The DOI matches its Crossref record; 138 of the 140 references it cites were found in a registry or an index.
Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities
Open accessAbstract
The idea of developing a system that can converse and understand human languages has been around since the 1200 s. With the advancement in artificial intelligence (AI), Conversational AI came of age in 2010 with the launch of Apple’s Siri. Conversational AI systems leveraged Natural Language Processing (NLP) to understand and converse with humans via speech and text. These systems have been deployed in sectors such as aviation, tourism, and healthcare. However, the application of Conversational AI in the architecture engineering and construction (AEC) industry is lagging, and little is known about the state of research on Conversational AI. Thus, this study presents a systematic review of Conversational AI in the AEC industry to provide insights into the current development and conducted a Focus Group Discussion to highlight challenges and validate areas of opportunities. The findings reveal that Conversational AI applications hold immense benefits for the AEC industry, but it is currently underexplored. The major challenges for the under exploration were highlighted and discusses for intervention. Lastly, opportunities and future research directions of Conversational AI are projected and validated which would improve the productivity and efficiency of the industry. This study presents the status quo of a fast-emerging research area and serves as the first attempt in the AEC field. Its findings would provide insights into the new field which be of benefit to researchers and stakeholders in the AEC industry.
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“All these additional cost overheads may be a prohibiting factor for smaller construction companies with limited financial resources (Saka et al. 2023b ).”
From A review of LLMs and their applications in the architecture, engineering and construction industry (2025) · cites this paper as Saka et al. 2023b
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Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024) · Sustainability · doi:10.3390/su16031166 · Cited by 302
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Metadata
- Authors
- Saka, A. B., Oyedele, L. O., Akanbi, L. A., Ganiyu, S. A., Chan, D. W. M., & Bello, S. A.
- Affiliations
- Leeds Beckett University (GB)
- University of the West of England (GB)
- Teesside University (GB)
- Hong Kong Polytechnic University (HK)
- Corresponding author
- Oyedele, Lukumon O.
- ORCID
- Venue
- Advanced Engineering Informatics
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 55
- Pages
- 101869
- ISSN
- 1474-0346 (ISSN-L)
- OpenAlex
- W4313457971
- Cited by
- 161
- References
- 140
- Citations by year
- 2026: 39 · 2025: 50 · 2024: 50 · 2023: 22
- FWCI
- 19.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Advanced Engineering Informatics (publishedVersion, cc-by)
- PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by)
- Leeds Beckett Repository (Leeds Beckett University) (submittedVersion)
- Teesside University Research Portal (Teesside University) (submittedVersion, cc-by)
- UWE Research Repository (UWE Bristol) (submittedVersion, cc-by)
- WestminsterResearch (University of Westminster) (submittedVersion, cc-by)
- Funders
- Engineering and Physical Sciences Research Council (EP/S031480/1)
- Topics
- Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- conversational AI, architecture, engineering and construction, natural language processing, speech recognition, chatbots, construction productivity, implementation barriers, future research directions
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