Shaping futures: A dialogue on chemical engineering education
Bibliographic record
Abstract
Abstract Engineering as a discipline, profession, practice, and area of study continues to add substantial value in an increasingly complex world. With continually evolving complexity around the planet, such as the need for massive energy transition, global health technologies, or sustainable food systems, how might engineering education practices and theory be considered within these rapid and necessary changes? This paper presents an experiment of co‐creation through experiential reflection about the state of chemical engineering education. Four chemical engineering professors engaged in a dialogue, facilitated by a researcher in education, through collaborative and actionable research. This dialogue uncovered innovative possibilities, educational themes, experiences, and opportunities for others in the profession to consider. The process of dialogue also encouraged the development of an imaginative future sense‐making, known as futuring, through a collective experience. The findings reveal instructive perspectives on the shape of chemical engineering education that should be of value not only to engineers, but also other professionals, practitioners, or those in various science, technology, and math fields.
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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 | 0.050 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.054 | 0.085 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".