Adopting artificial intelligence in architectural conceptual design : a systematic bibliometric analysis
Chen, Liangyu and Chen, Zhen and Dong, Feng (2026) Adopting artificial intelligence in architectural conceptual design : a systematic bibliometric analysis. Architecture, 6 (2). 60. ISSN 2673-8945 (https://doi.org/10.3390/architecture6020060)
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Abstract
This article presents a systematic bibliometric analysis on academic research into Artificial Intelligence (AI) applications in Architectural Conceptual Design (ACD). Based on a curated selection of publications indexed in the Web of Science (WoS) and Scopus databases between 2010 and 2025, this article shows a study that maps the intellectual evolution, thematic composition, and methodological trends of the field. By using the software tool VOSviewer, this study generates a series of knowledge graphs, including Keyword Co-Occurrence and International Collaboration Networks. The findings from this study reveal a rapid acceleration in AI-related research focused on the conceptual design stage, highlighting its transformative potential for architectural practice. Through a critical analysis of bibliometric results, this study identifies dominant research emphases, emerging directions, and persistent frictions between academic approaches and industry adoption. This review article contributes to the theoretical consolidation of AI applications in ACD and provides a structured foundation for future ACD-related research and practice.
ORCID iDs
Chen, Liangyu
ORCID: https://orcid.org/0009-0009-9736-9886, Chen, Zhen
ORCID: https://orcid.org/0000-0003-0212-1140 and Dong, Feng;
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Item type: Article ID code: 95990 Dates: DateEvent10 April 2026Published2 April 2026AcceptedSubjects: Fine Arts > Architecture
Technology > Engineering (General). Civil engineering (General) > Engineering design
Science > Mathematics > Electronic computers. Computer scienceDepartment: Faculty of Engineering > Architecture
Faculty of Science > Computer and Information SciencesDepositing user: Pure Administrator Date deposited: 10 Apr 2026 15:17 Last modified: 02 Jun 2026 07:11 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/95990
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