Comparative analysis demonstrates enhanced pattern detection and contextual validation in urban design systems, suggesting improved adaptability and accountability through human-in-the-loop AI.
This paper examines Artificial Intelligence (AI) in urban design research as both an analytical tool and an adaptive evaluation framework. By comparing AI-based and traditional methods, it shows how AI can reveal hidden patterns in complex urban systems while supporting human-centred design. Using a human-in-the-loop perspective, the paper highlights AI’s integrative, interdisciplinary role in strengthening methodological rigour, accountability, and contextual validation. It argues that AI-driven evaluation can enable more responsive, inclusive, and sustainable urban design solutions, providing a foundation for more effective and accountable research and practice.
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Choi et al. (2026) studied this question.
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