Generative AI in User Experience Design and Research: How Do UX Practitioners, Teams, and Companies Use GenAI in Industry?
Bibliographic record
Abstract
User Experience (UX) practitioners, like UX designers and researchers, have begun to adopt Generative Artificial Intelligence (GenAI) tools into their work practices. However, we lack an understanding of how UX practitioners, UX teams, and companies actually utilize GenAI and what challenges they face. We conducted interviews with 24 UX practitioners from multiple companies and countries, with varying roles and seniority. Our findings include: 1) There is a significant lack of GenAI company policies, with companies informally advising caution or leaving the responsibility to individual employees; 2) UX teams lack team-wide GenAI practices. UX practitioners typically use GenAI individually, favoring writing-based tasks, but note limitations for design-focused activities, like wireframing and prototyping; 3) UX practitioners call for better training on GenAI to enhance their abilities to generate effective prompts and evaluate output quality. Based on our findings, we provide recommendations for GenAI integration in the UX sector.
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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 | 0.089 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".