The Architectural Language of Biophilic Design After Architects Use Text-to-Image AI
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".