Exploring Design Practice with Generative AI: Perspectives from AEC Design Professionals

Paper Title

Exploring Design Practice with Generative AI: Perspectives from AEC Design Professionals

Publication Info

• Topic area: Integration of Generative AI into Architecture, Engineering, and Construction (AEC) design practices.
• Keywords: Generative AI, AEC design, conceptual design, design collaboration, creativity, client communication, design service innovation, professional attitudes, mixed-methods study, digital transformation.

Background and Problem

• Problem / challenge: Limited empirical studies exist on how AEC design professionals adopt and perceive Generative AI (GenAI) in their workflows, despite its growing prominence.
• Significance: Understanding GenAI’s role in AEC design is crucial for optimizing its application, addressing professional concerns, and guiding industry transformation.
• Motivation and related work: Previous research has focused on GenAI’s technical capabilities and its use in other design domains (e.g., industrial design, user experience design). However, AEC design remains underexplored, particularly regarding its unique regulatory constraints, collaborative demands, and the perspectives of frontline professionals.

Solution

• Proposed approach: A mixed-methods study combining semi-structured interviews with 20 AEC professionals and a survey of 191 professionals to analyze GenAI’s applications, perceptions, and potential in AEC design.
• Novelty:
  1. First empirical investigation of GenAI integration in AEC design practices.
  2. Identification of a “shame-yet-pride” dynamic in professional attitudes toward GenAI.
  3. Development of an application map for GenAI in AEC workflows, highlighting strategic uses.
  4. Recommendations for technical and mindset adaptations to enhance GenAI’s integration.
• Procedure and key techniques:
  • Conducted interviews with professionals meeting specific experience criteria (≥2 years in AEC and GenAI).
  • Designed and distributed a survey informed by interview findings to a broader sample.
  • Analyzed qualitative data using thematic coding and quantitative data using descriptive and inferential statistics.

Results

• Concrete findings:
  • GenAI is primarily used in conceptual design (43.5% of survey respondents) and design expression/documentation stages.
  • Key applications include inspiration exploration, client communication, and expanding design service boundaries.
  • GenAI’s creativity is perceived as “passive creativity,” emerging from its unpredictability and randomness.
  • Reliability issues, particularly in detail generation, remain a significant limitation.
• Advantage over baselines:
  • Enhanced efficiency in generating design inspiration and refining client communication materials.
  • Expanded service offerings, such as ancillary design assets (e.g., mascots, logos).
• Experiments / evaluation:
  • Interviews: 20 professionals with diverse roles and GenAI experience.
  • Survey: 191 respondents from multiple countries, covering various AEC disciplines.
  • Metrics: Usage frequency, application stages, collaboration types, perceived creativity, and reliability.
• Limitations and future work:
  • Sample may not represent all AEC professionals, especially those with limited GenAI use.
  • Findings may lag behind the latest GenAI advancements due to the rapid pace of development.
  • Future work should explore subgroup-specific perceptions and validate emerging GenAI tools in practice.

Summary

This study provides the first empirical investigation into how AEC design professionals integrate Generative AI into their workflows. It identifies GenAI’s primary applications in conceptual design, client communication, and service innovation, while highlighting challenges such as reliability issues and professional ambivalence. A nuanced “shame-yet-pride” dynamic reveals both the value placed on GenAI’s capabilities and the tendency to conceal its use. The study offers an application map and actionable recommendations for developing AEC-specific tools and fostering transparent collaboration with GenAI. These insights aim to guide the sustainable and effective adoption of GenAI in the AEC industry.