Viewpoints of the Architects on Using Artificial Intelligence in Green Architectural Design: A Case Study in the City of Kerman, Iran
Bibliographic record
Abstract
In architecture, AI can perform many tasks during the architectural design process, so it is necessary to understand this technology and its advantages and disadvantages, especially for those concerned with environmental and sustainable aspects. There are gaps and ambiguities in relation to the effect of using AI in architecture, especially when it comes to the principles of the green architecture, particularly in Iran. Therefore, the current study was formulated to determine the possibility of AI in supporting green architecture design, to determine the possibility of AI in achieving the principles of green architecture, to examine the architects' viewpoints regarding the role of AI in achieving the principles of green architecture by using a questionnaire, and to determine the most common AI programs used by architects the most. To address the research objectives, a questionnaire was adopted, and back-to-back translated, and distributed among 46 architects who worked in Kerman city, Iran. The data was collected during October 2024, and to analyze, SPSS Version 25 was used. The results of the study showed that to a great extent, AI can support green architecture design because majority of the respondents confirmed that AI can give multiple alternatives to environmental treatments and can help the designer make design decisions to achieve the green architecture principles. Overall, it was concluded that AI can achieve principles of green architecture.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".