A systematic review of changing conceptual to practice AI curation in museums: Text mining and bibliometric analysis

Abstract: The rapid development of artificial intelligence (AI) algorithms has accelerated the global digitization of museums. This study was conducted to clarify conceptual change to practice by applying a systematic literature review to a combination text mining and bibliometric analysis technique to visualization network. Based on the study selection articles from Web of Science（WOS). Our research questions focused on revealing the interconnected network of digital museum collections, expert knowledge and algorithms, and recommendation systems. The findings showed that 288 articles were finally selected to be analyzed.Conceptualizing AI curation in museums is currently underwayincombining AI with museum curatorial knowledge and innovate the practice mode of public participation in museum AI curation With emphasis on the the exchange of domain knowledge process. Moreover, three dimensions to consider including (1)design dimension focus on Methods and approaches for curating museum artificial intelligence exhibitions, (2) learning dimension focus on iterative development of new algorithm models guides the practice of intelligent curation,and (3) standard dimension focus on assessment and evaluation inpublic participation in curating museum cultural heritage exhibitions. In addition, the museum and AI community will mutually benefit. In particular, the convergence of new technologies and the exchange of domain knowledge would result in fairer and safer applications in the future as a result of learning from one another's flaws.

Keywords: AI curation, text mining, museum curation, Curation Principles

DOI: 10.54941/ahfe1004668

Cite this paper:

​