Conversational AI in museums: a systematic literature review using the people–process–technology framework

Abstract

The adoption of conversational artificial intelligence (AI) in museums has opened a new opportunity to create a richer visiting experience to tell stories for the preservation of culture. This paper contributes a systematic literature review (SLR) of 33 peer-reviewed papers covering the period from 2020 to mid-2025, by applying the people–process–technology (PPT) model to examine the social technological aspects of AI implementation. This combination is novel in a museum context, as previous research has largely treated these separately. Findings indicate a pronounced shift from text based chatbots (21% or 7 of 33 papers) to more immersive and interactive platforms (30% or 10 of 33 papers), reflecting the transition from the pandemic (2020 – 2023) to the post-pandemic period (2024 – 2025). Besides the evolution of these technologies, the technology component highlights the importance of data governance, digital preparedness, and value alignment at and across different levels. The people component includes the relevance of hedonic and utilitarian values. Meanwhile, the process component emphasizes both strategic and technical aspects of AI design and implementation, such as knowledge structuring, media selection, and narrative representation. In line with this agenda, this study addresses the trends in literature and provides a path for sustainable appropriation by museums.