Exploring case-based platforms: AI-powered meta-analysis of participatory design and planning practices

Case-based platforms, such as Participedia, PartScout, Co-Cities, and LATINNO, are increasingly recognized as inclusive, cumulative, and informative tools for designing collaborative and participatory actions. These platforms are equipped with social technologies, crowdsourcing applications, and human-computer interactions, which facilitate the dissemination, analysis, and exchange of participatory design and planning (PD&P) experiences that address chronic public problems related to shared interests and values. However, exploring the ever-increasing, interdisciplinary, and extensive scope of PD&P cases to gain insights is challenging for researchers and practitioners, thereby making it essential to develop effective strategies. Despite participatory practices being inherently collective, there is little discussion of how to leverage collective intelligence (CI) into participatory research. We claim, accordingly, that the systematic use of CI will enrich our understanding of the diverse realm of PD&P. We have approached case-based platforms through the lens of CI, as an umbrella term bridging various concepts encompassing cooperative, bottom-up, citizen-led, collaborative, and grassroots actions. This study aims to conduct a meta-analysis to reveal cross-case patterns, specifically probing contextual, methodological, and actor-related dimensions, within a dataset comprising 2,439 cases. The research design is grounded in the case survey method, further enhanced by integrating AI-based clustering, mapping, and semantic analysis. The findings point to the promising performance of the proposed method in revealing diversified and highly interconnected PD&P patterns. Despite its limitations, this preliminary study provides valuable insights through the CI genome, contributing to a comprehensive understanding of PD&P landscapes and stimulating new research questions.