Generative Artificial Intelligence for the Sustainable Digital Revitalization of Minnan Decorative Cement Tiles: A Design Science Approach to Cultural Heritage

Traditional architectural ornament embodies cultural knowledge and local memory, yet its digital treatment often remains confined to static documentation, lacking computational representation, generative reuse, and public participation. Taking Minnan decorative cement tiles from southern Fujian, China, as a case study, this study proposes a design-science framework for sustainable digital revitalization that integrates pattern survey and classification, shape-grammar reconstruction, Low-Rank Adaptation (LoRA) training, interactive web-prototype development, and user evaluation. Shape grammar was used to formalize compositional relationships, and two Stable Diffusion 1.5-based models were trained: Baseline LoRA and shape-grammar LoRA (SG-LoRA). Their outputs were compared using the Fréchet Inception Distance (FID) and expert ratings on a five-point Likert scale. The web prototype was shaped by 220 valid user-needs questionnaires and then evaluated formatively with 30 participants through the User Experience Questionnaire (UEQ). Respondents expressed positive intentions toward digital presentation, AI-assisted creation, and social sharing; all six UEQ scales scored positively, with Efficiency (1.850) and Perspicuity (1.733) scoring the highest. The results demonstrate a design pathway linking computable formal rules, generative adaptation, and public participation, offering preliminary evidence for formal-feature preservation, public accessibility, and participatory reuse.