This study investigates how collective intelligence emerges in crowdsourcing for architectural design. Previous studies have revealed that collective intelligence emerges from collaboration and can outperform individual intelligence. As design is a highly collaborative practice, collective intelligence plays a vital role in the design process. In this study, we compare the structure of two architectural design crowdsourcing systems using several methods. The results of the analysis suggest that design crowdsourcing systems can give rise to the following three types of collective intelligence: (1) discussive, which emerges from a conversation between designers and clients; (2) synthetic, which emerges from a parallel and sequential design development; and (3) evaluative, which is based on the wisdom of the crowd in evaluating and selecting designs. The article concludes with recommendations for collaborative design method.

Recent advances in machine learning have enabled computers to converse with humans meaningfully. In this study, we propose using this technology to facilitate design conversations in large-scale urban development projects by creating chatbot systems that can automate and streamline information exchange between stakeholders and designers. To this end, we developed and evaluated a proof-of-concept chatbot system that can perform design conversations on a specific construction project and convert those conversations into a list of requirements. Next, in an experiment with 56 participants, we compared the chatbot system to a regular online survey, focusing on user satisfaction and the quality and quantity of collected information. The results revealed that, with regard to user satisfaction, the participants preferred the chatbot experience to a regular survey. However, we found that chatbot conversations produced more data than the survey, with a similar rate of novel ideas but fewer themes. Our findings provide robust evidence that chatbots can be effectively used for design discussions in large-scale design projects and offer a user-friendly experience that can help to engage people in the design process. Based on this evidence, by providing a space for meaningful conversations between stakeholders and expanding the reach of design projects, the use of chatbot systems in interactive design systems can potentially improve design processes and their outcomes.

This paper explores the potential of chatbots, powered by large language models, as a tool for fostering community participation in architectural and urban design. By taking a hybrid approach to community participation in a real-world mixed-use building project, in which we integrated remote chatbot engagements with face-to-face workshops, we explored the potential for a hybrid approach to scaling up the reach of participation while ensuring that such participation is meaningful, genuine, and empowering. Our findings suggest that a hybrid approach amplified the strengths and mitigated the shortcomings of the two methods. The chatbot was effective in sustaining the length of participation, broadening the reach of participation, and creating a personalized environment for introspection. Meanwhile, the face-to-face workshops still played a crucial role in bolstering community ties and trust. This research contributes to understanding chatbots’ strengths and weaknesses in participatory processes, both within spatial design and beyond. In addition, it informs future explorations of participatory processes that span different spatial-temporal configurations.

Inspired by open-source and crowd sourcing technologies, the concept of ‘open-source architecture’ has been increasingly used to describe collaborative architectural design (CoDesign) in recent years. In this article, we aim to show that major related issues remain unresolved. As such, they limit the wider adaptation of open-source methods to the world of architecture. We focus on the following issues: first, architectural theorists have not addressed the meaningful limitations and possibilities of these methods in architecture, including the ramifications of sharing knowledge with end users; second, professional architects already share ideas through existing networks that resemble open-source models; third, projects that have aspired to democratise design processes in the recent past have encountered difficulties in sharing tacit professional knowledge with non-professional users; finally, existing open-source licences exclude professional liability which is the bedrock of relations between the architect, their clients, and regulatory authorities in architecture. To arrive at open-source architecture, we argue that these issues need to be further investigated and resolved. The article concludes with reflections on the exchange of knowledge and ideas between professionals and non-professionals, architects and users, and how this can be simplified by using crowd-sourcing technologies.

Architectural design decisions are primarily made through an interaction between an architect and a client during the conceptual design phase. However, in larger-scale public architecture projects, the client is frequently represented by a community that embraces numerous stakeholders. The scale, social diversity, and political layers of such collective clients make their interaction with architects challenging. A solution to address this challenge is using new information technologies that automate design interactions on an urban scale through crowdsourcing and artificial intelligence technologies. However, since such technologies have not yet been applied and tested in field conditions, it remains unknown how communities interact with such systems and whether useful concept designs can be produced in this way. To fill this gap in the literature, this paper reports the results of a case study architecture project where a novel crowdsourcing system was used to automate interactions with a community. The results of both quantitative and qualitative analyses revealed the effectiveness of our approach, which resulted in high-level stakeholder satisfaction and yielded conceptual designs that better reflect stakeholders’ preferences. Along with identifying opportunities for using advanced technologies to automate design interactions in the concept design phase, we also highlight the challenges of such technologies, thus warranting future research.

Urban planners need scalable ways to monitor pedestrian conditions across heterogeneous cities, but conventional Level-of-Service (LOS) methods are often locally calibrated and difficult to transfer. This study proposes a city-adaptive framework for pedestrian LOS mapping using spatio-temporal graph models and few-shot transfer learning. Pedestrian count data from Melbourne, Dublin, and Zurich were converted into six ordinal LOS classes using city-specific percentile thresholds computed from the training data, yielding a relative congestion measure rather than an absolute cross-city standard. We developed a spatio-temporal graph transformer with an ordinal prediction head and evaluated it under in-domain, zero-shot, few-shot, and domain-adaptive settings. The results show strong in-domain performance in Melbourne (accuracy 79.7%; Acc ± 1 99.1%) and effective adaptation to the city-adaptive ordinal classification task. Few-shot fine-tuning with only 5% labeled target city data recovered 95–99% of in-domain performance, suggesting that small amounts of local supervision can substantially reduce calibration requirements in data-scarce environments. KernelSHAP analysis indicates that short-term temporal lag features dominate predictions across cities, whereas spatial and contextual features vary more strongly with local urban structure. The findings suggest that few-shot transfer learning can support pedestrian LOS estimation in cities with limited labeled data; however, the proposed LOS formulation should be interpreted as a city-specific relative indicator rather than an absolute measure of pedestrian comfort, crowding, or service quality. While the framework was evaluated across three cities, additional validation in diverse urban contexts and against perceptual measures of pedestrian experience remains necessary. Overall, the study contributes a city-adaptive framework for transferable relative LOS prediction rather than a universal cross-city LOS standard.