The Effect of Applying Artificial Intelligence in Architecture College Developing Design Process
Bibliographic record
Abstract
In the last 20 years, the world has seen increasing use of Artificial intelligence (AI) in many disciplines, one of these disciplines is Architecture. This research aims to study the effect of using AI in Architecture schools, especially design studios, in which phase, and in what percentage. The methodology of the research is applied for AI programs in four main design steps: The concept phase developing the design, coloring and developing the elevations, rendering phasing by using the AI, and distributing a survey to Design 7 students to register their responses using AI in Architecture design selected case studies from students work was selected to reflect the research works. The results from the survey show that the students achieved applying AI in concept development by 75%, in the development design process by 72.54%, in coloring by 50%, in rendering by 48%, sustainability by 70%, and in developing building form and structure by 72.3%. The conclusion of the research recommends applying AI in the whole design process including concept development, developing design process, coloring, rendering, form, and structure under the teacher's supervision, and recommends teaching AI as a course in architecture engineering colleges.
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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 | 0.011 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".