Exploring intergenerational interactions with tiny social robots: a qualitative study
Bibliographic record
Abstract
Background: With the rapid aging of Canada's population, more older adults are choosing to "aging in place," yet they face challenges of social isolation and health risks. Emerging social robots are considered to have potential in reducing loneliness and promoting intergenerational communication. Objective: This study aimed to explore how two tiny social robots (EMO and AIBI) support intergenerational interactions between older adults and university students, focusing on their emotional value, functionality, and perceived risks or limitations. Methods: We applied the Interpretive Description qualitative methodology. Three focus groups were conducted in community settings, including 13 older adults (aged 51-81 years, including two in their early fifties who were active members of the lab's older adult partner group) and 13 university students (>18 years). The study involved separate introductions to the robots for each group, intergenerational joint sessions, and thematic analysis, following COREQ guidelines. Results: Three key themes emerged: (1) Emotional and companionship value-older adults highlighted improved mood, reduced loneliness, and practical benefits such as reminders; (2) Concerns about limitations and risks-students emphasized technical challenges, sustainability, and risks including over-reliance, communication barriers, and maintenance; (3) Bridging generations-both groups recognized the robots' role in fostering shared engagement and emotional resonance across age groups. Conclusion: Tiny social robots show promise in enhancing older adults' emotional well-being and fostering intergenerational connections. Differences in perspectives underscore the need for co-design approaches that integrate older adults lived experiences with younger people' concerns for usability and safety.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".