Co-Design and User Evaluation of a Robotic Mental Well-Being Coach to Support University Students’ Public Speaking Anxiety
Bibliographic record
Abstract
Public speaking anxiety is one of the most common subtypes of social anxiety and is a prevalent concern among university students. Many students experience excessive anxiety when giving presentations in front of other people, which can negatively impact their academic performance and overall mental well-being. With limited access to human coaches and interventions, there is a need for innovative technological solutions, including social robots, to extend and enhance mental health support and accessibility. In this article, we first outline a co-design study with five mental health professionals and a participatory design study with six university students, aiming to design a robotic mental well-being coach to help university students manage public speaking anxiety. Afterwards, we detail a user study with 50 university students to evaluate the usability and acceptability of the developed robotic mental well-being coach system. The findings showed that the robotic coach system, which includes the robot and a tablet, received a usability score of 84.05 and had high acceptability among participants who perceived the robot as knowledgeable and competent. Moreover, participants’ self-reported moods significantly improved following the study. Overall, the qualitative and quantitative analyses in this study yield promising results regarding the potential use of robotic coaches to help university students manage their public speaking anxiety.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".