Exploring How Older Adults Use a Smart Speaker–Based Voice Assistant in Their First Interactions: Qualitative Study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Smart speaker-based voice assistants promise support for the aging population, with the advantages of hands-free and eyes-free interaction modalities to handle requests. However, little is known about how older adults perceive the benefits of this type of device. OBJECTIVE: This study investigates how older adults experience and respond to a voice assistant when they first interact with it. Because first impressions act as strong predictors of the overall attitude and acceptability of new technologies, it is important to understand the user experiences of first exposure. METHODS: We conducted semistructured interviews with 18 people 74 years and older who had never used a smart speaker before, investigating the patterns of use, usability issues, and perspectives that older adults have when using a voice assistant for the first time. RESULTS: The overall first response to a voice assistant was positive, thanks to the simplicity of a speech-based interaction. In particular, a positive and polite response to complete the interaction with a voice assistant was prevalent, such as expressing gratitude or giving feedback about the quality of answers. Two predominant topics of commands made in the first interaction include asking health care-related questions and streaming music. However, most of the follow-up reactions were unfavorable because of the difficulty in constructing a structured sentence for a command; misperceptions about how a voice assistant operates; and concerns about privacy, security, and financial burdens. Overall, a speech-based interaction was perceived to be beneficial owing to its efficiency and convenience, but no other benefits were perceived. CONCLUSIONS: On the basis of the findings, we discuss design implications that can positively influence older adults' first experiences with a voice assistant, including helping better understand how a voice assistant works, incorporating mistakes and common interaction patterns into its design, and providing features tailored to the needs of older adults.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it