The role of generative design and additive manufacturing capabilities in developing human–AI symbiosis: Evidence from multiple case studies
Bibliographic record
Abstract
Abstract The benefits of additive manufacturing (AM) extend beyond the attributes of physical products and production processes they enable. Experience with AM can augment the way design is approached and can increase opportunities to pivot toward less familiar design tasks. We begin this qualitative study with a natural experiment made possible by an exogenous shock: the COVID‐19 pandemic. Through a three‐stage case study approach using a grounded theory‐building method, we contrast AM usage among a set of firms, half of which pivoted their resources away from their traditional production and toward a response to this shock. We engage in an abductive reasoning approach to consider common threads in AM capabilities that facilitated this pivoting. Our analyses suggest that the advanced use of generative design (GD), a category of computational technologies enabling novel and optimized design, is a critical attribute of these firms that ended up pivoting to make COVID‐related products. Specifically, firms with experience applying this capability demonstrated a unique ability to pivot during this shock and emphasized their valuation of AM‐enabled agility. We revisited these firms 2 years after initial contact and found that GD was associated with higher levels of innovation and was largely viewed by designers as a mechanism driving double‐loop learning. Overall, our study provides insights into the symbiosis between human and artificially intelligent GD, and the role of such symbiosis in advancing AM capabilities.
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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 | 0.043 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".