The Effect of Applying Artificial Intelligence in Architecture College Developing Design Process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it