Hawar Himdad J. Sektani, Fenk D. Miran, Hardi K. Abdullah
Community- and values-based approaches to the conservation of architectural heritage are increasingly emphasised. Yet empirical evidence on how local communities assess the potential of interventions for built heritage remains limited. Hence, when examining heritage interventions based on prevailing theories, visual scenario testing is considered a significant communication tool. Therefore, this study investigates local community preferences for different intervention levels in Koya City’s urban heritage using Artificial Intelligence (AI)-assisted visualisations that span a continuum of interventions. The visualisations serve as the basis for the survey, which was used to explore local preferences for the various heritage intervention scenarios. The correspondence between the AI-assisted visuals and the theoretical interventions was assessed by experts before the survey. The findings suggest that low- to moderate-intensity conservation strategies that preserve architectural character are consistently preferred by the survey community. In contrast, interventions that involved considerable physical change were markedly less favoured. Results from the expert validation test indicate that low- to moderate-intervention levels were reliably visualised using AI-assisted visualisations, while the higher-intervention levels were considered less representative. The supplementary calibration further highlights the importance of visual granularity in participatory heritage evaluation. However, the study remains exploratory and limited by the use of AI-assisted visualisations, a single case-study context, and the difficulty of translating nuanced conservation doctrines into visually discrete categories. This study makes a dual contribution by providing empirical evidence of local preferences across a continuum of conservation interventions, and by proposing an AI-assisted visual methodology to bridge expert conservation theory with public understanding.