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Author: search.filters.author.Sirisakdi, LimpasilpSubject: search.filters.subject.mindfulness

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    A Systematic Review of Architectural Atmosphere That Fosters Mindfulness Constructs
    (2025-07-01)
    Thampanichwat, Chaniporn
    ;
    Sirisakdi, Limpasilp
    ;
    Petsirasan, Sippakorn
    ;
    Wutisun, Duangkamon
    ;
    Singkham, Sathirat
    This study explores how architectural atmosphere can foster mindfulness constructs in response to the growing mental health crisis. Mindfulness, known for improving mental health, reducing stress, and enhancing overall well-being, is increasingly recognized as a potential solution to mental health challenges. However, research on how architectural atmosphere supports mindfulness is limited. This study systematically reviews architectural atmosphere features that promote mindfulness constructs, which includes awareness, openness, attention, focus, connection, and calmness. A literature review was conducted using the Scopus database, following PRISMA guidelines for transparency. Fifty-three articles were selected, focusing on mindfulness features in architectural atmosphere: awareness (4), openness (1), attention (28), focus (5), and connection (15). No studies were found on architectural atmosphere fostering calmness. The findings suggest that architectural atmosphere plays a significant role in supporting mindfulness, but further empirical studies are needed to validate these results in real-world contexts.
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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.