AI-Image Generation in Research Interviews: Opportunities and Challenges
Bibliographic record
Abstract
Drawing on our experience developing a visual polyvocal narrative of the immigration system in Canada and Brazil, we explore the role of artificial intelligence (AI) image generation as a tool for supporting interview participants in articulating their experiences. We found that the AI image generation process supported participants’ ability to reflect and express their experiences. However, there were several challenges due to technological limitations and inherent biases embedded in the AI, which resulted in unsatisfactory images and repeated image generation attempts. We came to conceptualize the AI image generation tool as a third agent in the interview process, facilitating access to artistic expression yet introducing content into the conversation. We identified five primary roles played by the AI image generation tool in the interview process: Helper (supported the image generation process), Distractor (transferred attention from the topic of study to prompt engineering), Motivator (motivated participants to better articulate their vision), Influencer (introduced content in the conversation), and Facilitator (facilitated reflection and sensemaking). We discuss avenues for maximizing the benefits of AI image generation in interviewing and mitigating its challenges. We contribute to a growing body of research on reflective and arts-based interventions in interviewing by illustrating the role new technologies can play in advancing the potential of interview-based research.
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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 | 0.265 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.023 | 0.037 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.006 | 0.006 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".