Bibliographic record
Abstract
Empathy is imperative for the creation of user-friendly products, and can be both taught and learned according to Jon Kolko in his book, “Well-Designed”. In it, he suggests that successful design requires the integration of human factors and an empathy with the users. However, in such statements as “the left circle is engineering, the right circle is design” and “engineering is a reductive activity… design, however, is frequently a generative activity” seem to imply that engineering does not overlap with design. Section headings including “Motivating Engineers”, and “How do you bridge the …gap between engineers and designers?” also strengthen the idea that engineering and design are not performed by the same people. Much of the literature on human factors implies that engineers are analytical, solution-oriented, and thorough. However, creativity and human considerations seem to have been left to someone else, or pushed to the end of the design process as a last-minute add-on. In this work, we focused on how to change this perception by helping engineers to better integrate human factors and empathy into their design processes.We have been exploring potential approaches that could encourage the two seemingly disparate worlds to merge together. After an initial design project with a focus on incorporating experiential learning and human factors did not achieve the expected outcomes, it was clear that encouragement and intentions were not enough to integrate empathetic principles into engineering design. Our research included analyzing different product choices based on experience in a specific area, and a case study to identify the source of human consideration in a capstone design project. This has culminated in the idea that a tool needed to be created to help novice designers introduce human factors into the early stages of their design process.We avoided making a checklist which could be completed with no real consideration for the user. Instead, we created a prototype of an application which we believe would help spark discussion and ideation, while interacting with designers on a platform that is accessible and recognizable. In this paper, we will describe the development activities that were required for this tool as well as the additional work needed to create an operational application for multiple operating platforms. In addition, we will discuss how we believe this will influence the incorporation of human factors into the design processes of novice designers and in which applications we believe this will be the most useful.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".