AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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2025
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}
}
2024
Gujar, P.; Paliwal, G.; Panyam, S.
Generative AI and the Future of Interactive and Immersive Advertising Proceedings Article
In: D., Rivas-Lalaleo; S.L.S., Maita (Ed.): ETCM - Ecuador Tech. Chapters Meet., Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835039158-9 (ISBN).
Abstract | Links | BibTeX | Tags: Ad Creation, Adversarial machine learning, Advertising Technology (AdTech), Advertizing, Advertizing technology, Augmented Reality, Augmented Reality (AR), Generative adversarial networks, Generative AI, Immersive, Immersive Advertising, Immersive advertizing, Interactive Advertising, Interactive advertizing, machine learning, Machine-learning, Marketing, Mixed reality, Mixed Reality (MR), Personalization, Personalizations, User Engagement, Virtual environments, Virtual Reality, Virtual Reality (VR)
@inproceedings{gujar_generative_2024,
title = {Generative AI and the Future of Interactive and Immersive Advertising},
author = {P. Gujar and G. Paliwal and S. Panyam},
editor = {Rivas-Lalaleo D. and Maita S.L.S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211805262&doi=10.1109%2fETCM63562.2024.10746166&partnerID=40&md5=179c5ceeb28ed72e809748322535c7ad},
doi = {10.1109/ETCM63562.2024.10746166},
isbn = {979-835039158-9 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {ETCM - Ecuador Tech. Chapters Meet.},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Generative AI is revolutionizing interactive and immersive advertising by enabling more personalized, engaging experiences through advanced technologies like VR, AR, and MR. This transformation is reshaping how advertisers create, deliver, and optimize content, allowing for two-way communication and blurring lines between digital and physical worlds. AI enhances user engagement through predictive analytics, real-time adaptation, and natural language processing, while also optimizing ad placement and personalization. Future trends include integration with emerging technologies like 5G and IoT, fully immersive experiences, and hyper-personalization. However, challenges such as privacy concerns, transparency issues, and ethical considerations must be addressed. As AI continues to evolve, it promises to create unprecedented opportunities for brands to connect with audiences in meaningful ways, potentially blurring the line between advertising and interactive entertainment. The industry must proactively address these challenges to ensure AI-driven advertising enhances user experiences while respecting privacy and maintaining trust. © 2024 IEEE.},
keywords = {Ad Creation, Adversarial machine learning, Advertising Technology (AdTech), Advertizing, Advertizing technology, Augmented Reality, Augmented Reality (AR), Generative adversarial networks, Generative AI, Immersive, Immersive Advertising, Immersive advertizing, Interactive Advertising, Interactive advertizing, machine learning, Machine-learning, Marketing, Mixed reality, Mixed Reality (MR), Personalization, Personalizations, User Engagement, Virtual environments, Virtual Reality, Virtual Reality (VR)},
pubstate = {published},
tppubtype = {inproceedings}
}
Venkatachalam, N.; Rayana, M.; Vignesh, S. Bala; Prathamesh, S.
Voice-Driven Panoramic Imagery: Real-Time Generative AI for Immersive Experiences Proceedings Article
In: Int. Conf. Intell. Data Commun. Technol. Internet Things, IDCIoT, pp. 1133–1138, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835032753-3 (ISBN).
Abstract | Links | BibTeX | Tags: Adaptive Visual Experience, First person, First-Person view, generative artificial intelligence, Generative Artificial Intelligence (AI), Image processing, Immersive, Immersive visual scene, Immersive Visual Scenes, Language processing, Natural Language Processing, Natural Language Processing (NLP), Natural language processing systems, Natural languages, Panoramic Images, Patient treatment, Personalized environment, Personalized Environments, Phobia Treatment, Prompt, prompts, Psychological intervention, Psychological Interventions, Real-Time Synthesis, User interaction, User interfaces, Virtual experience, Virtual Experiences, Virtual Reality, Virtual Reality (VR), Virtual-reality headsets, Visual experiences, Visual languages, Visual scene, Voice command, Voice commands, VR Headsets
@inproceedings{venkatachalam_voice-driven_2024,
title = {Voice-Driven Panoramic Imagery: Real-Time Generative AI for Immersive Experiences},
author = {N. Venkatachalam and M. Rayana and S. Bala Vignesh and S. Prathamesh},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85190121845&doi=10.1109%2fIDCIoT59759.2024.10467441&partnerID=40&md5=6594fbab013d9156b79a887f0d7209cb},
doi = {10.1109/IDCIoT59759.2024.10467441},
isbn = {979-835032753-3 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Int. Conf. Intell. Data Commun. Technol. Internet Things, IDCIoT},
pages = {1133–1138},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {This research study introduces an innovative system that aims to synthesize 360-degree panoramic images in Realtime based on vocal prompts from the user, leveraging state-of-The-Art Generative AI with a combination of advanced NLP models. The primary objective of this system is to transform spoken descriptions into immersive and interactive visual scenes, specifically designed to provide users with first-person field views. This cutting-edge technology has the potential to revolutionize the realm of virtual reality (VR) experiences, enabling users to effortlessly create and navigate through personalized environments. The fundamental goal of this system is to enable the generation of real-Time images that are seamlessly compatible with VR headsets, offering a truly immersive and adaptive visual experience. Beyond its technological advancements, this research also highlights its significant potential for creating a positive social impact. One notable application lies in psychological interventions, particularly in the context of phobia treatment and therapeutic settings. Here, patients can safely confront and work through their fears within these synthesized environments, potentially offering new avenues for therapy. Furthermore, the system serves educational and entertainment purposes by bringing users' imaginations to life, providing an unparalleled platform for exploring the boundaries of virtual experiences. Overall, this research represents a promising stride towards a more immersive and adaptable future in VR technology, with the potential to enhance various aspects of human lives, from mental health treatment to entertainment and education. © 2024 IEEE.},
keywords = {Adaptive Visual Experience, First person, First-Person view, generative artificial intelligence, Generative Artificial Intelligence (AI), Image processing, Immersive, Immersive visual scene, Immersive Visual Scenes, Language processing, Natural Language Processing, Natural Language Processing (NLP), Natural language processing systems, Natural languages, Panoramic Images, Patient treatment, Personalized environment, Personalized Environments, Phobia Treatment, Prompt, prompts, Psychological intervention, Psychological Interventions, Real-Time Synthesis, User interaction, User interfaces, Virtual experience, Virtual Experiences, Virtual Reality, Virtual Reality (VR), Virtual-reality headsets, Visual experiences, Visual languages, Visual scene, Voice command, Voice commands, VR Headsets},
pubstate = {published},
tppubtype = {inproceedings}
}
Greca, A. D.; Amaro, I.; Barra, P.; Rosapepe, E.; Tortora, G.
Enhancing therapeutic engagement in Mental Health through Virtual Reality and Generative AI: A co-creation approach to trust building Proceedings Article
In: M., Cannataro; H., Zheng; L., Gao; J., Cheng; J.L., Miranda; E., Zumpano; X., Hu; Y.-R., Cho; T., Park (Ed.): Proc. - IEEE Int. Conf. Bioinform. Biomed., BIBM, pp. 6805–6811, Institute of Electrical and Electronics Engineers Inc., 2024, ISBN: 979-835038622-6 (ISBN).
Abstract | Links | BibTeX | Tags: Co-creation, Electronic health record, Fundamental component, Generative adversarial networks, Generative AI, generative artificial intelligence, Immersive, Mental health, Personalized therapies, Personalized Therapy, Three-dimensional object, Trust, Trust building, Virtual environments, Virtual Reality, Virtual Reality (VR)
@inproceedings{greca_enhancing_2024,
title = {Enhancing therapeutic engagement in Mental Health through Virtual Reality and Generative AI: A co-creation approach to trust building},
author = {A. D. Greca and I. Amaro and P. Barra and E. Rosapepe and G. Tortora},
editor = {Cannataro M. and Zheng H. and Gao L. and Cheng J. and Miranda J.L. and Zumpano E. and Hu X. and Cho Y.-R. and Park T.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217278235&doi=10.1109%2fBIBM62325.2024.10822177&partnerID=40&md5=ed42f7ca6a0e52e9945402e2c439a7f0},
doi = {10.1109/BIBM62325.2024.10822177},
isbn = {979-835038622-6 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {Proc. - IEEE Int. Conf. Bioinform. Biomed., BIBM},
pages = {6805–6811},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Trust is a fundamental component of effective therapeutic relationships, significantly influencing patient engagement and treatment outcomes in mental health care. This paper presents a preliminary study aimed at enhancing trust through the co-creation of virtual therapeutic environments using generative artificial intelligence (AI). We propose a multimodal AI model, integrated into a virtual reality (VR) platform developed in Unity, which generates three-dimensional (3D) objects from textual descriptions. This approach allows patients to actively participate in shaping their therapeutic environment, fostering a collaborative atmosphere that enhances trust between patients and therapists. The methodology is structured into four phases, combining non-immersive and immersive experiences to co-create personalized therapeutic spaces and 3D objects symbolizing emotional or psychological states. Preliminary results demonstrate the system's potential in improving the therapeutic process through the real-time creation of virtual objects that reflect patient needs, with high-quality mesh generation and semantic coherence. This work offers new possibilities for patient-centered care in mental health services, suggesting that virtual co-creation can improve therapeutic efficacy by promoting trust and emotional engagement. © 2024 IEEE.},
keywords = {Co-creation, Electronic health record, Fundamental component, Generative adversarial networks, Generative AI, generative artificial intelligence, Immersive, Mental health, Personalized therapies, Personalized Therapy, Three-dimensional object, Trust, Trust building, Virtual environments, Virtual Reality, Virtual Reality (VR)},
pubstate = {published},
tppubtype = {inproceedings}
}
Song, Y.; Wu, K.; Ding, J.
In: Computers and Education: X Reality, vol. 4, 2024, ISSN: 29496780 (ISSN).
Abstract | Links | BibTeX | Tags: Game-based learning, Generative AI, Immersion, Interaction, Virtual Reality (VR)
@article{song_developing_2024,
title = {Developing an immersive game-based learning platform with generative artificial intelligence and virtual reality technologies – “LearningverseVR”},
author = {Y. Song and K. Wu and J. Ding},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205973323&doi=10.1016%2fj.cexr.2024.100069&partnerID=40&md5=91dd3ac3d01b4730f923f8541d5877f2},
doi = {10.1016/j.cexr.2024.100069},
issn = {29496780 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Computers and Education: X Reality},
volume = {4},
abstract = {The rapid evolution of generative artificial intelligence (AI) and virtual reality (VR) technologies are revolutionising various fields, including education and gaming industries. However, studies on how to enhance immersive game-based learning with AI and VR technologies remain scant. Given this, the article presents the creation of “LearningverseVR,” an immersive game-based learning platform developed using generative AI and VR technologies, which is based on “Learningverse,” a metaverse platform developed by the lead author and her research team. The “LearningverseVR” platform uses Unity as the client and Python, Flask and MySQL as the backend. Unity's multiplayer service provides multiplayer online functionality, supporting learners to engage in immersive and interactive learning activities. The design framework of the platform consists of two main components: Game-based learning with generative AI and immersion with VR technologies. First, generative AI is used to create NPCs with diverse personalities and life backgrounds, and enable learners to interact with NPCs without scripted dialogues, creating an interactive and immersive game-based learning environment. Secondly, such a learning experience is enhanced by leveraging the Large Language Model (LLM) ecosystem with VR technology. The creation of the “LearningverseVR” platform provides novel perspectives on digital game-based learning. © 2024 The Authors},
keywords = {Game-based learning, Generative AI, Immersion, Interaction, Virtual Reality (VR)},
pubstate = {published},
tppubtype = {article}
}
de Oliveira, E. A. Masasi; Silva, D. F. C.; Filho, A. R. G.
Improving VR Accessibility Through Automatic 360 Scene Description Using Multimodal Large Language Models Proceedings Article
In: ACM Int. Conf. Proc. Ser., pp. 289–293, Association for Computing Machinery, 2024, ISBN: 979-840070979-1 (ISBN).
Abstract | Links | BibTeX | Tags: 3D Scene, 3D scenes, Accessibility, Computer simulation languages, Descriptive information, Digital elevation model, Immersive, Language Model, Multi-modal, Multimodal large language model, Multimodal Large Language Models (MLLMs), Scene description, Virtual environments, Virtual Reality, Virtual Reality (VR), Virtual reality technology
@inproceedings{masasi_de_oliveira_improving_2024,
title = {Improving VR Accessibility Through Automatic 360 Scene Description Using Multimodal Large Language Models},
author = {E. A. Masasi de Oliveira and D. F. C. Silva and A. R. G. Filho},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206580797&doi=10.1145%2f3691573.3691619&partnerID=40&md5=6e80800fce0e6b56679fbcbe982bcfa7},
doi = {10.1145/3691573.3691619},
isbn = {979-840070979-1 (ISBN)},
year = {2024},
date = {2024-01-01},
booktitle = {ACM Int. Conf. Proc. Ser.},
pages = {289–293},
publisher = {Association for Computing Machinery},
abstract = {Advancements in Virtual Reality (VR) technology hold immense promise for enriching immersive experiences. Despite the advancements in VR technology, there remains a significant gap in addressing accessibility concerns, particularly in automatically providing descriptive information for VR scenes. This paper combines the potential of leveraging Multimodal Large Language Models (MLLMs) to automatically generate text descriptions for 360 VR scenes according to Speech-to-Text (STT) prompts. As a case study, we conduct experiments on educational settings in VR museums, improving dynamic experiences across various contexts. Despite minor challenges in adapting MLLMs to VR Scenes, the experiments demonstrate that they can generate descriptions with high quality. Our findings provide insights for enhancing VR experiences and ensuring accessibility to individuals with disabilities or diverse needs. © 2024 Copyright held by the owner/author(s).},
keywords = {3D Scene, 3D scenes, Accessibility, Computer simulation languages, Descriptive information, Digital elevation model, Immersive, Language Model, Multi-modal, Multimodal large language model, Multimodal Large Language Models (MLLMs), Scene description, Virtual environments, Virtual Reality, Virtual Reality (VR), Virtual reality technology},
pubstate = {published},
tppubtype = {inproceedings}
}