AI-Enabled Building Design Collaboration: Insights from Australian Adoption and Implementation

The integration of Artificial Intelligence (AI) into Architecture, Engineering, and Construction (AEC) practice is reshaping building design processes and collaborative workflows. However, AI’s role as a design collaborator remains poorly understood across educational and professional contexts. To address this gap, this study conducts an empirical survey of built environment students, academics, and professionals. Collectively, the study develops a comprehensive view of AI’s role in building design collaboration. The survey findings (n = 155) show widespread use of Large Language Models (LLMs) and image-generation tools across design education and practice, especially for creative and documentation-related tasks. While AI is valued for enhancing productivity and streamlining workflows, respondents also express concerns around technology dependency, data privacy, bias, and trust. This study contributes dual-perspective insights—encompassing both theoretical foundations and contemporary perceptions—into AI’s evolution towards transparent and multimodal design collaboration. The findings support a more structured and context-aware integration of AI tools into building design practice.

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Publication Details

Journal
Buildings
Published
2026-03-12
DOI
https://doi.org/10.3390/buildings16061126
Primary Topic
BIM and Construction Integration
Type
article
Field-Weighted Citation Impact
0.00

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article

AI-Enabled Building Design Collaboration: Insights from Australian Adoption and Implementation

Michael J. Ostwald, Ju Hyun Lee
Buildings
BIM and Construction Integration
article

AI-Enabled Building Design Collaboration: Insights from Australian Adoption and Implementation

Michael J. Ostwald, Ju Hyun Lee
article en

Abstract

The integration of Artificial Intelligence (AI) into Architecture, Engineering, and Construction (AEC) practice is reshaping building design processes and collaborative workflows. However, AI’s role as a design collaborator remains poorly understood across educational and professional contexts. To address this gap, this study conducts an empirical survey of built environment students, academics, and professionals. Collectively, the study develops a comprehensive view of AI’s role in building design collaboration. The survey findings (n = 155) show widespread use of Large Language Models (LLMs) and image-generation tools across design education and practice, especially for creative and documentation-related tasks. While AI is valued for enhancing productivity and streamlining workflows, respondents also express concerns around technology dependency, data privacy, bias, and trust. This study contributes dual-perspective insights—encompassing both theoretical foundations and contemporary perceptions—into AI’s evolution towards transparent and multimodal design collaboration. The findings support a more structured and context-aware integration of AI tools into building design practice.

BuildingsVol. 16(6)
UNSW Sydney (AU)
University of New South Wales
Openalex Percentile: Top 26%
BIM and Construction Integration
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AI-Enabled Building Design Collaboration: Insights from Australian Adoption and Implementation — Michael J. Ostwald, Ju Hyun Lee · Buildings (2026) | TGRS Research Map | TGRS