Care Providers’ Perspectives on the Design of Assistive Persuasive Behaviors for Socially Assistive Robots
Bibliographic record
Abstract
OBJECTIVES: The main objectives of this research are (1) to uniquely design assistive behaviors for socially assistive robots using the principles of persuasion from behavioral psychology, and (2) to investigate caregivers' perspectives and opinions on the use of these behaviors to engage and motivate older adults in cognitive activities. DESIGN: We developed 10 unique robot persuasive assistive behavior strategies for the social robot Pepper using both verbal and nonverbal communication modes. Robot verbal behaviors were designed using Cialdini's principles of persuasion; nonverbal behaviors included expansive movements of the body. Care providers' perceptions of the quality, strength, and persuasiveness of these robot persuasive behaviors were assessed based on the Perceived Argument Strength Likert scale. SETTING AND PARTICIPANTS: Eighteen formal and informal care providers caring for older adults including those living with mild cognitive impairments participated. METHODS: An online survey was designed consisting of short videos of the Pepper robot displaying each behavior. After viewing each video, care providers completed the Perceived Argument Strength Likert scale to evaluate 6 attributes for each behavior. They also provided comments. RESULTS: Results show robot assistive behaviors using praise with emotion, along with emotion with commitment were the most positively rated by care providers. Qualitative responses indicate robot body language and speech quality were influencing factors in how a person perceives assistance in human-robot interactions. CONCLUSIONS AND IMPLICATIONS: Our findings provide new insights into incorporating persuasive strategies into the design of assistive social robot behaviors with the aim of engaging and motivating older adults in an activity. The majority of care providers rated the robot persuasive behaviors positively. In designing a persuasive socially assistive robot for older adults, it is beneficial to display a combination of persuasive strategies, such as praise and commitment with emotion, to address individual users' needs and cognitive levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".