Mindful Architecture from Text-to-Image AI Perspectives: A Case Study of DALL-E, Midjourney, and Stable Diffusion

Abstract

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.

1. Introduction

Global warming is becoming increasingly critical, as reflected in the yearly rise in the Earth’s average temperature [1]. Extreme climate conditions are not only worsening the state of our planet’s health but also harming the physical health [2,3,4] and mental health of people worldwide [5,6,7]. A growing body of evidence attributes these challenges to human behaviors [8,9,10,11].

Mindfulness is a psychological mechanism that guides individuals toward sustainable behaviors [12,13]. A 2022 study demonstrated that mindfulness influences customer decision-making regarding water conservation [14]. A 2019 survey identified that mindfulness helps consumers reduce materialism while also enhancing their sustainable lifestyles and promoting the purchase of socially and environmentally friendly products [15]. A 2011 study found that mindfulness is significantly positively correlated with mindful consumption [16].

In addition to its benefits in promoting sustainable behaviors that can help address the global health challenges we face, numerous studies have shown that mindfulness also appears to be a solution to the physical [17,18] and mental health [19,20,21,22] issues affecting individuals impacted by global warming. The benefits of mindfulness are key factors driving this study to explore strategies for promoting it.

Since mindfulness is a psychological mechanism of humans [12,13], and previous studies have shown that architecture can influence human psychology [23,24], it follows that architecture can undoubtedly promote a state of mindfulness without requiring a trait of mindfulness or repeated mindfulness practice, as previously understood [25]. Thus, this study focuses on identifying architectural designs that potentially promote mindfulness, which is called mindful architecture.

A systematic literature review published in 2023 compiled publications highlighting the characteristics of mindful architecture [25]. The study collected data from Scopus, Google Scholar, and ThaiJO from 1983 to the end of September 2022 and found that only eight publications provided information on mindful architecture. Based on the findings from these eight publications, the architectural characteristics identified as fostering mindfulness will be synthesized in the following section.

1.1. The Characteristic of Mindful Architecture in the Current Publications

The first document that addresses mindful architecture was published in 2013 [26], outlining that architecture conducive to mindfulness should incorporate geometric structures in terms of mass and form, along with synesthetic elements such as light, color, and sound.

The remaining seven papers indicate that architecture promoting mindfulness is related to three architectural concepts: biophilic design [27,28,29], traditional Japanese architecture [30], and Buddhist contemplative spaces [31,32,33]. The characteristics of the three architectural concepts that influence mindfulness are as follows:

Biophilic design has been identified as one of the architectural concepts that promote mindfulness, primarily green mindfulness [27,28,29]. The characteristics of biophilic design architecture in this context include fascination with biophilic design, affiliation with biophilic design, exposure to nature, and connection to nature. The characteristics of light include natural light, daylight on walls, light from outside, white light, spotlight, light rays, the transition from darkness to brightness, and the interplay of light and shadow. In terms of color, the discussion includes color rays and hue. The materials include concrete or stone, wood texture, highly tactile surfaces, clay, and natural materials. The objects in biophilic design architecture include images of nature, words that signify a connection to nature, and signs that evoke a connection to nature. The views include plants, greenery, natural views, trees, river views, rainwater, and immersive water features. In terms of sound, it refers to amplified sound.

Traditional Japanese architecture [30] that promotes mindfulness must consist of forms characterized by “Ma”, which means in-betweenness or adjustable spaces. The movement, materials, and objects are designed under the concept of Michiyuki, which translates to “journey” and emphasizes considering perspectives from multiple viewpoints. The “Yugen” lighting design interplays with light and shadow to create a sense of obscurity. The color represents “Utsuroi”, the changing of time. Views of the building reveal “Hashi”, the connection between outdoor and indoor spaces, and again “Utsuroi”, the visibility of colored leaves changing with the seasons.

The Buddhist contemplative space, which influences mindfulness [31,32,33], identifies forms characterized by a calm and welcoming atmosphere. These spaces feature spatial elements that reflect the Buddhist atmosphere, along with the presence of natural light. Colors that affect mindfulness include the colorful designs in the traditional Tibetan style, the simplicity found in the Zen style, and the cool-tone spaces created for meditation. The objects in this concept include the Trick Lotus Jetiya in Thailand, the basement of Chedi in Chiang Sean, paintings of nature, images of nature, and focus objects. This architectural design allows people to see gardens, woodlands, forests, wild green areas, natural views, unobstructed views, and animals. Finally, in terms of sound, it includes quiet, natural sounds, meditation bells, and Zen music.

Several studies indicate that the architectural atmosphere is a crucial architectural element that fosters mindfulness within buildings [26,29,34]. The architectural atmosphere comprises the following elements: form, space, movement, light, color, material, object, view, sound, and weather [34,35,36,37,38,39]. Based on previous studies, architectural features that can promote mindfulness through the components of architectural atmosphere can be summarized as shown in

Table 1. Architectural atmosphere features supporting mindfulness from previous studies.

Component	Characteristics
Form	Geometric structure [26], fascination with biophilic design [26], affiliation with biophilic design [27], “Ma”, which means in-betweenness or adjustable spaces [30], calm atmosphere, and welcoming atmosphere [33]
Space	Exposure to nature [28], connection to nature [29], in-betweenness or adjustable space [30], and reflection of the Buddhist atmosphere [33]
Movement	Michiyuki: perspectives from multiple viewpoints [30]
Light	Synesthetic [26], natural light [27,29,31], daylight on wall [28], light from outside [28], white light [29], spotlight [29], light rays [29], transition from darkness to light [29], light and shadow [29], and Yugen: interplay between light and shadow to create a sense of obscurity [30]
Color	Synesthetic [26], color rays [29], hue [29], Utsuroi: the changing of time [30], colorful designs in the traditional Tibetan style [33], simplicity in the Zen style [33], and cool-tone spaces created for meditation [33]
Material	Natural materials [27], concrete [29], stone [29], clay [29], wood texture [29], highly tactile surfaces [29], and Michiyuki: perspectives from multiple viewpoints [30]
Object	Images of nature [28], words that signify a connection to nature [28], signs that evoke a connection to nature [28], Michiyuki: perspectives from multiple viewpoints [30], paintings of nature [33], focus objects [33], the Trick Lotus Jetiya in Thailand [31], and the basement of Chedi in Chiang Sean [32]
View	Natural views [27,28,33], plants, greenery [27], trees, river views [27,33], rainwater [29], immersive water features [29], Hashi: connection between outdoor and indoor spaces [30], Utsuroi: the visibility of colored leaves changing with the seasons [30], gardens [33], woodlands [33], forests [33], the wild [33], unblocked views [33], and animals [33]
Sound	Synesthetic [26], amplified sound [29], quiet environments [33], natural sound [33], meditation bell [33], Zen music [33]
Weather	None [25]

.

Not only does this systematic literature review highlight a significant educational gap between architecture and mindfulness [25], but other related studies also indicate that the characteristics of mindful architecture remain unclear [40,41]. However, recent studies have unveiled new solutions that may enable architects to envision architecture that fosters mindfulness more clearly, which will be discussed in the following section.

1.2. The Role of Text-to-Image AI Perspective in Architectural Visualization

Previous research has shown that text-to-image AI enables architects to conceptualize new architectural ideas more clearly [42]. Text-to-image AI plays a crucial role in providing new perspectives, showcasing possibilities, and expanding the creativity of architects by visualizing images from large datasets [43,44,45]. Several major architecture firms have started using text-to-image AI to create conceptual designs for architecture that seem to emerge out of nowhere [45,46,47]. Hence, text-to-image AI can assist architects in visualizing mindful architecture more clearly, making previously ambiguous characteristics more explicit.

The mainstream text-to-image AI models utilized for generating architectural images include DALL-E, Midjourney, and Stable Diffusion [48,49,50]. Thus, these three text-to-image AI models are particularly interesting in this study, as they may help clarify the characteristics of mindful architecture. However, the potential and limitations of DALL-E, Midjourney, and Stable Diffusion have been studied and are described as follows:

DALL-E is capable of generating images that are relatively realistic and detailed, particularly in the context of architectural imagery [43,51,52,53]. However, despite its impressive output, the images often display a lack of completeness, with certain elements or aspects not fully realized or developed [48].

In the case of Midjourney, it has gained significant popularity among artists due to its ability to generate visually striking and often surrealistic images [43,54]. At the same time, Midjourney also excels at generating architectural imagery, responding effectively to user prompts with a remarkable level of creativity and detail [48].

Stable Diffusion exhibits notable differences from the other three well-known text-to-image AI models. This is due to its support for various commands, such as sampling type, output image dimensions, and seed value [55]. However, previous research has found that Stable Diffusion cannot generate architecture in specific desired styles [48].

The literature review above identifies some limitations of using text-to-image AI for generating architectural images. Nevertheless, it also highlights the potential of this technology to create visual representations of architecture that remain as new concepts that still require images for better explanation. The key features and limitations of text-to-image AI in architectural visualization can be summarized as shown in

Table 2. Key features of text-to-image AI in architectural visualization.

Text-to-Image AI	Key Feature and Limitation
DALL-E	Realistic [43], incomplete in architectural imagery [48]
Midjourney	Surrealistic [54], popular for artists [43], excels in architectural images [48]
Stable Diffusion	Adjustable [55], unable to create architecture in specific styles [48]

.

While sustainability challenges arise from human behavior, mindfulness can encourage individuals to adopt more sustainable practices. Architecture has the potential to promote mindfulness instantly; however, the characteristics of architecture that foster mindfulness remain unclear. Text-to-image AI will likely assist architects in more clearly visualizing new architectural concepts.

To address the educational gap, this research focuses on identifying the characteristics of mindful architecture from the perspectives of the three text-to-image AI models, namely DALL-E, Midjourney, and Stable Diffusion. The research question for this study is as follows: How does each popular text-to-image AI model—DALL-E, Midjourney, and Stable Diffusion—represent the characteristics of mindful architecture?

Therefore, the structure of this article is as follows: Section 1 discusses the background and significance of this study, as well as the research objectives, which lead to the key research question—“How does each popular text-to-image AI model—DALL-E, Midjourney, and Stable Diffusion—represent the characteristics of mindful architecture?”—as previously mentioned. Section 2, therefore, discusses the details and rationale behind the design of the research methodology. Section 3 presents the results obtained from the preceding methodology, specifically reporting how DALL-E, Midjourney, and Stable Diffusion represent the characteristics of mindful architecture. Section 4 is the discussion of the results, where the findings are analyzed to determine whether they align with previous research. Finally, Section 5 provides the conclusion, summarizing the key outcomes of the study.

2. Methodology

The research methodology has been designed in three steps. First, the Database Section includes details on the methods used to obtain image data of mindful architecture generated by DALL-E, Midjourney, and Stable Diffusion. The second section, Data Coding, provides details on the methods used to decode the images of mindful architecture generated by DALL-E, Midjourney, and Stable Diffusion. Lastly, the Data Analysis Section explains the methods used to analyze the data in order to understand how each popular text-to-image AI model—DALL-E, Midjourney, and Stable Diffusion—represents the characteristics of mindful architecture. These three steps can be represented in a diagram, as shown in Figure 1.

The next step involves detailing the methodology for data collection from the database. Specifically, it entails gathering images generated by three text-to-image AI models—DALL-E, Midjourney, and Stable Diffusion—for use in this research.

2.1. Database

To investigate mindful architecture from the perspective of text-to-image AI, this study began by generating images using all three models—DALL-E, Midjourney, and Stable Diffusion [48,49,50]—as previously mentioned. Each model was employed to generate images a total of 10 times to yield 10 representative images. Given that the three text-to-image AI models operate differently, the architect, having experimented with creating architectural imagery using AI, used distinct prompts for each model. To ensure that prior knowledge or biases regarding mindful architecture did not influence this stage, the selected architect had no prior involvement in mindfulness-related research and had never designed architecture associated with mindfulness.

For DALL-E, the architect responsible for creating images found that using the same prompts produced images with similar characteristics. Therefore, image generation with DALL-E was performed differently by employing multiple prompts. In the first instance, the image generator utilized the prompts: mindful architecture, mindfulness architecture, architecture fostering mindfulness, and exterior perspective. Subsequently, an interactive prompt was employed, specifically to “redo with different styles of architecture”. Subsequently, the prompts “again”, followed by mindful architecture, mindfulness architecture, architecture fostering mindfulness, exterior perspective, hyper-realistic style render, and impact scenery were utilized. This was followed by the prompts: mindful architecture, mindfulness architecture, architecture fostering mindfulness, exterior perspective, hyper-realistic style render, impact scenery, sustainable material, and 1-story architecture. To obtain different images, the prompts “make it with curvy style architecture”, “make it more simple and calm”, and “more simple temple-like” were applied in succession. The prompts in the last two instances were “the simplified, temple-like mindful architecture with subtle cultural details” and “redo again with full exterior view”.

Midjourney can generate new and different images using the same prompts. The architect responsible for generating images chose a single set of prompts: mindful architecture, mindfulness architecture, architecture fostering mindfulness, architecture of mindfulness, and exterior perspective. However, the distinguishing factor between this model and the other two text-to-image AI models is its capability to generate four images simultaneously. The architect in question thus selected one image from the four produced in each of the ten instances to serve as the representative image for this study.

Similar to Midjourney, Stable Diffusion is capable of generating images in diverse forms using the same prompts. Thus, the architect employed a single set of prompts for Stable Diffusion to generate imagery of mindful architecture: mindful architecture, mindfulness architecture, architecture fostering mindfulness, and exterior perspective. The difference from Midjourney is that Stable Diffusion generates one image at a time, allowing the architect to directly utilize the images produced in all ten instances for this experiment. This can be summarized in the prompts used for all three AI models, as shown in

Table 3. Prompts for generating mindful architecture images using text-to-image AI.

Text-to-Image AI	Prompts
DALL-E	Mindful architecture, mindfulness architecture, architecture fostering/of mindfulness, and exterior perspective; redo with different styles of architecture; again; mindful architecture, mindfulness architecture, architecture fostering/of mindfulness, exterior perspective, hyper-realistic style render, impact scenery; mindful architecture, mindfulness architecture, architecture fostering/of mindfulness, exterior perspective, hyper-realistic style render, impact scenery, sustainable material, 1-story architecture; make it with curvy style architecture; make it more simple and calm; more simple temple-like; the simplified, temple-like mindful architecture with subtle cultural details; redo again with full exterior view
Midjourney	Mindful architecture, mindfulness architecture, architecture fostering/of mindfulness, exterior perspective
Stable Diffusion	Mindful Architecture, mindfulness architecture, architecture fostering/of mindfulness, exterior perspective

.

In summary, through image generation using text-to-image AI, 10 images were obtained from each model—DALL-E, Midjourney, and Stable Diffusion—resulting in a total of 30 images for the subsequent process, as shown in Appendix A.

2.2. Data Coding

The second step involved decoding the description of the characteristics of mindful architecture from the images obtained in the previous step. Content analysis was employed to examine how the 30 images obtained from the previous stage represented the characteristics of mindful architecture from text-to-image AI perspectives. It was chosen because it ensures structured analysis, enhances reliability, promotes replicability, and reduces bias in the research process [56,57]. This method is often used to describe, analyze, and interpret the meanings within the images in verbal form [56].

During the content analysis stage, an open-ended questionnaire was used to gather all the words describing the characteristics of mindful architecture generated by the three text-to-image AI models. The questions in the questionnaire were designed based on the literature review [58]. In the study of mindful architecture, its characteristics have been categorized into ten groups: form, space, movement, light, color, material, object, view, sound, and weather. Therefore, the questionnaire included the following question: “How would you describe the architecture depicted in the image in terms of form, space, movement, light, color, material, object, view, sound, and weather?”

Three architects performed this step to enhance the validity and credibility of the results obtained from this process, a method commonly called triangulation in research [57]. The three architects involved in this phase were selected based on their licensing as professionals and prior architectural design experience, ensuring they were well versed in architecture. All three individuals were recent graduates with a bachelor’s degree in architecture, having completed their studies no more than a year ago, and were still very familiar with architectural academic language.

Additionally, each architect selected for the study was required to have no previous involvement in research on mindful architecture or in designing related architectural projects. This criterion was established to ensure that all the words collected in this stage reflected the architects’ genuine consideration of AI-generated architectural images. This approach aimed to answer what mindful architecture looks like when created by text-to-image AI. To ensure that all responses from the three architects were not influenced or biased by familiarity with mindful architecture or pre-existing vocabulary, the architects were required to have no prior exposure to this concept. This ensured that the words collected reflected their interpretation of the images without external influences or preconceived notions.

Since the architects’ responses were initially provided in Thai, all textual data were translated from Thai to English to ensure consistency and accuracy across the dataset for the subsequent steps.

2.3. Data Analysis

The final step involved interpreting the language derived from the previous step in order to summarize the characteristics of mindful architecture as represented by the three text-to-image AI models. To systematically extract meaningful patterns from the keywords, several natural language processing techniques were applied. Natural language processing is a subfield of artificial intelligence (AI) that focuses on interactions between computers and human language [59]. The goal of natural language processing is to enable machines to understand, interpret, and generate human language meaningfully [59]. Natural language processing combines computational linguistics with machine learning and deep learning to process and analyze large amounts of natural language data, such as text or speech.

Natural language processing encompasses a wide range of tasks that allow machines to interact with human language. These tasks include text cleaning and preprocessing (e.g., removing unwanted characters or punctuation), tokenization (breaking down text into smaller components like words or sentences), and more advanced operations like part-of-speech tagging, syntactic parsing, and sentiment analysis [60]. Natural language processing is essential for tasks such as machine translation (e.g., Google Translate), speech recognition (e.g., Siri or Alexa), and information retrieval (e.g., search engines). It also plays a vital role in text-based analysis, allowing researchers to extract patterns, trends, and insights from vast amounts of unstructured text data [61].

In this study, natural language processing was used to process the architects’ textual descriptions of AI-generated architectural images. The primary tools for this task were the Natural Language Toolkit (nltk) package [59] and the Scikit-learn package [62] in Python 3 [63]. The data preprocessing process included cleaning the text by removing extraneous characters, numbers, and punctuation, as well as standardizing the text by converting it to lowercase [64]. This prepared the dataset for subsequent keyword extraction and analysis by eliminating noise in the data and ensuring that the analysis focused solely on the architectural keywords. Once the cleaning function was applied to every entry in the dataset, a structured and clean version of the data was obtained, featuring essential architectural atmospheres such as form, space, movement, light, color, material, object, view, sound, and weather.

2.3.1. Word Cloud Generation

Word cloud generation is a simple yet powerful data visualization technique often used in text analysis to depict the frequency of words in a dataset. In a word cloud, words are visually represented in a cluster or cloud-like formation, with the size of each word corresponding to its frequency of occurrence in the text [65]. The more frequently a word appears in the dataset, the more prominently it is displayed in the word cloud. Less frequent words are shown in smaller sizes [65]. This allows for quick visual identification of the most common words in a text.

Word clouds are beneficial for providing a high-level overview of the key themes or topics in a dataset without needing to read through all the text. By instantly highlighting the most frequent words, they offer a snapshot of the dominant content or concepts being discussed. Word clouds are often used as a first step in text analysis because they provide an intuitive, at-a-glance understanding of the data, making them accessible to a broad audience, including those unfamiliar with more complex statistical analyses [66].

Beyond basic frequency analysis, our approach integrated an examination of the different proceedings—or processes—associated with the various words used. We investigated how certain keywords related to distinct aspects of the architectural design process. For example, “light” and “space” can be associated with both aesthetic considerations and functional spatial planning, while “natural” might correspond to sustainable design practices and the integration of organic materials. By mapping these words to specific proceedings such as conceptual development, spatial organization, material selection, and environmental integration, we gained a deeper, more contextual understanding of the architects’ priorities and methodologies.

Before generating a word cloud, the text is preprocessed by removing extraneous elements such as punctuation, numbers, and common stopwords (e.g., “the”, “and”, “is”). This ensures that the word cloud focuses on the text’s most meaningful and content-rich words. After cleaning the text, the algorithm counts the frequency of each word. Words that appear more often in the dataset are assigned a higher frequency value. The words are then plotted in the word cloud, with their size and prominence reflecting their frequency. More frequent words appear more prominent, while less frequent words appear smaller. The placement of words in the cloud is often randomized or arranged to fit a predetermined space (e.g., square, oval) within the visualization.

In this research, word cloud generation was employed to visualize the architects’ keyword descriptions of AI-generated architectural designs. By generating a word cloud, we were able to quickly identify which architectural elements were mentioned most frequently in relation to AI-driven mindfulness design. For example, if words like “light”, “space”, and “natural” appeared prominently in the word cloud, it would indicate that these concepts were central to the descriptions provided by the architects. Word clouds are an effective tool for providing an initial understanding of text data and revealing patterns in a visually engaging format.

2.3.2. Word Frequency Analysis

Word frequency analysis is a more quantitative method of analyzing text, focusing on the number of times each word or term appears in a given dataset [67]. This technique goes beyond mere visualization by calculating and explicitly stating how often each word is used in the text [68]. Word frequency analysis provides a structured way to examine the prominence of different terms and helps uncover the most essential or recurring themes in the dataset.

The process of conducting word frequency analysis involves data cleaning, similar to word cloud generation. The cleaned text is then processed to count the number of times each word appears. This results in a frequency distribution where each unique word is associated with its corresponding count. Once the words have been counted, they can be ranked from most frequent to least frequent. This provides a clear view of the most commonly occurring terms in the dataset [68].

Word frequency analysis is particularly valuable because it provides exact numerical data on word occurrences, allowing researchers to make more objective comparisons and draw clearer conclusions about the text. Unlike word clouds, which provide a more qualitative, visually oriented representation, word frequency analysis yields precise information about the prominence of specific terms, making it a key tool in text-based research.

In this study, word frequency analysis was used to complement the word cloud visualization by providing exact figures on how often specific architectural terms appeared in the architects’ descriptions of AI-generated images. We were able to quantify the occurrence of key architectural concepts, making it easier to compare how often certain features, such as “light” or “space”, were discussed across different AI models. The frequency analysis provided a clear, quantitative reinforcement of the insights gleaned from the word cloud, offering a more precise view of the architectural trends reflected in the dataset.

2.3.3. Topic Modeling Analysis

Topic modeling is a technique used in natural language processing techniques to automatically discover the hidden thematic structure in a large collection of text data [69]. It is an unsupervised machine learning method, meaning it does not rely on pre-labeled data, and its primary objective is to identify abstract topics that occur across a corpus of documents [69]. Each topic consists of a group of words that frequently appear together, and these topics represent overarching themes or concepts present in the text.

Topic modeling allows researchers to uncover the latent structure in textual data by organizing words into meaningful clusters based on their statistical co-occurrence [70]. This technique is especially useful when dealing with large datasets, as it provides a way to summarize and make sense of the data without having to read through each document manually. The topics generated by the model can offer insights into patterns, themes, and trends within the data that might not be immediately obvious from simple keyword analysis. In this research, the latent Dirichlet allocation model was applied to the architects’ keywords describing AI-generated images to extract recurring architectural themes.

Latent Dirichlet allocation is one of the most widely used algorithms for topic modeling [70]. Latent Dirichlet allocation is a probabilistic model that assumes each document (in this case, a set of keywords describing an AI-generated image) is composed of a mixture of topics, and each topic is characterized by a distribution of words [71]. In simpler terms, latent Dirichlet allocation assumes that within a given text, there are multiple hidden topics, and each word in the text is generated from one of these topics with a certain probability.

Latent Dirichlet allocation works by analyzing the co-occurrence of words within the dataset and clustering them into topics based on their patterns of occurrence [72]. The algorithm assigns probabilities to each word belonging to a certain topic, which allows it to determine which words are most likely to appear together in a given topic [72]. Similarly, it assigns probabilities to each document (or image description in this study) comprising various topics. The result is a model where each topic is defined by a set of words, and each document is represented as a mixture of these topics [73].

For example, if latent Dirichlet allocation is applied to a set of architectural descriptions, one topic might include words such as “light”, “natural”, “transparent”, and “open”, representing a theme related to natural lighting and open space design. Another topic might contain words like “structure”, “concrete”, and “solid”, representing a theme around robust and industrial materials. Latent Dirichlet allocation’s strength lies in its ability to reveal these hidden themes and provide a structured view of the data.

By applying latent Dirichlet allocation, we were able to extract distinct topics that represented different architectural aspects emphasized by the AI models. This not only helped to uncover deeper patterns within the dataset but also provided valuable insights into how these AI models might prioritize certain design elements when creating images of architectural styles that promote mindfulness. By organizing keywords into topics, the latent Dirichlet allocation model revealed patterns of architectural design across the dataset, showing how different descriptors of form, space, light, and material were grouped together. The use of latent Dirichlet allocation enabled us to move beyond surface-level keyword frequency analysis and explore the relationships between different architectural features, offering a richer and more nuanced understanding of the data.

After completing the three steps outlined above, the analysis focused on words that appeared prominently in the word cloud, frequently in the word frequency analysis, and significantly in the topic modeling analysis to synthesize the final results of this study. The results obtained can explain how each popular text-to-image AI model—DALL-E, Midjourney, and Stable Diffusion—represents the characteristics of mindful architecture, as will be described in the following section.

3. Results

The results answer the research question regarding the characteristics of mindful architecture generated by each popular text-to-image AI model. These results are presented separately for each model: DALL-E, Midjourney, and Stable Diffusion, as follows:

3.1. Mindful Architecture from a DALL-E Perspective

The form of mindful architecture from a DALL-E perspective is most strongly associated with “lines”, which appear eight times, suggesting that lines are a dominant feature in the representation of mindful architecture. The subsequent most frequent characteristics are “straight” lines, occurring five times, followed by “curved” and “rounded” forms, which each appear four times. Other features, such as “corners”, “square”, “smooth”, and “surface”, appear three times, showing a moderate emphasis on defined edges and smooth textures. Finally, “flat” and “soft” surfaces appear two times each, indicating a more subtle presence of these characteristics in mindful architecture. In this section, the topic modeling analysis identified the following keywords: “rounded corners”, “rounded”, “curved”, “straight”, and “lines” (

Table 4. The form of mindful architecture from a DALL-E perspective.

Word Cloud	Word Frequency

).

The space in mindful architecture from DALL-E is most associated with the term “airy”, which appears six times, suggesting that open, light-filled spaces are a key characteristic. “Wide” follows closely, appearing five times, indicating a significant emphasis on spaciousness. “Open” is mentioned three times, reinforcing the importance of unobstructed, accessible areas. Other terms such as “connection”, “cluster”, “area”, “fun”, “open outside”, “outdoor”, and “outside” each appear twice, pointing to a moderate emphasis on connectivity, outdoor spaces, and a sense of openness that extends beyond the interior. In this section, the topic modeling analysis identified the following keywords: “feel”, “separate”, “open”, “wide”, and “airy” (

Table 5. The space of mindful architecture from a DALL-E perspective.

Word Cloud	Word Frequency

).

DALL-E shows that the movement in mindful architecture is primarily described by the term “horizontal”, which appears five times. The term “parallel” is mentioned four times, emphasizing order and alignment in spatial movement. “Lines” and “calm” occur three times, pointing to a smooth, undisturbed flow and a peaceful atmosphere. Other terms such as “level”, “flowing”, “connection”, “quiet”, “soft”, and “curve” each appear twice, suggesting a moderate emphasis on fluidity, quiet transitions, and gentle, soft movements within the architecture. In this section, the topic modeling analysis revealed key terms such as “horizontal lines”, “lines”, “calm”, “parallel”, and “horizontal” (

Table 6. The movement of mindful architecture from a DALL-E perspective.

Word Cloud	Word Frequency

).

In terms of light in mindful architecture, the term “soft” appears seven times, indicating that soft lighting is a prominent characteristic. Other terms such as “soft day”, “stript”, and “direct” are mentioned four times, suggesting that varying lighting qualities, including softer and more direct forms, are also essential but less emphasized. “Light day”, “interior”, and “natural” are mentioned three times, indicating a moderate focus on natural in interior lighting. Lastly, “shadow”, “horizontal”, and “warm” each appear twice, pointing to a subtler emphasis on shadows, horizontal light placement, and warmth in the lighting design. The topic modeling analysis highlighted key terms including “natural”, “softday”, “stript”, “direct”, and “soft” (

Table 7. The light of mindful architecture from a DALL-E perspective.

Word Cloud	Word Frequency

).

Regarding color in mindful architecture in DALL-E, “gray” is mentioned eight times, suggesting that gray tones are a dominant feature. “Tone” is referenced six times. The terms “natural”, “wood”, “brown”, and “cream” each appear four times, highlighting a strong connection to natural materials and earthy, neutral hues. “Neutral”, “green”, “eyes”, and “surrounding” are mentioned three times, pointing to a moderate use of calming, nature-inspired colors and a focus on colors that blend with the surrounding environment. The topic modeling analysis highlighted key terms including “brown”, “wood”, “cream”, “tone”, and “gray” (

Table 8. The color of mindful architecture from a DALL-E perspective.

Word Cloud	Word Frequency

).

DALL-E shows that the materials most frequently identified in mindful architecture include “concrete”, mentioned 12 times, suggesting a strong presence of this material in the design. “Glass” follows closely with 11 occurrences, indicating a significant use of transparent or reflective surfaces. “Wood” is mentioned nine times, while “stone” appears eight times. The term “slats” appears six times, suggesting a moderate use of slatted surfaces. “Materials” and “iron” are each mentioned four times. “Gravel” and “wood” are referenced three times, and “tiles” appear twice, suggesting that these materials play a less prominent but still notable role in the architecture. The key terms identified through topic modeling analysis include “slats”, “stone”, “wood”, “glass”, and “concrete” (

Table 9. The material of mindful architecture from a DALL-E perspective.

Word Cloud	Word Frequency

).

The objects in mindful architecture are closely linked to “floating”, “wall”, “blocks”, “potted”, “plant”, “natural”, and “look”, each mentioned three times, indicating a strong emphasis on organic and natural elements. “Making”, “walls”, and “top”, each mentioned two times, suggest a more subtle focus on construction, boundaries, and the upper elements of the architecture. The analysis revealed significant words such as “natural”, “potted plant”, “blocks”, “look”, and “plant” (

Table 10. The object of mindful architecture from a DALL-E perspective.

Word Cloud	Word Frequency

).

In the view aspect of DALL-E’s mindful architecture, the most prominent elements are “green” and “trees”, each mentioned five times, suggesting a strong focus on nature and greenery. “Foot” and “clear” appear four times. “Nature”, “hill”, and “mountain” are mentioned three times, showing a connection to natural landscapes and topography. The terms “natural”, “lawn”, “pool”, and “hill” each appear twice, highlighting a more subtle presence of these natural and outdoor features. Important terms that emerged from the topic modeling process include “clear”, “green trees”, “foot”, “trees”, and “green” (

Table 11. The view of mindful architecture from a DALL-E perspective.

Word Cloud	Word Frequency

).

The sounds depicted in the mindful architecture images generated by DALL-E are primarily associated with “water” (13 mentions) and “flowing” (12 mentions), suggesting a dominant presence of water-related sounds. “Nature” is referenced eight times, indicating an overall natural soundscape. “Bird sounds”, “singing”, and “wind” are each mentioned five times. “Soft” appears four times, indicating a calm auditory environment, while “fluttering” and “leaves” are mentioned three times, reinforcing the presence of nature-based sounds. “Music” appears twice, suggesting a more integration of musical elements. Keywords such as “singing”, “nature”, “flowing water”, “flowing”, and “water” were prominent in the analysis (

Table 12. The sounds of mindful architecture from a DALL-E perspective.

Word Cloud	Word Frequency

).

The weather in mindful architecture is primarily described with the terms “hot” and “light”, each mentioned six times, suggesting a focus on warm and bright conditions. “Wind”, “cool”, and “sunlight” are referenced four times, indicating a moderate presence of breezy and refreshing elements, along with sunlight. “Sun”, “cloudy”, and “cool” are mentioned three times, pointing to a more balanced climate with occasional cloud cover. The terms “little”, “like”, and “humid cool” each appear twice. Topic modeling results highlighted words like “cool”, “wind”, “sunlight”, “light”, and “hot” (

Table 13. The weather of mindful architecture from a DALL-E perspective.

Word Cloud	Word Frequency

).

In sum, mindful architecture from the perspective of the text-to-image AI model DALL-E has the following characteristics: The form was found to have the characteristics of straight and curved lines. A space that provides satisfaction will have characteristics of privacy, airiness, and openness. The movement is linear and proceeds horizontally. The lighting required should be relatively soft natural light. The color found is usually a brown color of wood with a reasonably gray tone. The materials found are often those with surfaces similar to stone, wood, and glass. The objects that appear include a natural wall made up of plants. The views are often green open spaces with large trees and various plants. The desired sound is usually a natural sound, especially the sound of gently flowing water. The ideal climates are usually naturally bright, with light winds and temperatures that are neither too hot nor too cold.

3.2. Mindful Architecture from a Midjourney Perspective

The form of mindful architecture from a Midjourney perspective is most closely associated with “square”, mentioned 13 times, followed by “shape” and “sharp”, each appearing 11 times, indicating a strong focus on geometrically defined forms. “Lines” and “straight” are referenced nine times, while “box” and “concrete” are mentioned seven times, highlighting industrial solid elements. Other terms like “structure”, “solid”, and “openings” appear six, five, and four times. The topic modeling analysis highlighted key terms including “lines”, “straight”, “shape”, “sharp”, and “square” (

Table 14. The form of mindful architecture from a Midjourney perspective.

Word Cloud	Word Frequency

).

In terms of space, “connected” is the most frequently identified term, mentioned six times, followed by “interior” and “exterior” (five times each), indicating a connected relationship between inside and outside. “Natural”, “walkway”, “pool”, and “openings” are referenced four times, suggesting a strong integration with the natural environment space, while “vertical”, “garden”, and “horizontal” are mentioned three times, pointing to varied spatial dimensions. The key terms identified through topic modeling analysis include “walkway”, “interior exterior”, “interior”, “exterior”, and “connected” (

Table 15. The space of mindful architecture from a Midjourney perspective.

Word Cloud	Word Frequency

).

Movement in mindful architecture from Midjourney is characterized by the term “movement”, mentioned 10 times, with “connection” and “continuity” following at 5 and 4 times, respectively, emphasizing fluid transitions and harmony. “Walkways” and “horizontal” appear three times, reinforcing movement along clear linear paths, while terms like “reflection”, “exterior”, “linearity”, “level”, and “interior” are mentioned two times. The analysis revealed significant words such as “horizontal movement”, “horizontal”, “continuity”, “connection”, and “movement” (

Table 16. The movement of mindful architecture from a Midjourney perspective.

Word Cloud	Word Frequency

).

In terms of light, “shadow” appears most frequently with 10 mentions, followed by “reflection” and “soft” at 9 times, indicating a focus on gentle and reflective light qualities. “Water” is referenced six times, suggesting a connection to liquid or reflective surfaces, while “dark”, “sunlight”, and “opening” are mentioned four times, pointing to a balance of light and shadow. “Trees”, “natural”, and “glass” each appear three times, highlighting the influence of nature and transparency on the lighting experience. Important terms that emerged from the topic modeling process include “soft shadow”, “water”, “reflection”, “soft”, and “shadow” (

Table 17. The light of mindful architecture from a Midjourney perspective.

Word Cloud	Word Frequency

).

For colors, “tones” is the most frequently mentioned term (nine times), followed by “concrete” (eight times), suggesting a focus on neutral color schemes. “Natural” and “gray” are each referenced seven times, with “green”, “plants”, and “brown” mentioned five, four, and three times, respectively, pointing to earth tones and nature-inspired colors. “Black”, “cream”, and “gold” each appear twice, suggesting a more restrained use of accent colors. Keywords such as “green”, “natural”, “gray”, “concrete”, and “tones” were prominent in the analysis (

Table 18. The color of mindful architecture from a Midjourney perspective.

Word Cloud	Word Frequency
.

).

The materials in mindful architecture by Midjourney are primarily associated with “concrete”, mentioned 12 times, followed by “glass” (11 times) and “stone” (10 times), indicating a strong emphasis on solid, durable, and transparent materials. “Wood” appears four times. Terms like “concrete concrete”, “concrete exposed”, “natural”, “water exposed”, “exposed”, and “clear” are mentioned three times, emphasizing both raw and transparent material expressions. Topic modeling results highlighted words like “concrete glass”, “wood”, “stone”, “glass”, and “concrete” (

Table 19. The material of mindful architecture from a Midjourney perspective.

Word Cloud	Word Frequency

).

In terms of objects from the Midjourney perspective, “stone” is mentioned six times, suggesting a solid, grounded presence. “Wooden”, “plants”, “walkway”, and “small” are referenced five times, highlighting a connection to nature and a more intimate scale. “Furniture” and “pond” are mentioned four times, with “stones”, “pool”, and “shrubs” appearing three times, indicating a focus on both natural and functional elements in the design. The analysis identified terms like “small”, “wooden”, “plants”, “walkway”, and “stone” (

Table 20. The object of mindful architecture from a Midjourney perspective.

Word Cloud	Word Frequency

).

The view in mindful architecture is most strongly associated with “natural” (13 mentions), followed by “pond” (11 mentions) and “trees” (7 mentions), showing a prominent connection to nature. “Vegetation” and “quiet” appear four times, while “atmosphere”, “stones”, “area”, “garden”, and “large” are referenced three times, reflecting a peaceful, green, and spacious environment. The topic modeling analysis highlighted key terms including “quiet”, “vegetation”, “trees”, “pond”, and “natural” (

Table 21. The view of mindful architecture from a Midjourney perspective.

Word Cloud	Word Frequency

).

The sounds associated with the architecture are predicted to include “water” (12 mentions), followed by “wind” (11 mentions) and “soft” (10 mentions), suggesting a nature-based soundscape. “Blowing”, “quietness”, and “trees” are mentioned seven times, while “nature” appears five times, emphasizing peaceful, natural sounds. “Leaves” is mentioned three times, and “birds” and “chirping” are each mentioned twice, pointing to sounds of life. The key terms identified through topic modeling analysis include “quietness”, “soft water”, “soft”, “wind”, and “water” (

Table 22. The sound of mindful architecture from a Midjourney perspective.

Word Cloud	Word Frequency

).

The weather is characterized by “cool” (10 mentions), followed by “sunlight” and “soft” (9 mentions each), indicating a refreshing and gentle atmosphere. “Wind” appears seven times, while “quiet” and “relaxing” are mentioned three times, suggesting a calm and soothing weather environment. “Cool cool”, “fog cool”, “sky”, and “morning” are referenced two times, pointing to specific weather conditions that contribute to the morning ambiance. The analysis revealed significant words such as “wind”, “sunlight”, “soft sunlight”, “soft”, and “cool” (

Table 23. The weather of mindful architecture from a Midjourney perspective.

Word Cloud	Word Frequency

).

Midjourney represents mindful architecture from the perspective of text-to-image AI. The forms found are structural parts or large glass panels with sharp lines. The resulting space must have a connection between the interior and exterior of the building in the form of a courtyard. Internal movement occurs through continuous horizontal corridors connected to vertical links. Proper lighting uses soft, natural light through glass or reflections from surfaces. Coloring often uses dark tones such as wood brown, gray, and black. Materials commonly found are concrete, wood, stone, and glass. The objects found are often floors made of materials such as glass, wood, and stone. Ideal views are often peaceful green spaces with large trees. The desired sound is usually the sound of natural wind or trees and quietness. The desired climate is usually a quiet, relaxing atmosphere with natural light and a breeze.

3.3. Mindful Architecture from a Stable Diffusion Perspective

The form of mindful architecture from a Stable Diffusion perspective is characterized by a strong emphasis on “structure” (11 mentions), followed by “lines” (10 mentions) and “glass” (9 mentions), indicating a focus on precise, organized forms and transparency. Terms like “large” and “sharp” (eight mentions each), “shape” and “openings” (seven mentions each), as well as “floating” (six mentions) and “box” (five mentions), reflect diverse shapes and openness in architectural form. “Flat” appears three times, hinting at a more straightforward, streamlined form. The topic modeling analysis highlighted key terms including “sharp”, “large”, “glass”, “lines”, and “structure” (

Table 24. The form of mindful architecture from a Stable Diffusion perspective.

Word Cloud	Word Frequency

).

In terms of space, the concept of being “connected” is most prominent, appearing 10 times, followed by “interior” (9 mentions) and “exterior” (7 mentions), emphasizing the integration and relationship between inside and outside spaces. The terms “floating” and “courtyard” (six mentions) reflect spaces that promote openness and fluidity, while “balcony” (five mentions) and “open” (four mentions) contribute to the overall spacious, open environment. The key terms identified through topic modeling analysis include “connected interior”, “courtyard”, “exterior”, “interior”, and “connected” (

Table 25. The space of mindful architecture from a Stable Diffusion perspective.

Word Cloud	Word Frequency

).

Regarding movement from a Stable Diffusion perspective, “horizontal” and “corridor” (eight mentions) suggest an emphasis on linear movement, while “inside” (six mentions) highlights interior movement. “Vertical” and “continuity” (five mentions), as well as terms like “smoothness”, “outside”, and “outdoor” (four mentions), show a connection to the natural movement and flow. “Walkway” and “change” (three mentions) further emphasize transitions in space. The topic modeling analysis highlighted key terms including “vertical”, “continuity”, “inside”, “corridor”, and “horizontal” (

Table 26. The movement of mindful architecture from a Stable Diffusion perspective.

Word Cloud	Word Frequency

).

In terms of lighting, “glass” (11 mentions) is commonly depicted in Stable Diffusion, reflecting transparency, while “soft” (10 mentions) suggests a gentle, ambient light. “Sunlight” (seven mentions) and “reflection” (six mentions) point to lighting effects, while “light natural” (five mentions) reinforces a connection to nature. “Shadow” (four mentions) and terms like “water”, “warm”, “trees”, and “sun” (three mentions each) create a varied light atmosphere. The key terms identified through topic modeling analysis include “reflection”, “soft sunlight”, “sunlight”, “soft”, and “glass” (

Table 27. The light of mindful architecture from a Stable Diffusion perspective.

Word Cloud	Word Frequency

).

Color-wise, “gray” (10 mentions) is the most prominent, with “brown” (7 mentions) and “black” (4 mentions) contributing to a neutral, earthy palette. The terms “wood” and “tone” (three mentions) add a natural touch, while “concrete”, “natural”, “green concrete”, “gray wood”, and “gray concrete” (two mentions each) reflect material-based colors. The analysis revealed significant words such as “gray brown”, “wood”, “black”, “brown”, and “gray” (

Table 28. The color of mindful architecture from a Stable Diffusion perspective.

Word Cloud	Word Frequency

).

Materials in the mindful architecture from Stable Diffusion include “glass” (nine mentions), “wood” (seven mentions), and “stone” (four mentions), indicating a balance of natural and transparent elements. Terms like “stone concrete” (three mentions), “concrete”, “concrete concrete”, and “steel” (three mentions) show the use of strong, durable materials. “Wood concrete” (two mentions) and “lines” (two mentions) further reinforce natural textures. Important terms that emerged from the topic modeling process include “concrete”, “glass wood”, “stone”, “wood”, and “glass” (

Table 29. The material of mindful architecture from a Stable Diffusion perspective.

Word Cloud	Word Frequency

).

In terms of objects, “floor” (10 mentions) is the most significant, followed by “glass” (9 mentions) and “wooden” and “stone” (8 mentions), which emphasize natural materials. “Large”, “walkway”, and “inside” (six mentions) reflect scale and movement, while “pond” (four mentions) contributes to a natural environment. “Furniture” and “orderly square” (two mentions each) suggest functional, orderly arrangements. Keywords such as “wooden floor”, “stone”, “wooden”, “glass”, and “floor” were prominent in the analysis (

Table 30. The object of mindful architecture from a Stable Diffusion perspective.

Word Cloud	Word Frequency

).

The view in mindful architecture in Stable Diffusion is most closely linked to “trees” (11 mentions), followed by “large” (7 mentions) and “peaceful” (6 mentions), highlighting natural, tranquil surroundings. Terms like “pond” and “green” (five mentions each) reflect greenery and water, while “grass” and “reflecting” (four mentions) suggest calm, reflective environments. “Shady”, “open”, and “garden” (three mentions) further contribute to this peaceful, nature-filled view. Topic modeling results highlighted words like “green”, “peaceful”, “large”, “large trees”, and “trees” (

Table 31. The view of mindful architecture from a Stable Diffusion perspective.

Word Cloud	Word Frequency

).

Sounds in mindful architecture are mainly linked to “quietness” and “trees” (10 mentions each), with “natural” (8 mentions) underscoring the calm, nature-based soundscape. “Wind” and “water” (seven mentions) add natural sound elements, while “pond” and “flowing” (four mentions) evoke calm, flowing water sounds. “Inner”, “blowing”, and “quiet” (three mentions each) reinforce a sense of calmness and tranquility. The analysis identified terms like “wind”, “natural”, “trees quietness”, “trees”, and “quietness” (

Table 32. The sound of mindful architecture from a Stable Diffusion perspective.

Word Cloud	Word Frequency

).

Finally, the weather depicted is characterized by “sunlight”, “relaxing”, “quiet”, and “breeze” (11 mentions each), indicating a serene, calming atmosphere. “Cool” (10 mentions) further suggests a refreshing environment, while “trees” and “surrounding” (9 mentions) emphasize the connection to nature. “Evening” (six mentions), “morning” (four mentions), and “soft” (two mentions) reinforce specific moments of calm and serenity in the environment. Key terms such as “breeze”, “quiet”, “quiet relaxing”, “sunlight”, and “relaxing” were also identified (

Table 33. The weather of mindful architecture from a Stable Diffusion perspective.

Word Cloud	Word Frequency

).

The depiction of mindful architecture from the perspective of the text-to-image AI model Stable Diffusion reveals the following characteristics: The shape found is a square with sharp lines. The spaces found are often connected inside and outside by walkways. Movement occurs by means of continuous horizontal connections. The lighting often involves soft light and shadows from reflections off the surface of water. The colors used are natural tones in shades of gray and green. The materials commonly found are wood, stone, glass, and concrete. The objects found are often small in size along the path, such as rocks, wood, and vegetation. Good views will have natural silence in the form of trees, plants, and ponds. The sounds produced in the area should be soft and gentle, such as the sound of wind and water. Favorable weather conditions are usually mild sunshine, a breeze, and a slightly cool temperature.

3.4. The Result of Mindful Architecture from Text-to-Image AI Perspectives

This section summarizes the decoded characteristics of mindful architecture as generated by DALL-E, Midjourney, and Stable Diffusion, as presented in Section 3.1, Section 3.2 and Section 3.3. It can be summarized that mindful architecture from the text-to-image AI perspective reveals the following defining characteristics: The forms found are lines that form squares with sharp edges. Airy spaces create a connection between the interior and exterior of the building. Horizontal movement helps create continuity in the interior corridors and vertical connections. Good lighting is natural light that is softened by shining through trees or reflecting off water or glass. The preferred colors are usually gray and brown based on natural materials such as wood or plants. The materials used are usually modified natural materials such as gravel, stone, wood, or glass. The objects often required are furniture, floors, and walkways made of wood and glass. The desired view is a natural view that provides a sense of peace in a garden area consisting of large trees. The desired sound should be a soft, natural sound, such as the sound of the wind blowing, the sound of leaves rustling, and the flow of water. The type of weather that evokes a sense of calm and relaxation is a gentle breeze.

4. Discussion

As mentioned in Section 1, although global warming is negatively affecting both the planet’s health and human well-being—physically and mentally—numerous studies have highlighted the potential benefits of mindfulness. Research indicates that mindfulness can encourage environmentally friendly behaviors while also enhancing both physical and mental health. Previous studies have shown that architectural design can cultivate a state of mindfulness. However, research on this topic remains limited. As research has shown that text-to-image AI can help architects clearly represent new architectural concepts, this study focuses on identifying the characteristics of mindful architecture from the perspectives of text-to-image AI models. The research question is as follows: How does each popular text-to-image AI model—DALL-E, Midjourney, and Stable Diffusion—represent the characteristics of mindful architecture?

To answer the research question, we generated images of mindful architecture using all three text-to-image AI models. Architects were then asked to decode how these AI models represented mindful architecture. Finally, the results were synthesized through word cloud analysis, word frequency analysis, and topic modeling to draw conclusions for this study. The findings of this study can be summarized as follows:

In summary, DALL-E represents mindful architecture through a combination of straight and curved lines, creating private, airy, and open spaces. The movement is linear and horizontal, with soft natural light and a color palette of brown wood and gray tones. The materials include stone, wood, and glass, and the objects feature natural plant walls. The views consist of green spaces with large trees, while the ideal sound is natural, such as gently flowing water. The climate is naturally bright, with light winds and mild temperatures. The characteristics consistent with previous research include soft natural light [27,29,33], stone and wood materials [29], open green spaces with visible trees [27,33], and natural sounds [33].

Midjourney represents mindful architecture with sharp structural lines and large glass panels. Spaces emphasize interior–exterior connections, often featuring courtyards and continuous horizontal corridors linked vertically. Soft natural light enters through glass and reflective surfaces. Dark tones like wood brown, gray, and black dominate the palette. Common materials include concrete, wood, stone, and glass. Views showcase peaceful green spaces with large trees, while sounds include natural wind and quietness. The ideal climate is a tranquil atmosphere with natural light and a gentle breeze. The architectural characteristics, consistent with the literature review, include courtyards that connect interior and exterior spaces [28,29,30], the representation of soft natural light [27,29,33], and using concrete, stone, and wood materials [29], as well as a quiet atmosphere and the sound of trees [33].

Stable Diffusion represents mindful architecture with square forms and sharp lines. Spaces are connected through walkways, with continuous horizontal movement. The lighting is soft, with shadows from water reflections. Colors feature natural tones of gray and green. Common materials include wood, stone, glass, and concrete. Objects along pathways are often small, such as rocks, wood, and vegetation. Views emphasize natural silence with trees, plants, and ponds. Sounds are soft and gentle, like wind and water. The ideal climate includes mild sunshine, a breeze, and a slightly cool temperature. The characteristics consistent with previous studies include square-shaped forms [26], the connection between interior and exterior spaces [28,29,30], and the reflection of light and shadows [30]. Other common characteristics include the representation of materials such as stone, wood, and concrete [29], the visibility of trees [27,28,33], and the perception of natural sounds like wind and water [33].

In summary, DALL-E provides the most detailed and abundant representation of architectural characteristics, including a variety of forms (straight and curved lines), diverse materials (stone, wood, and glass), and clear spatial qualities (private, airy, and open spaces). Additionally, it incorporates specific natural elements such as plant walls, large trees, and natural sounds like flowing water, as well as a precise climate description (light winds and mild temperatures). Based on previous studies, it can be observed that the characteristic of DALL-E is realism [43], which may suggest that realism influences the communication of architectural features despite existing limitations in generating architectural imagery [48]. While Midjourney and Stable Diffusion also present comprehensive aspects of mindful architecture, DALL-E covers a broader range of characteristics regarding form, space, materials, and environmental elements.

5. Conclusions

It can be concluded that each popular text-to-image AI model—DALL-E, Midjourney, and Stable Diffusion—represents the characteristics of mindful architecture through distinct yet overlapping attributes. Their generated forms emphasize structured lines with sharp edges, while spaces prioritize openness and seamless indoor–outdoor connections. Movement is defined by horizontal continuity and vertical links. The preferred lighting is soft, natural light, often diffused through trees or reflected off surfaces. The typical color palettes include natural tones like gray and brown, derived from wood, stone, and glass materials. Objects often feature wooden or glass elements in floors and walkways. Views highlight peaceful green spaces with large trees, while sounds favor soft natural elements like wind, rustling leaves, and flowing water. The ideal climate evokes calmness through gentle breezes and diffused sunlight. It was found that the mindful architecture images generated by DALL-E allowed architects to describe architectural characteristics in the most detailed manner.

Since the architectural characteristics mentioned above are frequently observed and found in the images generated by all three AI models, these are key features of mindful architecture when viewed through the perspective of text-to-image AI. Therefore, future mindful architecture designs can use these factors as guidelines to create architecture that promotes mindfulness. DALL-E appears to be the most powerful AI for providing architectural image data at present.

However, the conclusions drawn from this study still have certain limitations that future research must address. The first limitation is that the term “mindful architecture” used to define architecture that promotes mindfulness in this study was applied during the literature review process and the image generation by all three AI models. However, recent studies have shown that mindfulness encompasses many other intertwined emotions, including awareness, openness, focus, connection, calmness, and attention [74,75]. Future studies should incorporate these emotions into the research and experimentation related to mindful architecture. In addition, the findings of this research were derived from the creation of mindful architecture images using DALL-E, Midjourney, and Stable Diffusion, all of which still have limitations in generating architectural images [43,48,54,55]. Moreover, AI models still face ethical debates and concerns regarding copyright infringement. Therefore, future research should explore alternative approaches to designing mindful architecture using other AI models or by leveraging big data through other methods.

Finally, the growing interest in incorporating architectural and urban design concepts to promote mindfulness among individuals is becoming increasingly tangible, as evidenced by the announcement and ongoing construction of the Gelephu Mindfulness City in Bhutan. An initial examination of the rendered images of the Mindfulness City [76] reveals several characteristics that align with the Buddhist contemplative space discussed in Section 1.1. For example, these include a calm atmosphere, a welcoming atmosphere, a color palette similar to the traditional Tibetan style, and visual connections to nature and forests [31,32,33]. However, the architecture of the Mindfulness City also exhibits characteristics similar to two other architectural design approaches that have the potential to promote mindfulness: biophilic design and traditional Japanese architecture. It can be observed that these architectural designs [76] align with the concept of biophilic design, as they are connected to nature, incorporate natural light into buildings, and use wood as the primary construction material [27,28,29]. Lastly, the architecture of the Mindfulness City features [76] open and flexible spaces that facilitate adaptability and seamless connections between interior and exterior areas, resembling traditional Japanese architecture [30]. As the architecture of the Gelephu Mindfulness City is intentionally designed to promote mindfulness among its citizens, its characteristics, as previously discussed, align with several findings from past research. However, many architectural features that contribute to mindfulness in this city may not yet be explicitly identified, despite having already been designed and implemented by relevant stakeholders. Therefore, future research should explore the architectural characteristics of the Gelephu Mindfulness City as a realized example of mindful architecture, serving as a foundation for further studies.