Evaluating AI-Driven Sustainable Neuro-Urbanism through a Human-Centered Framework: The Case of Ras Elbar City, Egypt

This study applies and evaluates a previously developed human-centered framework for AI-driven sustainable neuro-urbanism through a case study of Ras Elbar City, Egypt. Building on earlier conceptual research that integrated artificial intelligence (AI), neuroscience-informed design, and sustainability principles, this paper seeks to test the framework’s practical relevance in the context of a developing coastal city. Using qualitative evaluation methods, including field observation and spatial assessment, the research examines twelve key urban design elements such as green spaces, mobility networks, lighting, and soundscapes. Each element is analyzed across three dimensions: neuro-urbanism goals, AI integration, and sustainability outcomes. The findings show that Ras Elbar has strong natural and sensory qualities that support mental well-being and social interaction, particularly through its coastal environment and walkable scale. However, the use of adaptive AI tools and smart technologies remains limited. The study highlights opportunities for introducing scalable, low-cost AI strategies that enhance emotional comfort, inclusivity, and environmental performance. It concludes by refining the framework for developing cities, offering practical guidelines to help planners and designers create more adaptive, sustainable, and mentally supportive urban environments.