ADAPTIVE INTERFACES IN AI-POWERED ART GALLERIES - Sharma, Tripti; Sharma, Aakash; Anwar, Syed Rashid; Seth, Kanika; Singh, Tanya; Choudhary, Devanand (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The introduction of artificial intelligence (AI) to art galleries is transforming the visitor-artwork interaction and establishing new adaptive and personalized experiences that are more context-relevant. This paper will discuss the concept of adaptive interface design and deployment within AI-assisted art spaces, and how dynamic systems can adapt the exhibition content to both a specific user profile, behavior, and emotional reaction. In an in-depth analysis of the literature on interactive technologies in museums, user experience design, and machine learning-based personalization, the research paper reveals current gaps in visitor interaction and the flexibility of the interface. The data are gathered using a mixed-method research design by using user observation, interviews, and analytics to comprehend the interaction patterns and the cognitive response to adaptive systems. The suggested system architecture is based on user profiling, behavior tracking, and real-time content adjustment algorithms that are implemented with the use of AI frameworks like TensorFlow and OpenCV. The examples of AI-boosted galleries that already exist and a prototype of an application at a local exhibition show that adaptive interfaces could have a significant potential to engage viewers further and lead to meaningful interactions with art. Measures of evaluation such as dwell time, emotional resonance and interaction diversity demonstrate a significant increase in user satisfaction and learning retention. The results point to the importance of AI-based adaptive interface as an intelligent mediator of art and audience, which can be scaled down to future digital curation activities and can contribute to inclusivity and accessibility in contemporary art environments.