System Development and Evaluation of a Social Robot as a Public Speaking Coach
Bibliographic record
Abstract
Presentation rehearsal is an essential activity that significantly impacts the quality of presentation delivery, especially for novice presenters, such as university students. However, rehearsals are often not done appropriately, or lack constructive feedback. To encourage effective presentation rehearsal, we devised a system involving a social humanoid robot acting as a public speaking coach, analyzing students’ presentations and providing feedback. We monitored acoustic aspects of speech, speech prosodies, and eye contact maintenance during presentations. Our aim was to assess robot acceptance, participants’ sense of interpersonal closeness with the robot, and perceived human nature attributes of the robot. This study presents the system development, followed by evaluations with 50 university students, as well as an evaluation by a public speaking coach. We found that the students, on average, gave high acceptance scores for the robot and reported moderate interpersonal closeness with the robot, and attributed human nature attributes to it. Additionally, an expert public speaking coach found the system to be able to provide reliable and relevant feedback to students considering their performance, and he also gave useful insights on potential improvements of the system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".