Conversational voice assistants are often imbued with personality and human-like characteristics (e.g., gender). While researchers have begun to examine and design for the downstream societal impacts of voice assistants encoding characteristics such as gender, we know little about other human-like characteristics such as age that are encoded in an artificial, yet, anthropomorphic voice. As older adults continue to adopt voice assistants, we brought older adults into an activity to customize human-like characteristics for their voice assistant. Our findings reveal the different stereotypes and assumptions individuals associated with voice assistant characteristics (e.g., age, gender, race). We also describe individuals' motivations behind customizing or not customizing these characteristics. We discuss how biases get encoded through our design process, marginalizing older adults and other non-dominant user groups and call for a need to examine the systemic, yet unspoken, power structures encoded in anthropomorphic technologies.

Visual online communities can present accessibility challenges to older adults or people with vision and motor disabilities. Motivated by this challenge, accessibility and HCI researchers have called for voice-based communities to support aging and disability. This paper extends prior work on voice community design and short-term use by providing empirical data on how people interact with voice communities over time and intentional instances of non-use. We conducted a one-year study with 43 blind and low vision older adults, of whom 21 used a voice-based community. We use vignettes to unpack five different voice community member roles - the obligatory poster, routine poster, cross-platform lurker, busy socialite, and visual expertise seeker - and discuss community interactions over time. Findings show how participation varied based on engagement in other communities and ways that participants sought interaction. We discuss (1) how to design voice communities for member roles and (2) the implications of synchronous and asynchronous voice community interaction in voice-only communities.

While past research has examined older adults’ voice assistant (VA) use, it is unclear whether VAs provide enough value to sustain use when compared to technologies such as smartphones. Research also suggests that barriers around structured command input may limit use. In order to investigate these gaps in adoption, we conducted interviews with ten older adults in a long-term care community who have adopted Alexa devices for at least one year. Participants learned to use Alexa through a training program that encouraged exploration. They used Alexa to complement their daily routines, improve their mood, engage in cognitively stimulating activities, and support socialization with others. We discuss our findings in the context of prior work, describe strategies to promote VA learning and adoption, and present design recommendations to support aging.

Although voice assistants are increasingly being adopted by older adults, we lack empirical research on how they interact with these devices for health information seeking. Also, prior work shows how voice assistant responses can provide misleading or inaccurate information and be harmful particularly in health contexts. Because of increased health needs while aging, this paper studies older adult’s (ages 65+) health-related voice assistant interactions. Motivated by a lack of empirical evidence for how older adults approach information seeking with emerging technologies, we first conducted a survey of n = 201 older adults to understand how they engage voice assistants compared to a range of offline and digital sources for health information seeking. Findings show how voice assistants were used for confirmatory health queries, with users showing signs of distrust. As much prior work focuses on perceptions of voice assistant use, we conducted scenario-based interviews with n = 35 older adults to study health-related voice assistant behavior. In interviews, participants engaged with different health topics (flu, migraine, high blood pressure) and scenario types (symptom-driven, behavior-driven) using a voice assistant. Findings show how conversational and human-like expectations with voice assistants lead to information breakdowns between the older adult and voice assistant. This paper contributes a nuanced query-level analysis of older adults’ voice-based health information seeking behaviors. Further, data provide evidence for how query reformulation happens with complex topics in voice-based information seeking. We use our findings to discuss how voice interfaces can better support older adults’ health information seeking behaviors and expectations.

To address the goal of increasing and broadening participation of youth in STEM fields, a learning ecosystem approach is a promising strategy. Learning analytics can play an important role in such efforts which aim to build learning supports across the diverse spaces in which learning and development occurs, including informal, formal, and online contexts. This paper introduces a city-level learning analytics implementation effort in a developing STEM ecosystem in one mid-sized city. We describe aspects of our design and research approach and challenges that emerge by taking a learning ecosystem perspective of learning and development.