VIRTUAL ART SPACES AND MACHINE LEARNING CURATION - Kumari, Poonam; Rashmi, Prachi; Chandra, Subhash; Sachdeva, Ankit; Ghosh, Raj Kumari; Kandhari, Harsimrat; Manav, Omkar Mahesh (ShodhKosh: Journal of Visual and Performing Arts, 2025)
With the development of machine learning (ML), the concept of virtual space in art is becoming a reality, and it is changing the established curatorial procedures and altering the ways in which audiences experience art. This paper explores the dynamic overlap between ML-driven systems and immersive digital spaces to know how curatorial decision-making, experience of audiences, and interpretation of art is being redefined. The study presents findings of virtual galleries, online archives and interactive display sites based on qualitative and quantitative approaches to assessing how algorithms predict, classify and even create visual artworks. Specific focus is made on the recommendation engines, neural models of style analysis, and generative networks that help to come up with new forms of curators. Simultaneously, the paper examines virtual and augmented reality (VR/AR) as experiential models with a particular focus on how the designs of immersivity, interactivity, and international openness broaden the curatorial practice. These virtual spaces give the viewer a chance to engage more, allowing them to create their own paths in collections of art and disrupting traditional ideas of authorship and originality as well as interpretive power.