This PhD thesis explores the evolving field of heritage planning, focusing on the cultural significance of heritage properties. It advocates for a value-based approach that recognizes the diverse perspectives of stakeholders, including experts, policymakers, and users. While participatory heritage aims to foster consensus-building, tensions may arise due to varying cultural significance conveyed by different stakeholder groups. Conventional research methods are time-consuming and costly, limiting their effectiveness in heritage planning. To address this gap, this research aims to utilize Artificial Intelligence (AI) models and information repositories, such as social media platforms, to understand the cultural significance of built heritage from different stakeholder groups’ perceptions. This research presents a theoretical framework that examines the factors influencing consensus-building on heritage values and attributes. Based on this framework, a public participation methodology empowered by AI is developed and tested in the case study of windcatchers in Yazd, Iran. This study compares the perceptions of three stakeholder groups: experts, policymakers, and users. The findings reveal consensus on the value of windcatchers while highlighting differing interpretations of their significance. The AI-empowered methodology proves effective in uncovering stakeholder groups' understanding of cultural significance. This framework can be replicated in other case studies, facilitating participatory heritage practices. The thesis contributes to knowledge in public participation, cultural significance, and AI in heritage planning, offering insights for practitioners and policymakers to promote inclusive heritage practices. It emphasizes the importance of stakeholders' contributions and advocates for a more diverse and inclusive approach to heritage planning.