Students perspectives on the development and deployment of an AI-enabled service robot in long-term care
Bibliographic record
Abstract
The need for Artificial Intelligence (AI) in gerontology education is underscored by the potential benefits it offers in addressing loneliness and supporting social connection among older adults in long-term care (LTC) homes. While the workforce in LTC is often overburdened, AI-enabled service robots present possible solutions to enhance residents' quality of life. However, the incorporation of AI and service robots in current gerontology curricula is lacking, and the views of students on this subject remain largely unexamined. This study aims to fill this gap by capturing the perspectives of university students regarding the deployment of an AI robot, Aether, in LTC environments. In this qualitative study, we conducted focus groups with 22 university students. Our thematic analysis identified three themes: (1) Aether's anticipated usage in geriatric care: companionship support and complementing the work of staff; (2) Challenges in using Aether: technical and emotional challenges; and (3) Solutions and future possibilities: expanding functionalities and integrating personalization features. Students expressed a positive outlook on the role of AI-enabled robots while also highlighting the necessity of addressing ethical and practical considerations. To effectively integrate AI technology in gerontology education, it is essential to understand and incorporate student perspectives, attitudes, and opinions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".