Can Social Robots Improve People’s Attitudes toward Individuals Who Stutter?
Bibliographic record
Abstract
Public attitudes toward stuttering are rooted in stereotypes and misconceptions, leading to negative reactions and discrimination against individuals who stutter. Previous research highlights the positive impact of educational interventions on people’s attitudes toward stuttering. The potential of social robots as an educational tool in the context of stuttering awareness remains unexplored. In the present study, we investigate whether a social robot can improve public attitudes when giving an interactive presentation on the topic. We compare its impact with a tablet-only condition. Additionally, we differentiate between two robot conditions—one in which the robot imitates stuttering and another where the robot has fluent speech. In the robot conditions, visuals are shown on a tablet. We used a co-design approach and incorporated the perspectives and experiences of two individuals with lived experiences of stuttering into our study design. A user study with 69 participants reveals significant improvements in attitudes across all three conditions, with no significant difference between conditions. However, participants perceived the robot as significantly “warmer,” more “attractive,” and “novel” when compared to the tablet. These findings provide valuable insights into the potential of social robots as intervention techniques for improving attitudes in the field of stuttering.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".