AHCI RESEARCH GROUP

Publications

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

OUR RESEARCH

Scientific Publications

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2025

Ariya, P.; Wongwan, N.; Intawong, K.; Puritat, K.

Assessing learning outcomes in immersive virtual reality with and without generative AI-Powered virtual npcs: a comparative analysis in a museum context Journal Article

In: Education and Information Technologies, 2025, ISSN: 13602357 (ISSN), (Publisher: Springer).

Abstract | Links | BibTeX | Tags: Generative AI, Immersive virtual reality, Interaction behaviors, Museum education, Virtual museums

@article{ariya_assessing_2025,

title = {Assessing learning outcomes in immersive virtual reality with and without generative AI-Powered virtual npcs: a comparative analysis in a museum context},

author = {P. Ariya and N. Wongwan and K. Intawong and K. Puritat},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105009622006&doi=10.1007%2Fs10639-025-13682-7&partnerID=40&md5=048e70be6ea3fe5d233f72f66fab7352},

doi = {10.1007/s10639-025-13682-7},

issn = {13602357 (ISSN)},

year  = {2025},

date = {2025-01-01},

journal = {Education and Information Technologies},

abstract = {This study examines the impact of generative AI-powered virtual assistants on learning outcomes, user experiences, and interaction behaviors in virtual reality (VR) environments within a museum context. Using a comparative experimental design, the research evaluates two participant groups: one experiencing a VR museum with AI-based virtual assistants and another engaging with a non-AI VR environment. User experience was assessed using the User Experience Scale questionnaire, knowledge acquisition was measured through pre- and post-test evaluations, and open-ended questions captured qualitative feedback. Both groups demonstrated significant improvements in knowledge acquisition, though the integration of AI did not yield statistically significant differences compared to the non-AI group. The AI-assisted group exhibited higher levels of engagement, spending more time exploring exhibits and interacting within the virtual environment. However, participants in the AI-assisted group rated lower in aesthetics and satisfaction, highlighting response delays and inconsistencies as key challenges. These findings underscore both the potential and limitations of integrating AI-based virtual assistants in educational and cultural settings, offering actionable insights for enhancing AI-driven learning experiences in VR while addressing its challenges in educational applications. © 2025 Elsevier B.V., All rights reserved.},

note = {Publisher: Springer},

keywords = {Generative AI, Immersive virtual reality, Interaction behaviors, Museum education, Virtual museums},

pubstate = {published},

tppubtype = {article}

}

2024

Vasic, I.; Fill, H. -G.; Quattrini, R.; Pierdicca, R.

LLM-Aided Museum Guide: Personalized Tours Based on User Preferences Proceedings Article

In: L.T., De Paolis; P., Arpaia; M., Sacco (Ed.): Lect. Notes Comput. Sci., pp. 249–262, Springer Science and Business Media Deutschland GmbH, 2024, ISBN: 03029743 (ISSN); 978-303171709-3 (ISBN).

Abstract | Links | BibTeX | Tags: Artificial intelligence techniques, Automated process, Cultural heritages, Extended reality, Language Model, Large language model, large language models, Modeling languages, Museum guide, User's preferences, Virtual environments, Virtual museum, Virtual museums, Virtual tour

@inproceedings{vasic_llm-aided_2024,

title = {LLM-Aided Museum Guide: Personalized Tours Based on User Preferences},

author = {I. Vasic and H. -G. Fill and R. Quattrini and R. Pierdicca},

editor = {De Paolis L.T. and Arpaia P. and Sacco M.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205127699&doi=10.1007%2f978-3-031-71710-9_18&partnerID=40&md5=fba73e38a432e0749b8e79197ef85310},

doi = {10.1007/978-3-031-71710-9_18},

isbn = {03029743 (ISSN); 978-303171709-3 (ISBN)},

year  = {2024},

date = {2024-01-01},

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

volume = {15029 LNCS},

pages = {249–262},

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

abstract = {The quick development of generative artificial intelligence (GenAI) techniques is a promising step toward automated processes in the field of cultural heritage (CH). The recent rise of powerful Large Language Models (LLMs) like ChatGPT has made them a commonly utilized tool for a wide range of tasks across various fields. In this paper, we introduce LLMs as a guide in the three-dimensional (3D) panoramic virtual tour of the Civic Art Gallery of Ascoli to enable visitors to express their interest and show them the requested content. The input to our algorithm is a user request in natural language. The processing tasks are performed with the OpenAI’s Generative Pre-trained Transformer (GPT) 4o model. Requests are handled through the OpenAI’s API. We demonstrate all the functionalities within a developed local web-based application. This novel approach is capable of solving the problem of generic guided tours in the museum and offers a solution for the more automatized and personalized ones. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},

keywords = {Artificial intelligence techniques, Automated process, Cultural heritages, Extended reality, Language Model, Large language model, large language models, Modeling languages, Museum guide, User's preferences, Virtual environments, Virtual museum, Virtual museums, Virtual tour},

pubstate = {published},

tppubtype = {inproceedings}

}