ArchiConnect: Supporting Architects’ Design Drafting with Dynamic Demands from Multi-Stakeholders
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 architecture design, while Generative AI can effortlessly create initial prototypes, architects struggle to update designs to stakeholders’ evolving requirements. Through a formative study (N = 12), we identified specific obstacles that architecture designers face when meeting the dynamic design demands of various project stakeholders. We therefore developed ArchiConnect, a proof-of-concept interactive system that helps architects communicate with multiple stakeholders and update final design deliverables. ArchiConnect supports creativity and engagement by visualizing evolving demands, conflicts, and concept extractions from diverse stakeholders. We evaluated our system in a week-long user study (N = 8) with a simulated project. Participants found ArchiConnect effective for improving multi-stakeholder communication and management, describing it as intuitive and useful. Our findings offer design considerations for future AI tools to better handle dynamic stakeholder needs, including how to address sustainability requirements in line with development goals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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