Testing a Digital Sustainable Regenerative Teaching Framework in an Architectural Undergraduate Design Studio
Bibliographic record
Abstract
We are confronted with an urgent imperative to transition from merely minimizing harm to developing sustainable, regenerative, and net-positive solutions that systematically restore ecosystems, achieve decarbonization, and enhance health outcomes. This study presents a phased Digital Sustainable Regenerative Design Studio framework, which was implemented in a third-year architecture studio at the Canadian University of Dubai. This methodology incorporates bespoke environmental simulation tools alongside quantitative metrics for climate adaptation, biodiversity, carbon sequestration, and health outcomes to inform creative design solutions. The students devised interventions across 20 urban plots in Dubai. A comprehensive analysis, encompassing real-time engagement with computational tools, reflective journals, feedback sessions, project analysis, and surveys, was conducted to assess the framework’s effectiveness in enhancing students’ comprehension of regenerative design and to examine their responses to complex, interdisciplinary challenges. Key findings reveal improved student performance in applying regenerative design principles and the formulation of strategies for climate-adaptive urbanism. The results suggest that this educational framework has the potential to influence and enhance pedagogical approaches, fostering architects committed to advancing sustainable, regenerative, and climate-adaptive urbanism.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".