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Exploring the Potential of Artificial Intelligence and Computing Technologies in Art Museums

Authors :
Rani Sunanda
Jining Dong
Shah Dhaneshwar
Xaba Siyanda
Singh Prabhat Ranjan
Source :
ITM Web of Conferences, Vol 53, p 01004 (2023)
Publication Year :
2023
Publisher :
EDP Sciences, 2023.

Abstract

The research intends to explore how Artificial Intelligence (AI) and computing technology can be used to create a more immersive and enjoyable experience within the context of a museum visit. Specifically, the study aims to identify ways in which AI and computing technologies can be leveraged to enrich the visitor’s experience, including by providing interactive content, automated personalization, and real-time access to relevant information. Additionally, the research will assess the potential for AI and computing technology to support improved data analytics and utilization of resources within museums, such as enhanced curation, digital preservation, and increased engagement with audiences. The study employed a qualitative methodology, utilizing interviews with museum professionals and surveys of museum visitors to collect data on visitor experiences. An analysis of the data was conducted to identify current and potential uses of AI and computing technology in art museums. The findings reveal that AI and computing technology are currently being used to facilitate access to collections, tour guidance, and educational activities while emerging technologies show promise for providing even more immersive and personalized visitor experiences. The results of this study suggest that AI and computing technology can play an important role in enhancing the visitor’s museum experience. The research provides recommendations for art museums to leverage AI and computing technology to optimize visitor engagement and foster more meaningful connections with works of art.

Subjects

Subjects :
Information technology
T58.5-58.64

Details

Language :
English
ISSN :
22712097
Volume :
53
Database :
Directory of Open Access Journals
Journal :
ITM Web of Conferences
Publication Type :
Academic Journal
Accession number :
edsdoj.42cf0d987f7f4543ab8189920ac9a43c
Document Type :
article
Full Text :
https://doi.org/10.1051/itmconf/20235301004