Digital Presentation and Interactive Learning for Intangible Cultural Heritage Preservation Using Artificial Intelligence - Liuxun Zhang; Zhouluo Wang; Rulan Yang; Qiang Yi (IEEE Access)
The preservation of intangible cultural heritage (ICH) faces significant and multifaceted challenges due to its ephemeral nature, reliance on oral traditions, and contextual embeddedness within lived cultural experiences. Traditional preservation approaches—such as textual documentation, static archiving, and audiovisual recordings—often fall short in capturing the dynamic, embodied, and performative characteristics that define ICH practices. To overcome these limitations, we propose an innovative computational framework that integrates advanced neural representations with structured symbolic logic and contextual grounding mechanisms. We introduce a novel neural-symbolic architecture capable of modeling the fluid, multimodal, and socially constructed nature of intangible cultural knowledge. Our approach includes a culturally informed reasoning strategy that enables the system to align observed cultural signals with both canonical forms and evolving variants within a specific tradition. This is further enhanced by a self-supervised semiotic alignment module, which dynamically adapts through iterative engagement with context-specific cues and emergent performative deviations. By leveraging cutting-edge artificial intelligence, our framework enables the digital preservation, interactive representation, and inclusive transmission of ICH, ensuring its resilience, relevance, and accessibility across generations and communities in a rapidly evolving global landscape.