AHCI RESEARCH GROUP

Publications

Papers published in international journals,
proceedings of conferences, workshops and books.

OUR RESEARCH

Scientific Publications

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2025

Lau, K. H. C.; Bozkir, E.; Gao, H.; Kasneci, E.

Evaluating Usability and Engagement of Large Language Models in Virtual Reality for Traditional Scottish Curling Proceedings Article

In: A., Del Bue; C., Canton; J., Pont-Tuset; T., Tommasi (Ed.): Lect. Notes Comput. Sci., pp. 177–195, Springer Science and Business Media Deutschland GmbH, 2025, ISBN: 03029743 (ISSN); 978-303191571-0 (ISBN).

Abstract | Links | BibTeX | Tags: Chatbots, Cultural heritages, Digital Cultural Heritage, Digital cultural heritages, Educational robots, Engineering education, Heritage education, Historic Preservation, Language Model, Large language model, large language models, Learning outcome, Model-based OPC, Usability engineering, User Engagement, Virtual Reality, Virtual-reality environment, Virtualization

@inproceedings{lau_evaluating_2025,

title = {Evaluating Usability and Engagement of Large Language Models in Virtual Reality for Traditional Scottish Curling},

author = {K. H. C. Lau and E. Bozkir and H. Gao and E. Kasneci},

editor = {Del Bue A. and Canton C. and Pont-Tuset J. and Tommasi T.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105006905979&doi=10.1007%2f978-3-031-91572-7_11&partnerID=40&md5=8a81fb09ff54e57b9429660a8898149a},

doi = {10.1007/978-3-031-91572-7_11},

isbn = {03029743 (ISSN); 978-303191571-0 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15628 LNCS},

pages = {177–195},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {This paper explores the innovative application of Large Language Models (LLMs) in Virtual Reality (VR) environments to promote heritage education, focusing on traditional Scottish curling presented in the game “Scottish Bonspiel VR”. Our study compares the effectiveness of LLM-based chatbots with pre-defined scripted chatbots, evaluating key criteria such as usability, user engagement, and learning outcomes. The results show that LLM-based chatbots significantly improve interactivity and engagement, creating a more dynamic and immersive learning environment. This integration helps document and preserve cultural heritage and enhances dissemination processes, which are crucial for safeguarding intangible cultural heritage (ICH) amid environmental changes. Furthermore, the study highlights the potential of novel technologies in education to provide immersive experiences that foster a deeper appreciation of cultural heritage. These findings support the wider application of LLMs and VR in cultural education to address global challenges and promote sustainable practices to preserve and enhance cultural heritage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {Chatbots, Cultural heritages, Digital Cultural Heritage, Digital cultural heritages, Educational robots, Engineering education, Heritage education, Historic Preservation, Language Model, Large language model, large language models, Learning outcome, Model-based OPC, Usability engineering, User Engagement, Virtual Reality, Virtual-reality environment, Virtualization},

pubstate = {published},

tppubtype = {inproceedings}

}

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}

}