AI-ENABLED INTERACTIVE INSTALLATIONS FOR MODERN MUSEUMS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3.2025.6670Keywords:
Artificial Intelligence, Interactive Installations, Smart Museums, Visitor Engaging, Human-Computer Interaction, Cultural Heritage TechnologyAbstract [English]
The high development of Artificial Intelligence (AI) has altered the visitor interaction paradigm in the contemporary museums. The paper describes the design, implementation, and evaluation of AI-enabled interactive installations that provide more cultural experience by improving the mechanisms of intelligent perception, the personalisation and adaptive interactivity. The suggested system will combine computer vision, natural language processing and multimodal sensing to analyze visitor behaviors and preference in real time. Using deep learning for gesture and speech analytics, the installation adapts to individual users/groups and responds through various tech-based interventions such as narrative, visual projection and ambient response. The system architecture combines low-latency interaction by using edge computing with continual learning and content optimization by having a cloud-based analytics layer. In pilot museum testing, it showed that visitor immersion, retention of learning and engagement measures were significantly improved when compared to traditional fixed exhibitions. The findings show that the duration of interaction with users has increased by 45 % and that the accuracy of the information that is displayed has improved by 32 %. In addition, qualitative feedback proved that the emotional appeal and inclusivity achieved by adaptive storytelling mechanisms were significant. This study shows a promise of applying AI-driven installations to transform the space of museums into a participatory, data-driven space that connects art, history, and technology. The next-generation of work is based on ethical governance of data, scalability with a variety of cultural backgrounds, and integration of affective computing to further human and machine co-sensationalize the heritage interpretation process.
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Copyright (c) 2025 Preetjot Singh, Mr. Nishant Trivedi, Priyanka S. Utage, Neha, Ms. Saiqa Khan, Deepak Minhas
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