A PERCEPTIVE CHATBOT SYSTEM FOR EXPLAINING ARTWORK BY INTEGRATING DATA FROM TRUSTED SOURCES

The increasing digitization of art collections and cultural heritage has created a need for intelligent systems that can enhance user engagement and understanding of artworks. This study presents the design and development of a perceptive chatbot system for explaining artwork by integrating data from trusted sources. The proposed system leverages advances in Artificial Intelligence, Natural Language Processing, and knowledge-based systems to provide accurate, contextual, and interactive explanations of artworks. By sourcing information from verified databases such as museum archives, scholarly publications, and curated digital repositories, the chatbot ensures reliability and authenticity of information. The system is designed to interpret user queries, analyze artwork metadata, and generate human-like responses that enhance user learning experiences. The study evaluates the system’s performance in terms of accuracy, response relevance, and user satisfaction. Findings indicate that integrating trusted data sources significantly improves the credibility and educational value of chatbot responses. The research concludes that perceptive chatbot systems can serve as effective tools for art education, virtual museum tours, and cultural preservation, although challenges related to data integration, context understanding, and system scalability remain.