Perspectives on artificial intelligence-enabled socially assistive robots in long-term care: Experiences with LOVOT among people with dementia and family caregivers
Bibliographic record
Abstract
Objective: Artificial intelligence (AI)-enabled Socially Assistive Robots (SARs) are reshaping dementia care in long-term care (LTC) settings. This critical reflection paper, co-authored by co-researchers (people living with dementia and family partners), explores the perceived potential, limitations, and ethical considerations of implementing a robot LOVOT in LTC. Methods: This paper presents a critical reflection based on interviews conducted and analyzed using a reflexive thematic approach. Among 12 co-researchers, three were older adults living with dementia; eight were family care partners and one was an older adult partner. Results: Three themes identified include (a) LOVOT's role in supporting engagement and stimulating conversation, (b) the irreplaceability of human touch and emotional nuances, and (c) concerns about ownership and equitable resource allocation. Discussion: Older adults emphasized that LOVOT has the potential to act as an active role as a companion for older adults with dementia. They also identified opportunities for improvement in design, especially around cultural responsiveness, technological familiarity and safety. Implications: This reflection brings forward the lived experience perspectives from older adults with dementia, and family partners as co-researchers, offering valuable insights to guide ethical and person-centered implementation of SARs like LOVOT in LTC settings.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".