From Artifact to Algorithm: The Role of AI in Reimagining Curatorial Practices in Contemporary Art Museums

The rapid digitization of cultural heritage and the growing complexity of audience engagement have compelled contemporary art museums to reconsider traditional curatorial practices. While artificial intelligence has demonstrated transformative potential across various fields, its role in redefining the conceptual and operational frameworks of museum curation remains underexplored. This study examines how AI technologies, ranging from computer vision to generative models, are reshaping curation from an artifact-centered process to an algorithm-mediated practice. The research adopts a case study methodology, analyzing AI implementations across three leading institutions: the Victoria and Albert Museum, the Museum of Modern Art, and the Palace Museum's digital lab. By synthesizing technical reports, curator interviews, and visitor feedback, the study identifies key patterns in how AI facilitates dynamic collection mapping, visitor-centric exhibition design, and generative curation. These applications reveal both the operational efficiencies gained and the emerging tensions between algorithmic automation and curatorial authority. Findings suggest that AI functions not merely as a tool but as an active collaborator in curation, introducing the concept of "algorithmic curation" as a new paradigm. However, this shift raises critical questions about authorship, bias, and the democratization of cultural interpretation. The study contributes to ongoing debates in digital museology by proposing a framework for ethical AI integration in curatorial workflows, while highlighting the need for institutional guidelines to balance innovation with cultural stewardship.