Empathy Development in Design Students: A Comparative Analysis of First- And Second-Year Cohorts
Bibliographic record
Abstract
This paper explores empathy development in engineering education. Empathy, the ability to understand and share others' feelings, is crucial in engineering practice, enabling engineers to consider the impact of their designs and decisions on stakeholders. Prior research highlights empathy's importance in engineering, connecting it to ethical responsibility and user-centred solutions (Hess et al.,2017). However, challenges exist in integrating empathy into engineering education, including the need for effective pedagogical strategies and tensions between traditional engineering culture and empathy development (Kotluk and Tormey,2025). This paper presents a comparative analysis of empathy development between firstyear and second-year engineering design students. The study uses the Empathy in Design Scale (EMPA-D) (Drouet et al., 2024), modified for engineering students' design tasks. The students in second year had significantly lower scores than first year students on two of the dimensions. These findings prompt the need for better understanding of how this construct changes in order to provide insights into empathy development in engineering students and inform educational practices to foster empathy in curricula.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".