Unraveling the Threads: Designing a Multiverse of Learning Through Interdisciplinary Co-Creation in Design Education
Bibliographic record
Abstract
This paper proposes a framework for reimagining design education as a "multiverse of learning" where students, educators, industry stakeholders, and communities co-create knowledge through intertwined experiences. It explores the roles of creators, connectors, and catalysts in this multiverse, offering a conceptual model for fostering creative collisions at the intersection of diverse disciplines. The paper also addresses critical tensions, such as ethical dilemmas, power imbalances, and the challenge of maintaining inclusivity in co-creative processes. Drawing on case studies, the paper illustrates how emerging technologies, like AI and collaborative virtual environments, can facilitate uncharted connections in design education. Finally, the paper envisions a future in which design graduates are equipped to navigate an unpredictable professional landscape by embracing co-creative problem-solving, lifelong learning, and adaptive expertise.
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How this classification was reachedexpand
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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".