Preparing Designers To Tackle ‘Wicked Problems’: The Role Of Research-Based Master Education In Design
Bibliographic record
Abstract
Incorporating teaching about ‘wicked problems’ to prepare designers to contribute to tackling them has been a conversation in design education for the last 50 years, but debates and questions still exist as to how to successfully do so. Master education in design was posited a decade ago as a potential focus point for this topic, but few studies exploring whether and/or how master programs might be succeeding in this area have been completed. This study applies a multi-method flexible research approach to explore the question: what is the role of research-based master education in preparing designers to tackle wicked problems? Findings from a small, yet global, sample of professors and students on their perceptions of wicked problems and how best to prepare designers to tackle them, as well as how their master programs in design are currently succeeding or facing challenges, provide insight that informs key recommendations for educators.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".