Design and cultural heritage in architecture in the age of artificial intelligence: generative models, HBIM, and evidence-based digital reconstruction

Abstract

This article examines how artificial intelligence (AI), especially generative AI, is reshaping architectural design, historic buildinginformation modelling (HBIM), and digital reconstruction in cultural heritage contexts. It clarifies a central distinction: AI outputs may be visually persuasive, but they become architectural or heritage knowledge only when they can be validated against historical evidence, spatial and material constraints, and conservation requirements. The article brings togethertwo bodies of literature: generative AI in architectural representation, early design, spatial exploration, and studio practice; and AI-supported HBIM, scan-to-HBIM, semantic enrichment, digital twins, and evidence-based reconstruction. Methodologically, this study is a thematic narrativeliterature review and conceptual synthesis, not an empirical case study, systematic review, or bibliometric analysis. The literature is analysed through three lenses: design stage, information form, and epistemic claim. The review shows that generative AI is most visible in conceptual design, visualisation, prompt-based exploration, and rapid production of alternatives. By contrast, AI-supported HBIM research is concerned mainly with point-cloud processing, semantic segmentation, scan-to-HBIM automation, ontology-based enrichment, data quality, and heritage-management workflows. This comparison reveals a persistent gap between thespeed of representation and thereliability of information. In heritage contexts, the gap is critical because HBIM models and reconstructions can guide conservation decisions, interpretation, and public memory.The article concludes that AI-supported heritage modelling should be assessed usingevidence, uncertainty, authenticity, provenance, metadata, paradata, and expert validation, rather thanvisual plausibility alone. Its original contribution is to frame evidence-based generative HBIM as an operational validation workflow. In this workflow, AI-generated elements remain provisional until they are linked to sources, marked by uncertainty, checked againstsurvey data, archival evidence, typological and material knowledge, and expert judgement, and integrated into HBIM as traceable information rather than as anautonomous reconstruction.