Conversational artificial intelligence in the AEC industry: A review of present status, challenges and opportunities
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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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Additional cost overheads may be a prohibiting factor for smaller construction companies with limited financial resources.
“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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- 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
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- conversational AI, architecture, engineering and construction, natural language processing, speech recognition, chatbots, construction productivity, implementation barriers, future research directions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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