FOLK ART FESTIVALS AND AI-DRIVEN VISITOR ANALYTICS - Vyshnavi A; Garg, Mohan; Goyal, Gopal; Parashar, Mukesh; Kapoor, Tarun; Pawar, Anil Bhanudas; Bhilare, Amol (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Folk art festivals are crucial in maintaining cultural heritage, identifying the community, and intergenerational knowledge transfer. Nonetheless, visitor behavior in such culturally saturated settings is difficult to comprehend because of shifting patterns of crowds, differing groups of participants, and different forms of involvement. The latest developments in the field of artificial intelligence (AI) provide a strong toolkit to filter and process extensive data on visitor attendance to make decisions helpful in preserving culture and managing the event in real-time. This paper examines the application of AI-based visitor analytics, namely machine learning, computer vision, and natural language processing to the planning and assessment of folk art festivals. Through mixed-methods research methodology, the paper evaluates the capabilities of AI methods to analyze visitor traffic, forecast congestion, interpret sentiment using textual and verbal responses, and divide the audience into groups by their behavioral patterns. The case studies of the chosen folk art festivals illustrate that the AI-based analytics can offer much greater operational efficiency, visitor experience, and insights, compared to the traditional manual way of observing. The results point to the idea that real-time crowd tracking is a feature that promotes security and resource distribution, the sentiment analysis performs a detailed assessment of visitor perceptions, and behavioral modeling influences specific cultural programming.