AI-Augmented Passive Architectural Design for Thermally Resilient Housing in Nigeria Through Structured Review and Conceptual Framework

For this review, thermal resilience is operationally defined as the capacity of housing to limit indoor heat exposure and dependence on mechanical cooling while maintaining feasible opportunities for occupant adaptation under climatic, electricity-supply and affordability constraints. Thermal resilience is therefore treated as broader than thermal comfort alone. This critical structured narrative review synthesises peer-reviewed analytical studies, supplemented by standards, policy documents, institutional reports and seminal background sources. Scopus and Google Scholar served as the principal literature-discovery sources, supplemented by targeted searches of publisher platforms, including ScienceDirect, Taylor & Francis Online and MDPI-hosted journals, and relevant institutional websites and repositories. The final search was completed on 30 July 2026. Evidence was coded under passive architectural design, adaptive thermal comfort, AI-enabled analytics, urban cooling, planning integration, affordability and governance. Across the reviewed evidence, climate-specific shading, natural ventilation, roof and envelope measures, landscape strategies and occupant adaptation are recurrent performance themes. At the same time, uncertainty arises from limited Nigerian measured datasets and the transferability of models developed in other climates. AI is treated as transparent decision support for comparing architectural and neighbourhood alternatives against comfort, overheating, energy, cost and liveability criteria, with local calibration and post-occupancy validation. The article develops an architecture-led, planning-integrated workflow linking climate intelligence, passive design, simulation, AI-assisted optimisation and performance-in-use feedback.