Actionable companion · Feedback & scaffolding

Learning with agency: Smart Speaker–Based Applications to Support Social Connectedness in Older Adult Residents in Affordable Housing: User-Centered Design Study

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Secondary school. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background Older adults in affordable housing face heightened risks of social isolation and loneliness due to limited social networks, transportation barriers, chronic conditions, and inadequate technology access. Smart speakers offer potential for enhancing social connectedness in this underserved population, yet technology interventions are rarely designed with meaningful input from older adults themselves. User-centered design (UCD) approaches can address this gap by engaging end users throughout the development process to ensure technology solutions align with their needs and living contexts. Objective This study aimed to engage older adults in affordable housing in an iterative UCD process to develop prototype scenarios for smart speaker–based applications that promote social connectedness while addressing safety, community-building, and wellness needs. Methods We conducted a 3-stage UCD study with 29 older adults (mean age 70, SD 6.8 years; 23/29, 79% African American; 20/29, 69% high school education or less) living alone in affordable housing between April 2021 and April 2022. Stage 1 included 5 focus groups (n=25) combining needs assessment discussions with rapid brainstorming activities. Stage 2 involved research team synthesis of focus group transcripts and brainstorming data to create a Design Strategies Map, development of initial prototype scenarios, 5 evaluation focus groups (n=18) to gather feedback, and iterative scenario refinement. Stage 3 comprised 4 validation focus groups (n=17) to assess refined scenarios and identify implementation recommendations. Participants included both smart speaker users (n=13) and nonusers (n=16). Data were analyzed using thematic analysis for needs assessment, content analysis for brainstorming ideas and feedback, and matrix analysis for systematic comparison across scenarios. Results Participants generated 153 ideas for smart speaker use, with Health and Safety and Daily Assistance being the most frequent categories. Analysis revealed that social connection needs were inseparable from safety concerns related to living alone. Through iterative co-design, we developed 7 prototype scenarios across 4 functional categories: Checking-In (peer and management safety verification with privacy controls), Social Companion (conversational artificial intelligence–based companionship and emotional support), Community Involvement (virtual bulletin boards and activity coordination), and Wellness Check (system-initiated monitoring of activity and behavioral patterns as health indicators with user-controlled interventions). Participants emphasized requirements for personalization, opt-in/opt-out controls, “Do-Not-Disturb” functionality, and safeguards preventing replacement of human connection. Conclusions Older adults in affordable housing engaged in technology design and provided valuable insights that challenge assumptions about their needs and preferences. The prototype scenarios addressed the dual imperatives of social connection and safety while living alone, offering a foundation for developing technology-based applications tailored to underserved populations. Implementation should prioritize user control, privacy protection, and human-in-the-loop design ensuring that technology facilitates rather than replaces human connection and community programming, alongside consideration of user characteristics to build trust and ensure effective, sustained use of the intended technology platform.

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Secondary school. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Participant ages · source passage

Methods We conducted a 3-stage UCD study with 29 older adults (mean age 70, SD 6.8 years; 23/29, 79% African American; 20/29, 69% high school education or less) living alone in affordable housing between April 2021 and April 2022.

Study methods · source passage

We conducted a UCD study to engage residents in developing smart speaker–based applications for social connectedness. Our process consisted of three major stages conducted between April 2021 and April 2022: (1) needs assessment and idea generation, (2) iterative prototype development, and (3) validation and implementation planning (Table 1).

Limitations and uncertainty · source passage

There are several limitations of this study. First, due to the COVID-19 pandemic, we were unfortunately unable to follow up with some participants throughout the process, resulting in their loss from the study. The pandemic also necessitated off-site meetings with transportation provision, which may have excluded some potential participants with mobility limitations or discomfort with van travel. Second, established trust relationships that facilitated recruitment may have introduced response bias, as participants with prior positive experiences with the research team may have been more inclined to provide favorable feedback. Given that real-world implementation will inevitably encounter residents who are skeptical, mistrustful of institutional technology, or uninterested in smart speaker–based applications, purposive recruitment of such individuals would elicit insights that a self-selected, engaged sample cannot provide. Third, incorporating diverse and sometimes contrasting ideas into the design was challenging. While we used multiple data synthesis methods, a more systematic approach to identifying and prioritizing user demands (eg, conjoint analysis or formal prioritization exercises) would have been beneficial for making design trade-off decisions explicit. Fourth, hearing-related concerns, such as hearing loss that hinders meaningful engagement with smart speakers, did not emerge spontaneously in focus group discussions. This absence may reflect sample characteristics or participants’ limited awareness of the relationship between hearing ability and voice interface usability. Although smart speaker designs incorporate features such as adjustable volume and visual display companions that may support more equitable access, their adequacy in practice remains unclear. Future work should more explicitly examine how hearing loss shapes engagement with smart speakers. Fifth, while we adapted facilitation approaches to encourage broad participation, differential comfort with technology discussions likely shaped whose voices were most prominent in certain design decisions. Furthermore, differences in age, education, technology access and usage, and prior smart speaker experience among participants may have influenced the outcomes at each phase of the study. Finally, this study focused on early-stage prototyping and scenario development. The scenarios represent participant aspirations and preferences but have not been validated through implementation.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
Days 16–30 · Design. Ask learners to attempt a problem unaided, compare an agent hint, explain a revision and verify the final reasoning against a trusted source. Make the permitted use explicit for each assignment. Name an accountable owner and set the baseline, success threshold and budget before implementation.
Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
Days 61–90 · Decide. Measure unaided transfer and delayed retention, calibration of confidence and the ability to identify an incorrect AI explanation. Record whether help-seeking becomes answer copying. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Jane Chung; Natalie Mansion; Tracey Gendron; Rachel E Wood; George Demiris. Smart Speaker–Based Applications to Support Social Connectedness in Older Adult Residents in Affordable Housing: User-Centered Design Study. JMIR Aging (2026-07-07). https://doi.org/10.2196/90053

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.