Evaluation of age-friendly public space renovation in older residential communities based on AHP and AIGC —a case study of Shaolin Community, Quanzhou, China

Using Shaolin Community in Quanzhou, China, as a case, this study examined artificial intelligence-generated content (AIGC) for early-stage design and evaluation of age-friendly public-space renovation. A four-criterion, 16-indicator system was developed through literature and policy review, field investigation, and professional consultation. A 15-member specialist panel applied the analytic hierarchy process (AHP) to determine weights. A separate 15-member evaluation panel, including six older resident representatives, then rated six scheme sets generated by Dreamina AI, MidJourney, and Stable Diffusion for five public-space nodes. Infrastructure Renovation received the highest criterion weight, while accessible facilities, cultural atmosphere, environmental sanitation, spatial accessibility, and quality of life ranked highly among the indicators. Within the case-specific workflows, the two Stable Diffusion scheme sets received the highest composite scores. Panel B ratings showed high concordance in scheme ordering (Kendall’s W = 0.931, p < 0.001), and the complete ranking remained unchanged across 64 one-at-a-time weight-perturbation scenarios (± 10% and ± 20%). Structured weighting made the screening criteria for AIGC concepts more explicit than an undifferentiated visual appraisal. However, the study covered one community, six scheme sets, limited resident participation, and platform-specific workflows, and no probabilistic uncertainty analysis was performed. The findings therefore should not be generalized beyond this setup. The integrated workflow is presented as preliminary conceptual-design decision support, not evidence of general tool superiority or implementation performance.