Athletes’ Experiences of AI-Generated Images in Qualitative Research
Bibliographic record
Abstract
Qualitative visual methods include approaches such as photo elicitation, photovoice, autophotography, and drawing, which can be used to build rapport, generate discussion within interviews, or as sources of data for analysis in qualitative studies. AI-generated images present a new frontier in visual qualitative methods: using images generated with AI programs can provide an innovative approach to creating images that represent participants’ experiences, but these have not been widely used or critically examined. The purpose of this study was to examine athletes’ perceptions of creating AI-generated images about their experiences of success and adversity in sport. The study included 10 participants between 20-61 years of age (8 women, 2 men) from various high performance sports (basketball, hockey, judo, rowing, running, swimming, track and field, ultramarathon). Athletes participated in qualitative interviews about their experiences of success and adversity in sport. During the interviews they worked with the researcher to generate images about their experiences using the AI program Midjourney, and athletes reflected on the images that were generated. Thematic analysis of the interviews generated the following themes: (a) Inaccuracies and mis-representation; (b) Misembodiment and disconnection with images; (c) Over-representation of emotional experiences; (d) Generating diverse representations of athletes; and (d) Re-creating important moments. Athletes also discussed challenges and limitations, as well as potential positive uses for AI-generated images in qualitative research. This study demonstrates how AI-generated images are different from other visual qualitative methods, and the results provide insights for exploring athletes’ experiences using AI-generated images in qualitative studies.
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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 | 0.069 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".