The Application and Challenges of Artificial Intelligence in Contemporary Art Curation

Abstract

The integration of Artificial Intelligence (AI) into contemporary art curation is revolutionizing how exhibitions are designed, artworks are selected, and audiences interact with art. Through technologies such as machine learning, natural language processing (NLP), and computer vision, AI enables curators to analyze vast collections, predict visitor preferences, and construct dynamic exhibition experiences. This paper examines the dual nature of AI in art curation—its potential to enhance creativity and efficiency, and the ethical, technical, and philosophical challenges it introduces. Drawing upon case studies from global museums and digital art platforms, the research identifies key application domains, evaluates curatorial outcomes, and analyzes stakeholders’ perceptions of AI-assisted practices. Quantitative data from 30 institutions are supplemented by qualitative analysis to explore how AI reshapes curatorial authority, audience engagement, and the meaning-making process. Findings reveal that while AI fosters innovation and accessibility, it also risks algorithmic bias, cultural homogenization, and diminished human interpretive agency. The paper concludes that sustainable AI curation requires a human–machine collaborative framework emphasizing transparency, inclusivity, and cultural sensitivity.