Artificial Intelligence Review· 2026Q1
Enabling spatial intelligence in the AEC lifecycle: a framework and review of large language models
- 0citations
- Q1SCImago
- 2026year
Short summary
A new layered framework and review of 76 studies show Large Language Models (LLMs) are evolving into active cognitive partners for spatial intelligence across the AEC lifecycle, from design to operation.
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Key points
- A novel layered framework categorizes spatial intelligence across the AEC lifecycle (design, construction, operation, research/education).
- LLMs are demonstrated to be effective in requirement translation, multimodal design generation, and regulatory compliance.
- Integration of LLMs with Building Information Modeling (BIM) across the lifecycle is highlighted as a key advancement.
- Persistent challenges include spatial reasoning accuracy, lack of physical embodiment, and data governance issues.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract The Architecture, Engineering, and Construction (AEC) industry is undergoing a profound digital transformation driven by the urgent need to integrate fragmented, heterogeneous data into actionable intelligence. This paper provides a systematic review of 76 representative studies to examine how Large Language Models (LLMs) enable spatial intelligence across the entire building lifecycle. A layered spatial intelligence framework is proposed, encompassing the dimensions of design, construction, operation, as well as research and education. The review demonstrates that LLMs are evolving from passive text processors into active cognitive partners capable of semantic interpretation, spatial reasoning, and complex decision-making. Key findings highlight the effectiveness of LLMs in requirement translation, multimodal design generation, regulatory compliance, and lifecycle Building Information Modeling (BIM) integration. Despite these advancements, significant challenges persist regarding spatial reasoning accuracy, the lack of physical embodiment, and institutional data governance. This research outlines a strategic roadmap toward embodied intelligence, AEC-specific world models, and enhanced human-AI collaboration, providing a structured foundation for the advancement of LLM-driven spatial intelligence in professional practice.
The authors' abstract, as published at the source. Artificial Intelligence Review, 2026 · DOI ↗
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Field: Building and Construction
Building and ConstructionEngineering