizumi, M.; Ohsuga, M.; Corchado, J. M.
Development and Assessment of a System to Help Students Improve Self-compassion Proceedings Article
In: R., Chinthaginjala; P., Sitek; N., Min-Allah; K., Matsui; S., Ossowski; S., Rodríguez (Ed.): Lect. Notes Networks Syst., pp. 43–52, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 23673370 (ISSN); 978-303182072-4 (ISBN).
Abstract | Links | BibTeX | Tags: Avatar, Generative adversarial networks, Generative AI, Health issues, Mental health, Self-compassion, Students, Training program, University students, Virtual avatar, Virtual environments, Virtual Reality, Virtual Space, Virtual spaces, Visual imagery
@inproceedings{koizumi_development_2025,
title = {Development and Assessment of a System to Help Students Improve Self-compassion},
author = {M. Koizumi and M. Ohsuga and J. M. Corchado},
editor = {Chinthaginjala R. and Sitek P. and Min-Allah N. and Matsui K. and Ossowski S. and Rodríguez S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218979175&doi=10.1007%2f978-3-031-82073-1_5&partnerID=40&md5=b136d4a114ce5acfa89f907ccecc145f},
doi = {10.1007/978-3-031-82073-1_5},
isbn = {23673370 (ISSN); 978-303182072-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Networks Syst.},
volume = {1259},
pages = {43–52},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Mental health issues are becoming more prevalent among university students. The mindful self-compassion (MSC) training program, which was introduced to address this issue, has shown some efficacy. However, many people, particularly Japanese people, have difficulty recalling visual imagery or feel uncomfortable or resistant to treating themselves with compassion. This study proposes and develops a system that uses virtual space and avatars to help individuals improve their self-compassion. In the proposed system, the user first selects an avatar of a person with whom to talk (hereafter referred to as “partner”), and then talks about the problem to the avatar of his/her choice. Next, the user changes viewpoints and listens to the problem as the partner’s avatar and responds with compassion. Finally, the user returns to his/her own avatar and listens to the compassionate response spoken as the partner avatar. We first conducted surveys to understand the important system components, and then developed prototypes. In light of the results of the experiments, we improved the prototype by introducing a generative AI. The first prototype used the user’s spoken voice as it was, but the improved system uses the generative AI to organize and convert the voice and present it. In addition, we added a function to generate and add advice with compression. The proposed system is expected to contribute to the improvement of students’ self-compassion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Avatar, Generative adversarial networks, Generative AI, Health issues, Mental health, Self-compassion, Students, Training program, University students, Virtual avatar, Virtual environments, Virtual Reality, Virtual Space, Virtual spaces, Visual imagery},
pubstate = {published},
tppubtype = {inproceedings}
}
Mental health issues are becoming more prevalent among university students. The mindful self-compassion (MSC) training program, which was introduced to address this issue, has shown some efficacy. However, many people, particularly Japanese people, have difficulty recalling visual imagery or feel uncomfortable or resistant to treating themselves with compassion. This study proposes and develops a system that uses virtual space and avatars to help individuals improve their self-compassion. In the proposed system, the user first selects an avatar of a person with whom to talk (hereafter referred to as “partner”), and then talks about the problem to the avatar of his/her choice. Next, the user changes viewpoints and listens to the problem as the partner’s avatar and responds with compassion. Finally, the user returns to his/her own avatar and listens to the compassionate response spoken as the partner avatar. We first conducted surveys to understand the important system components, and then developed prototypes. In light of the results of the experiments, we improved the prototype by introducing a generative AI. The first prototype used the user’s spoken voice as it was, but the improved system uses the generative AI to organize and convert the voice and present it. In addition, we added a function to generate and add advice with compression. The proposed system is expected to contribute to the improvement of students’ self-compassion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Li, Y.; Pang, E. C. H.; Ng, C. S. Y.; Azim, M.; Leung, H.
Enhancing Linear Algebra Education with AI-Generated Content in the CityU Metaverse: A Comparative Study Proceedings Article
In: T., Hao; J.G., Wu; X., Luo; Y., Sun; Y., Mu; S., Ge; W., Xie (Ed.): Lect. Notes Comput. Sci., pp. 3–16, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-981964406-3 (ISBN).
Abstract | Links | BibTeX | Tags: Comparatives studies, Digital age, Digital interactions, digital twin, Educational metaverse, Engineering education, Generative AI, Immersive, Matrix algebra, Metaverse, Metaverses, Personnel training, Students, Teaching, University campus, Virtual environments, virtual learning environment, Virtual learning environments, Virtual Reality, Virtualization
@inproceedings{li_enhancing_2025,
title = {Enhancing Linear Algebra Education with AI-Generated Content in the CityU Metaverse: A Comparative Study},
author = {Y. Li and E. C. H. Pang and C. S. Y. Ng and M. Azim and H. Leung},
editor = {Hao T. and Wu J.G. and Luo X. and Sun Y. and Mu Y. and Ge S. and Xie W.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003632691&doi=10.1007%2f978-981-96-4407-0_1&partnerID=40&md5=c067ba5d4c15e9c0353bf315680531fc},
doi = {10.1007/978-981-96-4407-0_1},
isbn = {03029743 (ISSN); 978-981964406-3 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lect. Notes Comput. Sci.},
volume = {15589 LNCS},
pages = {3–16},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {In today’s digital age, the metaverse is emerging as the forthcoming evolution of the internet. It provides an immersive space that marks a new frontier in the way digital interactions are facilitated and experienced. In this paper, we present the CityU Metaverse, which aims to construct a digital twin of our university campus. It is designed as an educational virtual world where learning applications can be embedded in this virtual campus, supporting not only remote and collaborative learning but also professional technical training to enhance educational experiences through immersive and interactive learning. To evaluate the effectiveness of this educational metaverse, we conducted an experiment focused on 3D linear transformation in linear algebra, with teaching content generated by generative AI, comparing our metaverse system with traditional teaching methods. Knowledge tests and surveys assessing learning interest revealed that students engaged with the CityU Metaverse, facilitated by AI-generated content, outperformed those in traditional settings and reported greater enjoyment during the learning process. The work provides valuable perspectives on the behaviors and interactions within the metaverse by analyzing user preferences and learning outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.},
keywords = {Comparatives studies, Digital age, Digital interactions, digital twin, Educational metaverse, Engineering education, Generative AI, Immersive, Matrix algebra, Metaverse, Metaverses, Personnel training, Students, Teaching, University campus, Virtual environments, virtual learning environment, Virtual learning environments, Virtual Reality, Virtualization},
pubstate = {published},
tppubtype = {inproceedings}
}
In today’s digital age, the metaverse is emerging as the forthcoming evolution of the internet. It provides an immersive space that marks a new frontier in the way digital interactions are facilitated and experienced. In this paper, we present the CityU Metaverse, which aims to construct a digital twin of our university campus. It is designed as an educational virtual world where learning applications can be embedded in this virtual campus, supporting not only remote and collaborative learning but also professional technical training to enhance educational experiences through immersive and interactive learning. To evaluate the effectiveness of this educational metaverse, we conducted an experiment focused on 3D linear transformation in linear algebra, with teaching content generated by generative AI, comparing our metaverse system with traditional teaching methods. Knowledge tests and surveys assessing learning interest revealed that students engaged with the CityU Metaverse, facilitated by AI-generated content, outperformed those in traditional settings and reported greater enjoyment during the learning process. The work provides valuable perspectives on the behaviors and interactions within the metaverse by analyzing user preferences and learning outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Casas, L.; Hannah, S.; Mitchell, K.
HoloJig: Interactive Spoken Prompt Specified Generative AI Environments Journal Article
In: IEEE Computer Graphics and Applications, vol. 45, no. 2, pp. 69–77, 2025, ISSN: 02721716 (ISSN).
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
Tracy, K.; Spantidi, O.
Impact of GPT-Driven Teaching Assistants in VR Learning Environments Journal Article
In: IEEE Transactions on Learning Technologies, vol. 18, pp. 192–205, 2025, ISSN: 19391382 (ISSN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Cognitive loads, Computer interaction, Contrastive Learning, Control groups, Experimental groups, Federated learning, Generative AI, Generative artificial intelligence (GenAI), human–computer interaction, Interactive learning environment, interactive learning environments, Learning efficacy, Learning outcome, learning outcomes, Student engagement, Teaching assistants, Virtual environments, Virtual Reality (VR)
@article{tracy_impact_2025,
title = {Impact of GPT-Driven Teaching Assistants in VR Learning Environments},
author = {K. Tracy and O. Spantidi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001083336&doi=10.1109%2fTLT.2025.3539179&partnerID=40&md5=34fea4ea8517a061fe83b8294e1a9a87},
doi = {10.1109/TLT.2025.3539179},
issn = {19391382 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Transactions on Learning Technologies},
volume = {18},
pages = {192–205},
abstract = {Virtual reality (VR) has emerged as a transformative educational tool, enabling immersive learning environments that promote student engagement and understanding of complex concepts. However, despite the growing adoption of VR in education, there remains a significant gap in research exploring how generative artificial intelligence (AI), such as generative pretrained transformer can further enhance these experiences by reducing cognitive load and improving learning outcomes. This study examines the impact of an AI-driven instructor assistant in VR classrooms on student engagement, cognitive load, knowledge retention, and performance. A total of 52 participants were divided into two groups experiencing a VR lesson on the bubble sort algorithm, one with only a prescripted virtual instructor (control group), and the other with the addition of an AI instructor assistant (experimental group). Statistical analysis of postlesson quizzes and cognitive load assessments was conducted using independent t-tests and analysis of variance (ANOVA), with the cognitive load being measured through a postexperiment questionnaire. The study results indicate that the experimental group reported significantly higher engagement compared to the control group. While the AI assistant did not significantly improve postlesson assessment scores, it enhanced conceptual knowledge transfer. The experimental group also demonstrated lower intrinsic cognitive load, suggesting the assistant reduced the perceived complexity of the material. Higher germane and general cognitive loads indicated that students were more invested in meaningful learning without feeling overwhelmed. © 2008-2011 IEEE.},
keywords = {Adversarial machine learning, Cognitive loads, Computer interaction, Contrastive Learning, Control groups, Experimental groups, Federated learning, Generative AI, Generative artificial intelligence (GenAI), human–computer interaction, Interactive learning environment, interactive learning environments, Learning efficacy, Learning outcome, learning outcomes, Student engagement, Teaching assistants, Virtual environments, Virtual Reality (VR)},
pubstate = {published},
tppubtype = {article}
}
Virtual reality (VR) has emerged as a transformative educational tool, enabling immersive learning environments that promote student engagement and understanding of complex concepts. However, despite the growing adoption of VR in education, there remains a significant gap in research exploring how generative artificial intelligence (AI), such as generative pretrained transformer can further enhance these experiences by reducing cognitive load and improving learning outcomes. This study examines the impact of an AI-driven instructor assistant in VR classrooms on student engagement, cognitive load, knowledge retention, and performance. A total of 52 participants were divided into two groups experiencing a VR lesson on the bubble sort algorithm, one with only a prescripted virtual instructor (control group), and the other with the addition of an AI instructor assistant (experimental group). Statistical analysis of postlesson quizzes and cognitive load assessments was conducted using independent t-tests and analysis of variance (ANOVA), with the cognitive load being measured through a postexperiment questionnaire. The study results indicate that the experimental group reported significantly higher engagement compared to the control group. While the AI assistant did not significantly improve postlesson assessment scores, it enhanced conceptual knowledge transfer. The experimental group also demonstrated lower intrinsic cognitive load, suggesting the assistant reduced the perceived complexity of the material. Higher germane and general cognitive loads indicated that students were more invested in meaningful learning without feeling overwhelmed. © 2008-2011 IEEE.
Li, Z.; Zhang, H.; Peng, C.; Peiris, R.
Exploring Large Language Model-Driven Agents for Environment-Aware Spatial Interactions and Conversations in Virtual Reality Role-Play Scenarios Proceedings Article
In: Proc. - IEEE Conf. Virtual Real. 3D User Interfaces, VR, pp. 1–11, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833153645-9 (ISBN).
Abstract | Links | BibTeX | Tags: Chatbots, Computer simulation languages, Context- awareness, context-awareness, Digital elevation model, Generative AI, Human-AI Interaction, Language Model, Large language model, large language models, Model agents, Role-play simulation, role-play simulations, Role-plays, Spatial interaction, Virtual environments, Virtual Reality, Virtual-reality environment
@inproceedings{li_exploring_2025,
title = {Exploring Large Language Model-Driven Agents for Environment-Aware Spatial Interactions and Conversations in Virtual Reality Role-Play Scenarios},
author = {Z. Li and H. Zhang and C. Peng and R. Peiris},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002706893&doi=10.1109%2fVR59515.2025.00025&partnerID=40&md5=60f22109e054c9035a0c2210bb797039},
doi = {10.1109/VR59515.2025.00025},
isbn = {979-833153645-9 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces, VR},
pages = {1–11},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Recent research has begun adopting Large Language Model (LLM) agents to enhance Virtual Reality (VR) interactions, creating immersive chatbot experiences. However, while current studies focus on generating dialogue from user speech inputs, their abilities to generate richer experiences based on the perception of LLM agents' VR environments and interaction cues remain unexplored. Hence, in this work, we propose an approach that enables LLM agents to perceive virtual environments and generate environment-aware interactions and conversations for an embodied human-AI interaction experience in VR environments. Here, we define a schema for describing VR environments and their interactions through text prompts. We evaluate the performance of our method through five role-play scenarios created using our approach in a study with 14 participants. The findings discuss the opportunities and challenges of our proposed approach for developing environment-aware LLM agents that facilitate spatial interactions and conversations within VR role-play scenarios. © 2025 IEEE.},
keywords = {Chatbots, Computer simulation languages, Context- awareness, context-awareness, Digital elevation model, Generative AI, Human-AI Interaction, Language Model, Large language model, large language models, Model agents, Role-play simulation, role-play simulations, Role-plays, Spatial interaction, Virtual environments, Virtual Reality, Virtual-reality environment},
pubstate = {published},
tppubtype = {inproceedings}
}
Recent research has begun adopting Large Language Model (LLM) agents to enhance Virtual Reality (VR) interactions, creating immersive chatbot experiences. However, while current studies focus on generating dialogue from user speech inputs, their abilities to generate richer experiences based on the perception of LLM agents' VR environments and interaction cues remain unexplored. Hence, in this work, we propose an approach that enables LLM agents to perceive virtual environments and generate environment-aware interactions and conversations for an embodied human-AI interaction experience in VR environments. Here, we define a schema for describing VR environments and their interactions through text prompts. We evaluate the performance of our method through five role-play scenarios created using our approach in a study with 14 participants. The findings discuss the opportunities and challenges of our proposed approach for developing environment-aware LLM agents that facilitate spatial interactions and conversations within VR role-play scenarios. © 2025 IEEE.
Ly, D. -N.; Do, H. -N.; Tran, M. -T.; Le, K. -D.
Evaluation of AI-Based Assistant Representations on User Interaction in Virtual Explorations Proceedings Article
In: W., Buntine; M., Fjeld; T., Tran; M.-T., Tran; B., Huynh Thi Thanh; T., Miyoshi (Ed.): Commun. Comput. Info. Sci., pp. 323–337, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 18650929 (ISSN); 978-981964287-8 (ISBN).
Abstract | Links | BibTeX | Tags: 360-degree Video, AI-Based Assistant, Cultural heritages, Cultural science, Multiusers, Single users, Social interactions, Three dimensional computer graphics, User interaction, Users' experiences, Virtual environments, Virtual Exploration, Virtual Reality, Virtualization
@inproceedings{ly_evaluation_2025,
title = {Evaluation of AI-Based Assistant Representations on User Interaction in Virtual Explorations},
author = {D. -N. Ly and H. -N. Do and M. -T. Tran and K. -D. Le},
editor = {Buntine W. and Fjeld M. and Tran T. and Tran M.-T. and Huynh Thi Thanh B. and Miyoshi T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004253350&doi=10.1007%2f978-981-96-4288-5_26&partnerID=40&md5=5f0a8c1e356cd3bdd4dda7f96f272154},
doi = {10.1007/978-981-96-4288-5_26},
isbn = {18650929 (ISSN); 978-981964287-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Commun. Comput. Info. Sci.},
volume = {2352 CCIS},
pages = {323–337},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Exploration activities, such as tourism, cultural heritage, and science, enhance knowledge and understanding. The rise of 360-degree videos allows users to explore cultural landmarks and destinations remotely. While multi-user VR environments encourage collaboration, single-user experiences often lack social interaction. Generative AI, particularly Large Language Models (LLMs), offer a way to improve single-user VR exploration through AI-driven virtual assistants, acting as tour guides or storytellers. However, it’s uncertain whether these assistants require a visual presence, and if so, what form it should take. To investigate this, we developed an AI-based assistant in three different forms: a voice-only avatar, a 3D human-sized avatar, and a mini-hologram avatar, and conducted a user study to evaluate their impact on user experience. The study, which involved 12 participants, found that the visual embodiments significantly reduce feelings of being alone, with distinct user preferences between the Human-sized avatar and the Mini hologram. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.},
keywords = {360-degree Video, AI-Based Assistant, Cultural heritages, Cultural science, Multiusers, Single users, Social interactions, Three dimensional computer graphics, User interaction, Users' experiences, Virtual environments, Virtual Exploration, Virtual Reality, Virtualization},
pubstate = {published},
tppubtype = {inproceedings}
}
Exploration activities, such as tourism, cultural heritage, and science, enhance knowledge and understanding. The rise of 360-degree videos allows users to explore cultural landmarks and destinations remotely. While multi-user VR environments encourage collaboration, single-user experiences often lack social interaction. Generative AI, particularly Large Language Models (LLMs), offer a way to improve single-user VR exploration through AI-driven virtual assistants, acting as tour guides or storytellers. However, it’s uncertain whether these assistants require a visual presence, and if so, what form it should take. To investigate this, we developed an AI-based assistant in three different forms: a voice-only avatar, a 3D human-sized avatar, and a mini-hologram avatar, and conducted a user study to evaluate their impact on user experience. The study, which involved 12 participants, found that the visual embodiments significantly reduce feelings of being alone, with distinct user preferences between the Human-sized avatar and the Mini hologram. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Cao, X.; Ju, K. P.; Li, C.; Jain, D.
SceneGenA11y: How can Runtime Generative tools improve the Accessibility of a Virtual 3D Scene? Proceedings Article
In: Conf Hum Fact Comput Syst Proc, Association for Computing Machinery, 2025, ISBN: 979-840071395-8 (ISBN).
Abstract | Links | BibTeX | Tags: 3D application, 3D modeling, 3D scenes, Accessibility, BLV, DHH, Discrete event simulation, Generative AI, Generative tools, Interactive computer graphics, One dimensional, Runtimes, Three dimensional computer graphics, Video-games, Virtual 3d scene, virtual 3D scenes, Virtual environments, Virtual Reality
@inproceedings{cao_scenegena11y_2025,
title = {SceneGenA11y: How can Runtime Generative tools improve the Accessibility of a Virtual 3D Scene?},
author = {X. Cao and K. P. Ju and C. Li and D. Jain},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105005772656&doi=10.1145%2f3706599.3720265&partnerID=40&md5=9b0bf29c3e89b70efa2d6a3e740829fb},
doi = {10.1145/3706599.3720265},
isbn = {979-840071395-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Conf Hum Fact Comput Syst Proc},
publisher = {Association for Computing Machinery},
abstract = {With the popularity of virtual 3D applications, from video games to educational content and virtual reality scenarios, the accessibility of 3D scene information is vital to ensure inclusive and equitable experiences for all. Previous work include information substitutions like audio description and captions, as well as personalized modifications, but they could only provide predefined accommodations. In this work, we propose SceneGenA11y, a system that responds to the user’s natural language prompts to improve accessibility of a 3D virtual scene in runtime. The system primes LLM agents with accessibility-related knowledge, allowing users to explore the scene and perform verifiable modifications to improve accessibility. We conducted a preliminary evaluation of our system with three blind and low-vision people and three deaf and hard-of-hearing people. The results show that our system is intuitive to use and can successfully improve accessibility. We discussed usage patterns of the system, potential improvements, and integration into apps. We ended with highlighting plans for future work. © 2025 Copyright held by the owner/author(s).},
keywords = {3D application, 3D modeling, 3D scenes, Accessibility, BLV, DHH, Discrete event simulation, Generative AI, Generative tools, Interactive computer graphics, One dimensional, Runtimes, Three dimensional computer graphics, Video-games, Virtual 3d scene, virtual 3D scenes, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
With the popularity of virtual 3D applications, from video games to educational content and virtual reality scenarios, the accessibility of 3D scene information is vital to ensure inclusive and equitable experiences for all. Previous work include information substitutions like audio description and captions, as well as personalized modifications, but they could only provide predefined accommodations. In this work, we propose SceneGenA11y, a system that responds to the user’s natural language prompts to improve accessibility of a 3D virtual scene in runtime. The system primes LLM agents with accessibility-related knowledge, allowing users to explore the scene and perform verifiable modifications to improve accessibility. We conducted a preliminary evaluation of our system with three blind and low-vision people and three deaf and hard-of-hearing people. The results show that our system is intuitive to use and can successfully improve accessibility. We discussed usage patterns of the system, potential improvements, and integration into apps. We ended with highlighting plans for future work. © 2025 Copyright held by the owner/author(s).
Peter, K.; Makosa, I.; Auala, S.; Ndjao, L.; Maasz, D.; Mbinge, U.; Winschiers-Theophilus, H.
Co-creating a VR Narrative Experience of Constructing a Food Storage Following OvaHimba Traditional Practices Proceedings Article
In: IMX - Proc. ACM Int. Conf. Interact. Media Experiences, pp. 418–423, Association for Computing Machinery, Inc, 2025, ISBN: 979-840071391-0 (ISBN).
Abstract | Links | BibTeX | Tags: 3D Modelling, 3D models, 3d-modeling, Co-designs, Community-based, Community-Based Co-Design, Computer aided design, Cultural heritage, Cultural heritages, Food storage, Human computer interaction, Human engineering, Indigenous Knowledge, Information Systems, Interactive computer graphics, Interactive computer systems, IVR, Namibia, OvaHimba, Ovahimbum, Photogrammetry, Sustainable development, Virtual environments, Virtual Reality
@inproceedings{peter_co-creating_2025,
title = {Co-creating a VR Narrative Experience of Constructing a Food Storage Following OvaHimba Traditional Practices},
author = {K. Peter and I. Makosa and S. Auala and L. Ndjao and D. Maasz and U. Mbinge and H. Winschiers-Theophilus},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007984089&doi=10.1145%2f3706370.3731652&partnerID=40&md5=36f95823413852d636b39bd561c97917},
doi = {10.1145/3706370.3731652},
isbn = {979-840071391-0 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IMX - Proc. ACM Int. Conf. Interact. Media Experiences},
pages = {418–423},
publisher = {Association for Computing Machinery, Inc},
abstract = {As part of an attempt to co-create a comprehensive virtual environment in which one can explore and learn traditional practices of the OvaHimba people, we have co-designed and implemented a VR experience to construct a traditional food storage. In collaboration with the OvaHimba community residing in Otjisa, we have explored culturally valid representations of the process. We have further investigated different techniques such as photogrammetry, generative AI and manual methods to develop 3D models. Our findings highlight the importance of context, process, and community-defined relevance in co-design, the fluidity of cultural realities and virtual representations, as well as technical challenges. © 2025 Copyright held by the owner/author(s).},
keywords = {3D Modelling, 3D models, 3d-modeling, Co-designs, Community-based, Community-Based Co-Design, Computer aided design, Cultural heritage, Cultural heritages, Food storage, Human computer interaction, Human engineering, Indigenous Knowledge, Information Systems, Interactive computer graphics, Interactive computer systems, IVR, Namibia, OvaHimba, Ovahimbum, Photogrammetry, Sustainable development, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
As part of an attempt to co-create a comprehensive virtual environment in which one can explore and learn traditional practices of the OvaHimba people, we have co-designed and implemented a VR experience to construct a traditional food storage. In collaboration with the OvaHimba community residing in Otjisa, we have explored culturally valid representations of the process. We have further investigated different techniques such as photogrammetry, generative AI and manual methods to develop 3D models. Our findings highlight the importance of context, process, and community-defined relevance in co-design, the fluidity of cultural realities and virtual representations, as well as technical challenges. © 2025 Copyright held by the owner/author(s).
Pielage, L.; Schmidle, P.; Marschall, B.; Risse, B.
Interactive High-Quality Skin Lesion Generation using Diffusion Models for VR-based Dermatological Education Proceedings Article
In: Int Conf Intell User Interfaces Proc IUI, pp. 878–897, Association for Computing Machinery, 2025, ISBN: 979-840071306-4 (ISBN).
Abstract | Links | BibTeX | Tags: Deep learning, Dermatology, Diffusion Model, diffusion models, Digital elevation model, Generative AI, Graphical user interfaces, Guidance Strategies, Guidance strategy, Image generation, Image generations, Inpainting, Interactive Generation, Medical education, Medical Imaging, Simulation training, Skin lesion, Upsampling, Virtual environments, Virtual Reality
@inproceedings{pielage_interactive_2025,
title = {Interactive High-Quality Skin Lesion Generation using Diffusion Models for VR-based Dermatological Education},
author = {L. Pielage and P. Schmidle and B. Marschall and B. Risse},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001923208&doi=10.1145%2f3708359.3712101&partnerID=40&md5=639eec55b08a54ce813f7c1016c621e7},
doi = {10.1145/3708359.3712101},
isbn = {979-840071306-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Int Conf Intell User Interfaces Proc IUI},
pages = {878–897},
publisher = {Association for Computing Machinery},
abstract = {Malignant melanoma is one of the most lethal forms of cancer when not detected early. As a result, cancer screening programs have been implemented internationally, all of which require visual inspection of skin lesions. Early melanoma detection is a crucial competence in medical and dermatological education, and it is primarily trained using 2D imagery. However, given the intrinsic 3D nature of skin lesions and the importance of incorporating additional contextual information about the patient (e.g., skin type, nearby lesions, etc.), this approach falls short of providing a comprehensive and scalable learning experience. A potential solution is the use of Virtual Reality (VR) scenarios, which can offer an effective strategy to train skin cancer screenings in a realistic 3D setting, thereby enhancing medical students' awareness of early melanoma detection. In this paper, we present a comprehensive pipeline and models for generating malignant melanomas and benign nevi, which can be utilized in VR-based medical training. We use diffusion models for the generation of skin lesions, which we have enhanced with various guiding strategies to give educators maximum flexibility in designing scenarios and seamlessly placing lesions on virtual agents. Additionally, we have developed a tool which comprises a graphical user interface (GUI) enabling the generation of new lesions and adapting existing ones using an intuitive and interactive inpainting strategy. The tool also offers a novel custom upsampling strategy to achieve a sufficient resolution required for diagnostic purposes. The generated skin lesions have been validated in a user study with trained dermatologists, confirming the overall high quality of the generated lesions and the utility for educational purposes. © 2025 Copyright held by the owner/author(s).},
keywords = {Deep learning, Dermatology, Diffusion Model, diffusion models, Digital elevation model, Generative AI, Graphical user interfaces, Guidance Strategies, Guidance strategy, Image generation, Image generations, Inpainting, Interactive Generation, Medical education, Medical Imaging, Simulation training, Skin lesion, Upsampling, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
Malignant melanoma is one of the most lethal forms of cancer when not detected early. As a result, cancer screening programs have been implemented internationally, all of which require visual inspection of skin lesions. Early melanoma detection is a crucial competence in medical and dermatological education, and it is primarily trained using 2D imagery. However, given the intrinsic 3D nature of skin lesions and the importance of incorporating additional contextual information about the patient (e.g., skin type, nearby lesions, etc.), this approach falls short of providing a comprehensive and scalable learning experience. A potential solution is the use of Virtual Reality (VR) scenarios, which can offer an effective strategy to train skin cancer screenings in a realistic 3D setting, thereby enhancing medical students' awareness of early melanoma detection. In this paper, we present a comprehensive pipeline and models for generating malignant melanomas and benign nevi, which can be utilized in VR-based medical training. We use diffusion models for the generation of skin lesions, which we have enhanced with various guiding strategies to give educators maximum flexibility in designing scenarios and seamlessly placing lesions on virtual agents. Additionally, we have developed a tool which comprises a graphical user interface (GUI) enabling the generation of new lesions and adapting existing ones using an intuitive and interactive inpainting strategy. The tool also offers a novel custom upsampling strategy to achieve a sufficient resolution required for diagnostic purposes. The generated skin lesions have been validated in a user study with trained dermatologists, confirming the overall high quality of the generated lesions and the utility for educational purposes. © 2025 Copyright held by the owner/author(s).
Li, H.; Wang, Z.; Liang, W.; Wang, Y.
X’s Day: Personality-Driven Virtual Human Behavior Generation Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 5, pp. 3514–3524, 2025, ISSN: 10772626 (ISSN).
Abstract | Links | BibTeX | Tags: 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
@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}
}
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,
Song, T.; Pabst, F.; Eck, U.; Navab, N.
Enhancing Patient Acceptance of Robotic Ultrasound through Conversational Virtual Agent and Immersive Visualizations Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, vol. 31, no. 5, pp. 2901–2911, 2025, ISSN: 10772626 (ISSN).
Abstract | Links | BibTeX | Tags: 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
@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}
}
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.
Tong, Y.; Qiu, Y.; Li, R.; Qiu, S.; Heng, P. -A.
MS2Mesh-XR: Multi-Modal Sketch-to-Mesh Generation in XR Environments Proceedings Article
In: Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR, pp. 272–276, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833152157-8 (ISBN).
Abstract | Links | BibTeX | Tags: 3D meshes, 3D object, ControlNet, Hand-drawn sketches, Hands movement, High quality, Image-based, immersive visualization, Mesh generation, Multi-modal, Pipeline codes, Realistic images, Three dimensional computer graphics, Virtual environments, Virtual Reality
@inproceedings{tong_ms2mesh-xr_2025,
title = {MS2Mesh-XR: Multi-Modal Sketch-to-Mesh Generation in XR Environments},
author = {Y. Tong and Y. Qiu and R. Li and S. Qiu and P. -A. Heng},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000423684&doi=10.1109%2fAIxVR63409.2025.00052&partnerID=40&md5=caeace6850dcbdf8c1fa0441b98fa8d9},
doi = {10.1109/AIxVR63409.2025.00052},
isbn = {979-833152157-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},
pages = {272–276},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {We present MS2Mesh-XR, a novel multimodal sketch-to-mesh generation pipeline that enables users to create realistic 3D objects in extended reality (XR) environments using hand-drawn sketches assisted by voice inputs. In specific, users can intuitively sketch objects using natural hand movements in mid-air within a virtual environment. By integrating voice inputs, we devise ControlNet to infer realistic images based on the drawn sketches and interpreted text prompts. Users can then review and select their preferred image, which is subsequently reconstructed into a detailed 3D mesh using the Convolutional Reconstruction Model. In particular, our proposed pipeline can generate a high-quality 3D mesh in less than 20 seconds, allowing for immersive visualization and manipulation in runtime XR scenes. We demonstrate the practicability of our pipeline through two use cases in XR settings. By leveraging natural user inputs and cutting-edge generative AI capabilities, our approach can significantly facilitate XR-based creative production and enhance user experiences. Our code and demo will be available at: https://yueqiu0911.github.io/MS2Mesh-XR/. © 2025 IEEE.},
keywords = {3D meshes, 3D object, ControlNet, Hand-drawn sketches, Hands movement, High quality, Image-based, immersive visualization, Mesh generation, Multi-modal, Pipeline codes, Realistic images, Three dimensional computer graphics, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
We present MS2Mesh-XR, a novel multimodal sketch-to-mesh generation pipeline that enables users to create realistic 3D objects in extended reality (XR) environments using hand-drawn sketches assisted by voice inputs. In specific, users can intuitively sketch objects using natural hand movements in mid-air within a virtual environment. By integrating voice inputs, we devise ControlNet to infer realistic images based on the drawn sketches and interpreted text prompts. Users can then review and select their preferred image, which is subsequently reconstructed into a detailed 3D mesh using the Convolutional Reconstruction Model. In particular, our proposed pipeline can generate a high-quality 3D mesh in less than 20 seconds, allowing for immersive visualization and manipulation in runtime XR scenes. We demonstrate the practicability of our pipeline through two use cases in XR settings. By leveraging natural user inputs and cutting-edge generative AI capabilities, our approach can significantly facilitate XR-based creative production and enhance user experiences. Our code and demo will be available at: https://yueqiu0911.github.io/MS2Mesh-XR/. © 2025 IEEE.
Timmerman, K.; Mertens, R.; Yoncalik, A.; Spriet, L.
Cities Unseen: Experiencing the Imagined Proceedings Article
In: Proc. Int. Conf. Tangible, Embed., Embodied Interact., TEI, Association for Computing Machinery, Inc, 2025, ISBN: 979-840071197-8 (ISBN).
Abstract | Links | BibTeX | Tags: Art installation, Embodiment, Immersive, Immersive Storytelling, Multiplayers, Presence, Real time interactions, Sensory Involvement, Through the lens, Urban environments, Virtual environments, Virtual Reality
@inproceedings{timmerman_cities_2025,
title = {Cities Unseen: Experiencing the Imagined},
author = {K. Timmerman and R. Mertens and A. Yoncalik and L. Spriet},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000440301&doi=10.1145%2f3689050.3707685&partnerID=40&md5=6aa0968146eab8f91fba6eaeb30f7f9c},
doi = {10.1145/3689050.3707685},
isbn = {979-840071197-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. Int. Conf. Tangible, Embed., Embodied Interact., TEI},
publisher = {Association for Computing Machinery, Inc},
abstract = {"Cities Unseen: Experiencing the Imagined" is an art installation that reinterprets Italo Calvino's "Invisible Cities" through the lens of virtual reality. The project employs a physical suitcase as a portal, allowing participants to enter and explore virtual urban environments using the Oculus Quest 3. The cityscapes will be developed with generative AI, converting Calvino's descriptions into prompts, creating an immersive space for philosophical reflection on the nature of travel and the boundaries between reality and imagination. By integrating Unity's shared spatial anchors and advanced multiplayer features, "Cities Unseen" supports real-time interaction among participants, emphasizing the social and collaborative dimensions of virtual travel. © 2025 Copyright held by the owner/author(s).},
keywords = {Art installation, Embodiment, Immersive, Immersive Storytelling, Multiplayers, Presence, Real time interactions, Sensory Involvement, Through the lens, Urban environments, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
"Cities Unseen: Experiencing the Imagined" is an art installation that reinterprets Italo Calvino's "Invisible Cities" through the lens of virtual reality. The project employs a physical suitcase as a portal, allowing participants to enter and explore virtual urban environments using the Oculus Quest 3. The cityscapes will be developed with generative AI, converting Calvino's descriptions into prompts, creating an immersive space for philosophical reflection on the nature of travel and the boundaries between reality and imagination. By integrating Unity's shared spatial anchors and advanced multiplayer features, "Cities Unseen" supports real-time interaction among participants, emphasizing the social and collaborative dimensions of virtual travel. © 2025 Copyright held by the owner/author(s).
Linares-Pellicer, J.; Izquierdo-Domenech, J.; Ferri-Molla, I.; Aliaga-Torro, C.
Breaking the Bottleneck: Generative AI as the Solution for XR Content Creation in Education Book Section
In: Lecture Notes in Networks and Systems, vol. 1140, pp. 9–30, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 23673370 (ISSN).
Abstract | Links | BibTeX | Tags: Adversarial machine learning, Augmented Reality, Breakings, Content creation, Contrastive Learning, Development process, Educational context, Federated learning, Generative adversarial networks, Immersive learning, Intelligence models, Learning experiences, Mixed reality, Resource intensity, Technical skills, Virtual environments
@incollection{linares-pellicer_breaking_2025,
title = {Breaking the Bottleneck: Generative AI as the Solution for XR Content Creation in Education},
author = {J. Linares-Pellicer and J. Izquierdo-Domenech and I. Ferri-Molla and C. Aliaga-Torro},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212478399&doi=10.1007%2f978-3-031-71530-3_2&partnerID=40&md5=aefee938cd5b8a74ee811a463d7409ae},
doi = {10.1007/978-3-031-71530-3_2},
isbn = {23673370 (ISSN)},
year = {2025},
date = {2025-01-01},
booktitle = {Lecture Notes in Networks and Systems},
volume = {1140},
pages = {9–30},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {The integration of Extended Reality (XR) technologies-Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)-promises to revolutionize education by offering immersive learning experiences. However, the complexity and resource intensity of content creation hinders the adoption of XR in educational contexts. This chapter explores Generative Artificial Intelligence (GenAI) as a solution, highlighting how GenAI models can facilitate the creation of educational XR content. GenAI enables educators to produce engaging XR experiences without needing advanced technical skills by automating aspects of the development process from ideation to deployment. Practical examples demonstrate GenAI’s current capability to generate assets and program applications, significantly lowering the barrier to creating personalized and interactive learning environments. The chapter also addresses challenges related to GenAI’s application in education, including technical limitations and ethical considerations. Ultimately, GenAI’s integration into XR content creation makes immersive educational experiences more accessible and practical, driven by only natural interactions, promising a future where technology-enhanced learning is universally attainable. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},
keywords = {Adversarial machine learning, Augmented Reality, Breakings, Content creation, Contrastive Learning, Development process, Educational context, Federated learning, Generative adversarial networks, Immersive learning, Intelligence models, Learning experiences, Mixed reality, Resource intensity, Technical skills, Virtual environments},
pubstate = {published},
tppubtype = {incollection}
}
The integration of Extended Reality (XR) technologies-Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR)-promises to revolutionize education by offering immersive learning experiences. However, the complexity and resource intensity of content creation hinders the adoption of XR in educational contexts. This chapter explores Generative Artificial Intelligence (GenAI) as a solution, highlighting how GenAI models can facilitate the creation of educational XR content. GenAI enables educators to produce engaging XR experiences without needing advanced technical skills by automating aspects of the development process from ideation to deployment. Practical examples demonstrate GenAI’s current capability to generate assets and program applications, significantly lowering the barrier to creating personalized and interactive learning environments. The chapter also addresses challenges related to GenAI’s application in education, including technical limitations and ethical considerations. Ultimately, GenAI’s integration into XR content creation makes immersive educational experiences more accessible and practical, driven by only natural interactions, promising a future where technology-enhanced learning is universally attainable. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Zhu, X. T.; Cheerman, H.; Cheng, M.; Kiami, S. R.; Chukoskie, L.; McGivney, E.
Designing VR Simulation System for Clinical Communication Training with LLMs-Based Embodied Conversational Agents Proceedings Article
In: Conf Hum Fact Comput Syst Proc, Association for Computing Machinery, 2025, ISBN: 979-840071395-8 (ISBN).
Abstract | Links | BibTeX | Tags: 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
@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}
}
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).
Sabir, A.; Hussain, R.; Pedro, A.; Park, C.
Personalized construction safety training system using conversational AI in virtual reality Journal Article
In: Automation in Construction, vol. 175, 2025, ISSN: 09265805 (ISSN).
Abstract | Links | BibTeX | Tags: Construction safety, Construction safety training, Conversational AI, Digital elevation model, Helmet mounted displays, Language Model, Large language model, large language models, Personalized safety training, Personnel training, Safety training, Training Systems, Virtual environments, Virtual Reality, Workers'
@article{sabir_personalized_2025,
title = {Personalized construction safety training system using conversational AI in virtual reality},
author = {A. Sabir and R. Hussain and A. Pedro and C. Park},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002741042&doi=10.1016%2fj.autcon.2025.106207&partnerID=40&md5=376284339bf10fd5d799cc56c6643d36},
doi = {10.1016/j.autcon.2025.106207},
issn = {09265805 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Automation in Construction},
volume = {175},
abstract = {Training workers in safety protocols is crucial for mitigating job site hazards, yet traditional methods often fall short. This paper explores integrating virtual reality (VR) and large language models (LLMs) into iSafeTrainer, an AI-powered safety training system. The system allows trainees to engage with trade-specific content tailored to their expertise level in a third-person perspective in a non-immersive desktop virtual environment, eliminating the need for head-mounted displays. An experimental study evaluated the system through qualitative, survey-based assessments, focusing on user satisfaction, experience, engagement, guidance, and confidence. Results showed high satisfaction rates (>85 %) among novice users, with improved safety knowledge. Expert users suggested advanced scenarios, highlighting the system's potential for expansion. The modular architecture supports customization across various construction settings, ensuring adaptability for future improvements. © 2024},
keywords = {Construction safety, Construction safety training, Conversational AI, Digital elevation model, Helmet mounted displays, Language Model, Large language model, large language models, Personalized safety training, Personnel training, Safety training, Training Systems, Virtual environments, Virtual Reality, Workers'},
pubstate = {published},
tppubtype = {article}
}
Training workers in safety protocols is crucial for mitigating job site hazards, yet traditional methods often fall short. This paper explores integrating virtual reality (VR) and large language models (LLMs) into iSafeTrainer, an AI-powered safety training system. The system allows trainees to engage with trade-specific content tailored to their expertise level in a third-person perspective in a non-immersive desktop virtual environment, eliminating the need for head-mounted displays. An experimental study evaluated the system through qualitative, survey-based assessments, focusing on user satisfaction, experience, engagement, guidance, and confidence. Results showed high satisfaction rates (>85 %) among novice users, with improved safety knowledge. Expert users suggested advanced scenarios, highlighting the system's potential for expansion. The modular architecture supports customization across various construction settings, ensuring adaptability for future improvements. © 2024
Lakehal, A.; Alti, A.; Annane, B.
CORES: Context-Aware Emotion-Driven Recommendation System-Based LLM to Improve Virtual Shopping Experiences Journal Article
In: Future Internet, vol. 17, no. 2, 2025, ISSN: 19995903 (ISSN).
Abstract | Links | BibTeX | Tags: Context, Context-Aware, Customisation, Decisions makings, E- commerces, e-commerce, Emotion, emotions, Language Model, Large language model, LLM, Recommendation, Virtual environments, Virtual Reality, Virtual shopping
@article{lakehal_cores_2025,
title = {CORES: Context-Aware Emotion-Driven Recommendation System-Based LLM to Improve Virtual Shopping Experiences},
author = {A. Lakehal and A. Alti and B. Annane},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218626299&doi=10.3390%2ffi17020094&partnerID=40&md5=a0f68e273de08b2c33d03da4cb6c19bb},
doi = {10.3390/fi17020094},
issn = {19995903 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {Future Internet},
volume = {17},
number = {2},
abstract = {In today’s business landscape, artificial intelligence (AI) plays a pivotal role in shopping processes and customization. As the demand for customization grows, virtual reality (VR) emerges as an innovative solution to improve users’ perception and decision making in virtual shopping experiences (VSEs). Despite its potential, limited research has explored the integration of contextual information and emotions in VR to deliver effective product recommendations. This paper presents CORES (context-aware emotion-driven recommendation system), a novel approach designed to enrich users’ experiences and to support decision making in VR. CORES combines advanced large language models (LLMs) and embedding-based context-aware recommendation strategies to provide customized products. Therefore, emotions are collected from social platforms, and relevant contextual information is matched to enable effective recommendation. Additionally, CORES leverages transformers and retrieval-augmented generation (RAG) capabilities to explain recommended items, facilitate VR visualization, and generate insights using various prompt templates. CORES is applied to a VR shop of different items. An empirical study validates the efficiency and accuracy of this approach, achieving a significant average accuracy of 97% and an acceptable response time of 0.3267s in dynamic shopping scenarios. © 2025 by the authors.},
keywords = {Context, Context-Aware, Customisation, Decisions makings, E- commerces, e-commerce, Emotion, emotions, Language Model, Large language model, LLM, Recommendation, Virtual environments, Virtual Reality, Virtual shopping},
pubstate = {published},
tppubtype = {article}
}
In today’s business landscape, artificial intelligence (AI) plays a pivotal role in shopping processes and customization. As the demand for customization grows, virtual reality (VR) emerges as an innovative solution to improve users’ perception and decision making in virtual shopping experiences (VSEs). Despite its potential, limited research has explored the integration of contextual information and emotions in VR to deliver effective product recommendations. This paper presents CORES (context-aware emotion-driven recommendation system), a novel approach designed to enrich users’ experiences and to support decision making in VR. CORES combines advanced large language models (LLMs) and embedding-based context-aware recommendation strategies to provide customized products. Therefore, emotions are collected from social platforms, and relevant contextual information is matched to enable effective recommendation. Additionally, CORES leverages transformers and retrieval-augmented generation (RAG) capabilities to explain recommended items, facilitate VR visualization, and generate insights using various prompt templates. CORES is applied to a VR shop of different items. An empirical study validates the efficiency and accuracy of this approach, achieving a significant average accuracy of 97% and an acceptable response time of 0.3267s in dynamic shopping scenarios. © 2025 by the authors.
Gatti, E.; Giunchi, D.; Numan, N.; Steed, A.
Around the Virtual Campfire: Early UX Insights into AI-Generated Stories in VR Proceedings Article
In: Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR, pp. 136–141, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833152157-8 (ISBN).
Abstract | Links | BibTeX | Tags: Generative AI, Images synthesis, Immersive, Interactive Environments, Language Model, Large language model, Storytelling, User input, User study, Users' experiences, Virtual environments, VR
@inproceedings{gatti_around_2025,
title = {Around the Virtual Campfire: Early UX Insights into AI-Generated Stories in VR},
author = {E. Gatti and D. Giunchi and N. Numan and A. Steed},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000263662&doi=10.1109%2fAIxVR63409.2025.00027&partnerID=40&md5=cd804d892d45554e936d0221508b3447},
doi = {10.1109/AIxVR63409.2025.00027},
isbn = {979-833152157-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},
pages = {136–141},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Virtual Reality (VR) presents an immersive platform for storytelling, allowing narratives to unfold in highly engaging, interactive environments. Leveraging AI capabilities and image synthesis offers new possibilities for creating scalable, generative VR content. In this work, we use an LLM-driven VR storytelling platform to explore how AI-generated visuals and narrative elements impact the user experience in VR storytelling. Previously, we presented AIsop, a system to integrate LLM-generated text and images and TTS audio into a storytelling experience, where the narrative unfolds based on user input. In this paper, we present two user studies focusing on how AI-generated visuals influence narrative perception and the overall VR experience. Our findings highlight the positive impact of AI-generated pictorial content on the storytelling experience, highlighting areas for enhancement and further research in interactive narrative design. © 2025 IEEE.},
keywords = {Generative AI, Images synthesis, Immersive, Interactive Environments, Language Model, Large language model, Storytelling, User input, User study, Users' experiences, Virtual environments, VR},
pubstate = {published},
tppubtype = {inproceedings}
}
Virtual Reality (VR) presents an immersive platform for storytelling, allowing narratives to unfold in highly engaging, interactive environments. Leveraging AI capabilities and image synthesis offers new possibilities for creating scalable, generative VR content. In this work, we use an LLM-driven VR storytelling platform to explore how AI-generated visuals and narrative elements impact the user experience in VR storytelling. Previously, we presented AIsop, a system to integrate LLM-generated text and images and TTS audio into a storytelling experience, where the narrative unfolds based on user input. In this paper, we present two user studies focusing on how AI-generated visuals influence narrative perception and the overall VR experience. Our findings highlight the positive impact of AI-generated pictorial content on the storytelling experience, highlighting areas for enhancement and further research in interactive narrative design. © 2025 IEEE.
Guo, P.; Zhang, Q.; Tian, C.; Xue, W.; Feng, X.
Digital Human Techniques for Education Reform Proceedings Article
In: ICETM - Proc. Int. Conf. Educ. Technol. Manag., pp. 173–178, Association for Computing Machinery, Inc, 2025, ISBN: 979-840071746-8 (ISBN).
Abstract | Links | BibTeX | Tags: Augmented Reality, Contrastive Learning, Digital elevation model, Digital human technique, Digital Human Techniques, Digital humans, Education Reform, Education reforms, Educational Technology, Express emotions, Federated learning, Human behaviors, Human form models, Human techniques, Immersive, Innovative technology, Modeling languages, Natural language processing systems, Teachers', Teaching, Virtual environments, Virtual humans
@inproceedings{guo_digital_2025,
title = {Digital Human Techniques for Education Reform},
author = {P. Guo and Q. Zhang and C. Tian and W. Xue and X. Feng},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001671326&doi=10.1145%2f3711403.3711428&partnerID=40&md5=dd96647315af9409d119f68f9cf4e980},
doi = {10.1145/3711403.3711428},
isbn = {979-840071746-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {ICETM - Proc. Int. Conf. Educ. Technol. Manag.},
pages = {173–178},
publisher = {Association for Computing Machinery, Inc},
abstract = {The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).},
keywords = {Augmented Reality, Contrastive Learning, Digital elevation model, Digital human technique, Digital Human Techniques, Digital humans, Education Reform, Education reforms, Educational Technology, Express emotions, Federated learning, Human behaviors, Human form models, Human techniques, Immersive, Innovative technology, Modeling languages, Natural language processing systems, Teachers', Teaching, Virtual environments, Virtual humans},
pubstate = {published},
tppubtype = {inproceedings}
}
The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).
Oliveira, E. A. Masasi De; Sousa, R. T.; Bastos, A. A.; Cintra, L. Martins De Freitas; Filho, A. R. G.
Immersive Virtual Museums with Spatially-Aware Retrieval-Augmented Generation Proceedings Article
In: IMX - Proc. ACM Int. Conf. Interact. Media Experiences, pp. 437–440, Association for Computing Machinery, Inc, 2025, ISBN: 979-840071391-0 (ISBN).
Abstract | Links | BibTeX | Tags: Association reactions, Behavioral Research, Generation systems, Geographics, Human computer interaction, Human engineering, Immersive, Information Retrieval, Interactive computer graphics, Language Model, Large language model, large language models, Museums, Retrieval-Augmented Generation, Search engines, Spatially aware, User interfaces, Virtual environments, Virtual museum, Virtual museum., Virtual Reality, Visual Attention, Visual languages
@inproceedings{masasi_de_oliveira_immersive_2025,
title = {Immersive Virtual Museums with Spatially-Aware Retrieval-Augmented Generation},
author = {E. A. Masasi De Oliveira and R. T. Sousa and A. A. Bastos and L. Martins De Freitas Cintra and A. R. G. Filho},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007979183&doi=10.1145%2f3706370.3731643&partnerID=40&md5=db10b41217dd8a0b0705c3fb4a615666},
doi = {10.1145/3706370.3731643},
isbn = {979-840071391-0 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {IMX - Proc. ACM Int. Conf. Interact. Media Experiences},
pages = {437–440},
publisher = {Association for Computing Machinery, Inc},
abstract = {Virtual Reality has significantly expanded possibilities for immersive museum experiences, overcoming traditional constraints such as space, preservation, and geographic limitations. However, existing virtual museum platforms typically lack dynamic, personalized, and contextually accurate interactions. To address this, we propose Spatially-Aware Retrieval-Augmented Generation (SA-RAG), an innovative framework integrating visual attention tracking with Retrieval-Augmented Generation systems and advanced Large Language Models. By capturing users' visual attention in real time, SA-RAG dynamically retrieves contextually relevant data, enhancing the accuracy, personalization, and depth of user interactions within immersive virtual environments. The system's effectiveness is initially demonstrated through our preliminary tests within a realistic VR museum implemented using Unreal Engine. Although promising, comprehensive human evaluations involving broader user groups are planned for future studies to rigorously validate SA-RAG's effectiveness, educational enrichment potential, and accessibility improvements in virtual museums. The framework also presents opportunities for broader applications in immersive educational and storytelling domains. © 2025 Copyright held by the owner/author(s).},
keywords = {Association reactions, Behavioral Research, Generation systems, Geographics, Human computer interaction, Human engineering, Immersive, Information Retrieval, Interactive computer graphics, Language Model, Large language model, large language models, Museums, Retrieval-Augmented Generation, Search engines, Spatially aware, User interfaces, Virtual environments, Virtual museum, Virtual museum., Virtual Reality, Visual Attention, Visual languages},
pubstate = {published},
tppubtype = {inproceedings}
}
Virtual Reality has significantly expanded possibilities for immersive museum experiences, overcoming traditional constraints such as space, preservation, and geographic limitations. However, existing virtual museum platforms typically lack dynamic, personalized, and contextually accurate interactions. To address this, we propose Spatially-Aware Retrieval-Augmented Generation (SA-RAG), an innovative framework integrating visual attention tracking with Retrieval-Augmented Generation systems and advanced Large Language Models. By capturing users' visual attention in real time, SA-RAG dynamically retrieves contextually relevant data, enhancing the accuracy, personalization, and depth of user interactions within immersive virtual environments. The system's effectiveness is initially demonstrated through our preliminary tests within a realistic VR museum implemented using Unreal Engine. Although promising, comprehensive human evaluations involving broader user groups are planned for future studies to rigorously validate SA-RAG's effectiveness, educational enrichment potential, and accessibility improvements in virtual museums. The framework also presents opportunities for broader applications in immersive educational and storytelling domains. © 2025 Copyright held by the owner/author(s).
Casas, L.; Mitchell, K.
Structured Teaching Prompt Articulation for Generative-AI Role Embodiment with Augmented Mirror Video Displays Proceedings Article
In: S.N., Spencer (Ed.): Proc.: VRCAI - ACM SIGGRAPH Int. Conf. Virtual-Reality Contin. Appl. Ind., Association for Computing Machinery, Inc, 2025, ISBN: 979-840071348-4 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Augmented Reality, Computer interaction, Contrastive Learning, Cultural icon, Experiential learning, Generative adversarial networks, Generative AI, human-computer interaction, Immersive, Pedagogical practices, Role-based, Teachers', Teaching, Video display, Virtual environments, Virtual Reality
@inproceedings{casas_structured_2025,
title = {Structured Teaching Prompt Articulation for Generative-AI Role Embodiment with Augmented Mirror Video Displays},
author = {L. Casas and K. Mitchell},
editor = {Spencer S.N.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217997060&doi=10.1145%2f3703619.3706049&partnerID=40&md5=7141c5dac7882232c6ee8e0bef0ba84e},
doi = {10.1145/3703619.3706049},
isbn = {979-840071348-4 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc.: VRCAI - ACM SIGGRAPH Int. Conf. Virtual-Reality Contin. Appl. Ind.},
publisher = {Association for Computing Machinery, Inc},
abstract = {We present a classroom enhanced with augmented reality video display in which students adopt snapshots of their corresponding virtual personas according to their teacher's live articulated spoken educational theme, linearly, such as historical figures, famous scientists, cultural icons, and laterally according to archetypal categories such as world dance styles. We define a structure of generative AI prompt guidance to assist teachers with focused specified visual role embodiment stylization. By leveraging role-based immersive embodiment, our proposed approach enriches pedagogical practices that prioritize experiential learning. © 2024 ACM.},
keywords = {Artificial intelligence, Augmented Reality, Computer interaction, Contrastive Learning, Cultural icon, Experiential learning, Generative adversarial networks, Generative AI, human-computer interaction, Immersive, Pedagogical practices, Role-based, Teachers', Teaching, Video display, Virtual environments, Virtual Reality},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a classroom enhanced with augmented reality video display in which students adopt snapshots of their corresponding virtual personas according to their teacher's live articulated spoken educational theme, linearly, such as historical figures, famous scientists, cultural icons, and laterally according to archetypal categories such as world dance styles. We define a structure of generative AI prompt guidance to assist teachers with focused specified visual role embodiment stylization. By leveraging role-based immersive embodiment, our proposed approach enriches pedagogical practices that prioritize experiential learning. © 2024 ACM.
Aloudat, M. Z.; Aboumadi, A.; Soliman, A.; Al-Mohammed, H. A.; Al-Ali, M.; Mahgoub, A.; Barhamgi, M.; Yaacoub, E.
Metaverse Unbound: A Survey on Synergistic Integration Between Semantic Communication, 6G, and Edge Learning Journal Article
In: IEEE Access, vol. 13, pp. 58302–58350, 2025, ISSN: 21693536 (ISSN).
Abstract | Links | BibTeX | Tags: 6g wireless system, 6G wireless systems, Augmented Reality, Block-chain, Blockchain, Blockchain technology, Digital Twin Technology, Edge learning, Extended reality (XR), Language Model, Large language model, large language models (LLMs), Metaverse, Metaverses, Semantic communication, Virtual environments, Wireless systems
@article{aloudat_metaverse_2025,
title = {Metaverse Unbound: A Survey on Synergistic Integration Between Semantic Communication, 6G, and Edge Learning},
author = {M. Z. Aloudat and A. Aboumadi and A. Soliman and H. A. Al-Mohammed and M. Al-Ali and A. Mahgoub and M. Barhamgi and E. Yaacoub},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003088610&doi=10.1109%2fACCESS.2025.3555753&partnerID=40&md5=8f3f9421ce2d6be57f8154a122ee192c},
doi = {10.1109/ACCESS.2025.3555753},
issn = {21693536 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Access},
volume = {13},
pages = {58302–58350},
abstract = {With a focus on edge learning, blockchain, sixth generation (6G) wireless systems, semantic communication, and large language models (LLMs), this survey paper examines the revolutionary integration of cutting-edge technologies within the metaverse. This thorough examination highlights the critical role these technologies play in improving realism and user engagement on three main levels: technical, virtual, and physical. While the virtual layer focuses on building immersive experiences, the physical layer highlights improvements to the user interface through augmented reality (AR) goggles and virtual reality (VR) headsets. Blockchain-powered technical layer enables safe, decentralized communication. The survey highlights how the metaverse has the potential to drastically change how people interact in society by exploring applications in a variety of fields, such as immersive education, remote work, and entertainment. Concerns about privacy, scalability, and interoperability are raised, highlighting the necessity of continued study to realize the full potential of the metaverse. For scholars looking to broaden the reach and significance of the metaverse in the digital age, this paper is a useful tool. © 2013 IEEE.},
keywords = {6g wireless system, 6G wireless systems, Augmented Reality, Block-chain, Blockchain, Blockchain technology, Digital Twin Technology, Edge learning, Extended reality (XR), Language Model, Large language model, large language models (LLMs), Metaverse, Metaverses, Semantic communication, Virtual environments, Wireless systems},
pubstate = {published},
tppubtype = {article}
}
With a focus on edge learning, blockchain, sixth generation (6G) wireless systems, semantic communication, and large language models (LLMs), this survey paper examines the revolutionary integration of cutting-edge technologies within the metaverse. This thorough examination highlights the critical role these technologies play in improving realism and user engagement on three main levels: technical, virtual, and physical. While the virtual layer focuses on building immersive experiences, the physical layer highlights improvements to the user interface through augmented reality (AR) goggles and virtual reality (VR) headsets. Blockchain-powered technical layer enables safe, decentralized communication. The survey highlights how the metaverse has the potential to drastically change how people interact in society by exploring applications in a variety of fields, such as immersive education, remote work, and entertainment. Concerns about privacy, scalability, and interoperability are raised, highlighting the necessity of continued study to realize the full potential of the metaverse. For scholars looking to broaden the reach and significance of the metaverse in the digital age, this paper is a useful tool. © 2013 IEEE.
Häfner, P.; Eisenlohr, F.; Karande, A.; Grethler, M.; Mukherjee, A.; Tran, N.
Leveraging Virtual Prototypes for Training Data Collection in LLM-Based Voice User Interface Development for Machines Proceedings Article
In: Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR, pp. 281–285, Institute of Electrical and Electronics Engineers Inc., 2025, ISBN: 979-833152157-8 (ISBN).
Abstract | Links | BibTeX | Tags: Artificial intelligence, Behavioral Research, Data collection, Language Model, Large language model, large language models, Model-based OPC, Training data, User interface development, Virtual environments, Virtual Prototype, Virtual Prototyping, Virtual Reality, Voice User Interface, Voice User Interfaces, Wizard of Oz, Wizard-of-Oz Method
@inproceedings{hafner_leveraging_2025,
title = {Leveraging Virtual Prototypes for Training Data Collection in LLM-Based Voice User Interface Development for Machines},
author = {P. Häfner and F. Eisenlohr and A. Karande and M. Grethler and A. Mukherjee and N. Tran},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000344182&doi=10.1109%2fAIxVR63409.2025.00054&partnerID=40&md5=05fe014eddba395881575bec5d96ce15},
doi = {10.1109/AIxVR63409.2025.00054},
isbn = {979-833152157-8 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {Proc. - IEEE Int. Conf. Artif. Intell. Ext. Virtual Real., AIxVR},
pages = {281–285},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Voice User Interfaces (VUIs) are becoming increasingly valuable in industrial applications, offering hands-free control in complex environments. However, developing and validating VUIs for such applications faces challenges, including limited access to physical prototypes and high testing costs. This paper presents a methodology that utilizes virtual reality (VR) prototypes to collect training data for large language model (LLM)-based VUIs, allowing early-stage voice control development before physical prototypes are accessible. Through an immersive Wizard-of-Oz (WoZ) method, participants interact with a virtual reality representation of a machine, generating realistic, scenario-based conversational data. This combined WoZ and VR approach enables high-quality data collection and iterative model training, offering an effective solution that can be applied across various types of machine. Preliminary findings demonstrate the viability of VR in generating diverse and robust data sets that closely simulate real-world dialogs for voice interactions in industrial settings. © 2025 IEEE.},
keywords = {Artificial intelligence, Behavioral Research, Data collection, Language Model, Large language model, large language models, Model-based OPC, Training data, User interface development, Virtual environments, Virtual Prototype, Virtual Prototyping, Virtual Reality, Voice User Interface, Voice User Interfaces, Wizard of Oz, Wizard-of-Oz Method},
pubstate = {published},
tppubtype = {inproceedings}
}
Voice User Interfaces (VUIs) are becoming increasingly valuable in industrial applications, offering hands-free control in complex environments. However, developing and validating VUIs for such applications faces challenges, including limited access to physical prototypes and high testing costs. This paper presents a methodology that utilizes virtual reality (VR) prototypes to collect training data for large language model (LLM)-based VUIs, allowing early-stage voice control development before physical prototypes are accessible. Through an immersive Wizard-of-Oz (WoZ) method, participants interact with a virtual reality representation of a machine, generating realistic, scenario-based conversational data. This combined WoZ and VR approach enables high-quality data collection and iterative model training, offering an effective solution that can be applied across various types of machine. Preliminary findings demonstrate the viability of VR in generating diverse and robust data sets that closely simulate real-world dialogs for voice interactions in industrial settings. © 2025 IEEE.