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Author: search.filters.author.Sirisakdi, LimpasilpSubject: search.filters.subject.text-to-image AI

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    Mindful Architecture from Text-to-Image AI Perspectives: A Case Study of DALL-E, Midjourney, and Stable Diffusion
    (2025-03-01)
    Thampanichwat, Chaniporn
    ;
    Wongvorachan, Tarid
    ;
    Sirisakdi, Limpasilp
    ;
    Chunhajinda, Pornteera
    ;
    Bunyarittikit, Suphat
    Mindful architecture is poised to foster sustainable behavior and simultaneously mitigate the physical and mental health challenges arising from the impacts of global warming. Previous studies demonstrate that a substantial educational gap persists between architecture and mindfulness. However, recent advancements in text-to-image AI have begun to play a significant role in generating conceptual architectural imagery, enabling architects to articulate their ideas better. This study employs DALL-E, Midjourney, and Stable Diffusion—popular tools in the field—to generate imagery of mindful architecture. Subsequently, the architects decoded the architectural characteristics in the images into words. These words were then analyzed using natural language processing techniques, including Word Cloud Generation, Word Frequency Analysis, and Topic Modeling Analysis. Research findings conclude that mindful architecture from text-to-image AI perspectives consistently features structured lines with sharp edges, prioritizes openness with indoor–outdoor spaces, employs both horizontal and vertical movement, utilizes natural lighting and earth-tone colors, incorporates wood, stone, and glass elements, and emphasizes views of serene green spaces—creating environments characterized by gentle natural sounds and calm atmospheric qualities. DALL-E is the text-to-image AI that provides the most detailed representation of mindful architecture.
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    The Architectural Language of Biophilic Design After Architects Use Text-to-Image AI
    (2025-03-01)
    Thampanichwat, Chaniporn
    ;
    Wongvorachan, Tarid
    ;
    Sirisakdi, Limpasilp
    ;
    Somngam, Panyaphat
    ;
    Petlai, Taksaporn
    Biophilic design is an architectural concept that bridges the gap between modern buildings and the innate human longing for nature. In addition, it promotes physical and mental well-being while aligning with several Sustainable Development Goals. Recent research highlights that the architectural language used to describe the attributes of biophilic architecture remains unclear. Previous research has shown that text-to-image AI enhances architects’ ability to articulate their ideas more effectively. Therefore, this study aims to address the following research question: What are the architectural languages of biophilic design after architects use text-to-image AI? The initial step involves generating images of biophilic architecture by using three popular text-to-image AI tools: DALL-E 3, MidJourney, and Stable Diffusion. The 30 selected images were used to help architects develop the architectural language to describe the characteristics of biophilic design across 10 categories: Form, Space, Movement, Light, Color, Material, Object, View, Sound, and Weather. The terms obtained were analyzed using natural language processing (NLP) techniques, including word cloud analysis, frequency analysis, and topic modeling. The results indicate that the architectural language of biophilic design exhibits greater detail and clarity after architects utilize text-to-image AI. Nevertheless, in some instances, the language used to describe biophilic design is also constrained by the images generated by the text-to-image AI that the architects observe.