BEYOND THE STATIC PAST: A GENERATIVE AI FRAMEWORK FOR MULTI-PATH HISTORICAL RECONSTRUCTION AND IMMERSIVE VR EXPLORATION

The preservation of global cultural heritage is facing an unprecedented crisis. Rapid urbanization, geopolitical conflict, and environmental degradation are erasing historical sites at an alarming rate, often leaving behind only fragmented ruins or digital photographs. Current digital preservation methodologies, such as Photogrammetry and LiDAR (Light Detection and Ranging), have enabled the creation of high-fidelity "Digital Twins." However, these methods are fundamentally limited; they capture the site only as a static, frozen artefact, devoid of the complex temporal and social contexts that shaped it. They present history as a finished product rather than an evolving process.

This research proposes a novel AI-Enhanced Reconstruction Framework that transcends traditional documentation by integrating Natural Language Processing (NLP), Neural Radiance Fields (NeRF), and Generative Adversarial Networks (GANs). We introduce the concept of "Branching Historical Timelines," a computational approach that allows users to explore not just the history that occurred, but also "counterfactual" or alternative histories based on variable pivot points. By integrating these generative models into an immersive Virtual Reality (VR) environment, the proposed system transforms passive observation into active historical inquiry. This framework offers a robust tool for both educational simulation and digital humanities research, shifting the paradigm from static preservation to dynamic simulation.