EXTENDED REALITIES AS METHODS OF REPRESENTATION WITHIN ARCHITECTURAL PEDAGOGY
Bibliographic record
Abstract
Although various extended reality (XR) technologies share origins in entertainment, the medium has warranted integration within a range of disciplines, most notably in architectural praxis and pedagogy.In the past, immersive technologies have become synonymous with architectural representation.Previously, XR tools were confined to the visualization of final outcomes, however with increasingly robust software and hardware, they have begun to cascade into other developmental processes and phases.In recent years, there has been a strong push in academia to incorporate immersive experiences into development and idea iteration processes, representation methods, and media for instruction.Such tools are not only able to improve architecture student's abilities to understand the spaces they design digitally in a more comprehensive manner but they are also able to provide extensive insight into existing and historical architectural projects, allowing students to gain a more complete understanding of the built environment.This paper re-examines the AEC industry's relationship with various immersive media and the role these XR technologies play within architectural development and processes.It will begin with defining and distinguishing various XR technologies, including virtual reality (VR), augmented reality (AR) and mixed reality (MR), through a literature review.Through a series of case studies within both architectural pedagogy at Canada's largest architecture program and the professional industry at large, this paper will posit not only the changes in the purpose of immersive technologies in architecture, but also outline the merits for their use within contemporary architectural pedagogy.The paper concludes with projections on the future role of XR platforms within the context of architectural pedagogy.
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.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.041 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".