Reframing urban morphology for AI. Integrating qualitative datasets specialised 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), Town-scape by Gordon Cullen (1961), and The Death and Life of Great American Cities by Jane Jacobs (1961). These books, cornerstones of urban form stud-ies, were chosen for their shared historical context, thematic alignment, and methodological complementarity. Starting from three prompts, the Concept Correlator AI was taught to de-scribe 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 simi-lar 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 com-putational efficiency in urban morphology research. Some definitions in the analysis were identified as incorrect and required reformulation of the lexi-con 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