Assessing Student-User Experience in Building Design Studios Using Advanced Intelligent Tools: A Pilot Study at the University
Bibliographic record
Abstract
Recent advancements in technology have revolutionized various fields, including architecture education. However, studies exploring immersive realities in architectural design studios remain limited. This research addresses this gap by conducting a pilot study at the Architectural Engineering Intermediate Design Studio, which thoroughly explores the use of Virtual Reality (VR) to help architectural students enhance their design skills, detect design flaws and clashes between different systems, and provide valuable insights for the advancement of architectural education and practice. The study will adopt a qualitative methodology that utilizes in-depth interviews conducted with the students while they explore their designs using VR hardware. The research findings revealed the effectiveness of VR in identifying the following: Enhancing the visualization of design challenges, providing a comprehensive building assessment, and holistically detecting design qualities and system integration problems. The pilot study recommends integrating the use of VR into the curriculum of the intermediate design studio level at the University. The research findings will contribute to the current knowledge base and guide future advancements in immersive design technologies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".