Authors: Assistant Professor Dr. Kanchan Kumari, Professor Manisha V. Nain
Abstract: The converging of artificial intelligence (AI) and climate adjustment within smart city frameworks has appeared as one of the most consequential and contested domains of modern urban governance and sustainability scholarship. This literature review critically examines peer-reviewed research published between 2015 and 2024, drawing on 97 sources spanning urban informatics, environmental science, education and pedagogy, political economy, and science and technology studies (STS). The chapter is structured around five thematic axes: (1) the conceptual architecture of AI-driven climate adaptation systems; (2) the educational and pedagogical dimensions of preparing citizens, professionals, and policymakers to engage with these systems; (3) the socio-technical inequalities embedded in smart city infrastructures; (4) the governance and transparency challenges that AI deployment presents; and (5) the emergent research gaps that demand scholarly and institutional attention. The review finds that while substantial technical literature documents the capabilities of AI in climate modelling, urban heat island mitigation, flood risk prediction, and energy optimisation, there exists a pronounced deficit in research that bridges technical systems design with education, civic participation, and critical literacy. Furthermore, the majority of existing research reflects perspectives from the Global North, with limited attention to the pedagogical, political, and infrastructural conditions of cities in the Global South. This chapter argues that climate adaptation cannot be reduced to a computational problem and that AI systems, however sophisticated, are embedded in social, political, and pedagogical contexts that determine their effectiveness, equity, and legitimacy. The review synthesises these dimensions into a conceptual framework — the Adaptive Intelligence Pedagogy (AIP) model — which foregrounds critical climate literacy as a precondition for just and effective AI-driven urban adaptation. Directions for future research, policy, and educational practice are identified.
