Impact of UX Internships on Human-computer Interaction Graduate Students: A Qualitative Analysis of Internship Reports
Bibliographic record
Abstract
Objectives. Internships can bring a host of professional and academic benefits to students. Then, how do User Experience (UX) internships influence Human-Computer Interaction (HCI) graduate students’ professional and academic growth? What are the challenges experienced by HCI graduate students during internships? We explored these two research questions. Participants. Our study participants were 42 HCI graduate students who completed UX internships. They came from computing and related disciplines, including computer science, information technology, psychology, and design. Some of the participants’ internship titles were Interaction Designer, Design Researcher, UX Programmer, and Business Intelligence Analyst. Study Method. We conducted a thematic analysis on 42 graduate students’ UX internship reports that were collected over 6 years to uncover themes in relation to our two research questions. Findings. As for UX internship benefits, we found that students learned about the workplace culture (e.g., academia vs. industry/government on research design processes) and core UX technical (e.g., research, design, programming) and people skills (e.g., teamwork, empathy toward end-users); they also realized what they wanted in future careers after completing their internships. We also found internship challenges that were related to the internship program (e.g., the availability of internship opportunities), the host organizations (e.g., the quality of mentorship received), and remote working (e.g., difficulty over conducting remote usability testing). Conclusions. We make practical recommendations for HCI educators, UX practitioners, and HCI graduate students on how they can work collaboratively to create a meaningful UX internship experience. These recommendations include researching the host organization prior to internships, providing comprehensive onboarding, and being transparent with internship constraints.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".