Reframing Urban Morphology for AI: Integrating Qualitative Dataset for a Specialized Artificial Intelligence

To explore the potential of a customized GPT-4o language model in under-standing and analysing urban morphology theory, three influential books were carefully selected: The Image of the City by Kevin Lynch (1960), Townscape by Gordon Cullen (1961), and The Death and Life of Great American Cities by Jane Jacobs (1961). These books, cornerstones of urban form studies, were chosen for their shared historical context, thematic alignment, and methodological comple mentarity. Starting from three prompts, the Concept Correlator AI was taught to describe the books through keywords and tables and connect the different words through diagrams. By leveraging this glossary, the model analysed prompts related to these concepts, drawing from a comprehensive collection of sources to provide well-informed, concise responses. It systematically compared definitions across the books uploaded in its folders, grouping similar ones under a unified category. Through this refined glossary, the study evaluated AI’s ability to interpret urban forms concepts and identified gaps between the existing definitions and the responses of the customized GPT-4o model. Ultimately, this framework strengthened AI’s foundational knowledge, improving both the contextual accuracy of responses and computational efficiency in urban morphology research. Some definitions in the analysis were identified as incorrect and required reformulation of the lexicon by the AI. This targeted data organization enhances research efficiency by minimizing unnecessary data processing and improving the precision of AI-generated outputs. By bridging traditional typo-morphological theories with emerging AI methodologies, the study contributes to AI’s systematic and meaningful integration into urban morphology, advancing theoretical understanding and practical applications.