Jun 2026· Health Communication· pp.
1-15
· 0 citations· 49 references
Medicine
TL;DR
It is suggested that the benefits of AI voice assistants for older adults may be more evident in supporting everyday autonomy and competence than in addressing social isolation, and that psychological benefits of short-term use may not immediately translate into changes in global well-being outcomes.
Abstract
Aging often reduces opportunities for everyday interaction and instrumental support, raising questions about the potential role of artificial intelligence (AI) technologies in later-life well-being. This study examined how AI voice assistants may fulfill, or fall short of, older adults' basic psychological needs through the lens of self-determination theory (SDT). Eighteen U.S.-based community-dwelling older adults (ages 66-85) participated in a two-week AI voice assistant trial and completed daily diaries and pre- and post-intervention surveys. Using mixed methods, qualitative thematic analysis and large language model-assisted content analysis, we analyzed 237 diary entries (1,512 sentences) across three SDT dimensions: autonomy, competence, and relatedness. Findings reveal voice assistants primarily supported autonomy (medication reminders, safety, emotional regulation) and competence (task accomplishment, activity maintenance), with experiences remaining largely consistent across the two-week period. Relatedness responses were the most divided: some participants valued companionship features while others found them inauthentic or uncomfortable. Despite positive daily experiences, loneliness and life satisfaction showed no significant change. These findings suggest that the benefits of AI voice assistants for older adults may be more evident in supporting everyday autonomy and competence than in addressing social isolation, and that psychological benefits of short-term use may not immediately translate into changes in global well-being outcomes.
As the global population of older adults grows, AI-powered Voice Assistants (VAs) are explored as tools to mitigate loneliness, social isolation, and cognitive decline. By enabling intuitive, hands-free interaction, VAs provide a means to enhance independence, emotional well-being, and daily functioning for older adults. This paper presents a systematic review on older adults’ interactions with VAs, analysing 48 studies, which is more than double the scope of prior reviews. Studies were selected from an initial pool of 109 publications across three major Human-Computer Interaction (HCI) databases: Scopus, PubMed, and the ACM Digital Library. Through thematic analysis of research questions addressed in the selected studies, six key research themes were identified: (1) usage patterns and interaction behaviours, (2) adoption barriers and learnability challenges, (3) user experience and satisfaction, (4) cognitive and emotional impacts, (5) design considerations for older adults, and (6) future directions. A major finding is the VAs potential role in offering emotional companionship, particularly for socially isolated individuals. However, findings also suggested that technological illiteracy, trust concerns, and usability challenges hinder VAs’ adoption. This review highlights several important gaps in the existing literature. There is a lack of longitudinal research, and limited understanding of how these systems are used in real-world settings compared to controlled environments. Many studies also fail to report participants’ levels of technological experience and rely on samples that are not fully representative of the broader older adult population. Taken together, these limitations suggest that current research may not fully capture long-term use, contextual variability, or the diversity of older adults. In response, this paper outlines a set of evidence-based recommendations for future research and design. It emphasises the need to account for novelty effects in short-term studies and to adopt more ecologically valid approaches, such as in-home or longer-term deployments. It also highlights the importance of minimising artificial constraints that may influence user behaviour, and of systematically documenting user–device interactions alongside participants’ digital literacy. These steps can support more rigorous and representative evaluation practices, contributing to the development of voice assistant technologies that are better aligned with real-world use and more accessible, ethical, and effective for older adults.
Nadeesha Pathirana, Amal Htait, E. Wanner· Universal Access in the Info...· 0 citations
BACKGROUND
Amid growing concerns surrounding social isolation among older adults, AI chatbots have emerged as promising tools for providing digital companionship. While the intersection of aging and technology has been widely explored, there remains a gap in understanding the nuanced ways in which AI chatbots function as relational partners in older adults' daily lives.
OBJECTIVES
This study aimed to examine the roles of the AI chatbot in fostering digital companionship for adults in later life.
METHODS
Drawing on a qualitative research design, the study conducted in-depth, semi-structured interviews with individuals in later life who regularly engaged with AI chatbots. Thematic analysis method was used to synthesize the qualitative data.
RESULTS
Participants described the AI chatbot as multifaceted relational agents-occupying roles such as human-like companions, supportive teachers, specialized consultants, practical assistants, and entertainment facilitators. These interactions were reported to support emotional well-being, enhance cognitive engagement, and promote a sense of self-efficacy in daily living.
CONCLUSIONS
The findings illuminate the profound and diverse ways in which AI chatbots are integrated into the everyday lives of older users, extending far beyond utilitarian functions. As conversational AI continues to advance, designers and policymakers should acknowledge its potential to support aging in place by fostering connection, engagement, and autonomy among older populations.
Yijin Wu, Fengbo Jiao, Haokun Wang et al.· The gerontologist· 0 citations
As South Korea enters a super-aged society, the rapid growth of older adults living alone has intensified concerns about social isolation, loneliness, and depression. This study examined whether AI care robots can serve as an effective psychosocial intervention for these three interrelated emotional vulnerabilities using an explanatory sequential mixed-methods design. In the quantitative phase, a quasi-experimental study (n = 60; 30 robot users vs. 30 controls) over 2 months demonstrated significant group-by-time interaction effects across all three outcomes via linear mixed models. In the qualitative phase, in-depth interviews with three participants revealed three mechanisms through which robot interactions produced emotional change: restoration of social presence, emotional companionship, and recovery of daily routine and self-efficacy. The findings extend the Convoy Model to include technology-mediated support and suggest that AI-human interactions activate interpersonal mechanisms analogous to Sullivan's Interpersonal Theory. Implications for community-based technology-assisted care policy and practice are discussed.
S. Lee· Journal of Applied Gerontolo...· 0 citations
Conversational robots are increasingly proposed to provide everyday emotional support for older adults. However, it remains unclear which encouragement strategies are perceived as effective and whether interpersonal strategy preferences transfer to Human–Robot Interaction (HRI). This study compared the perceived effectiveness of five encouragement strategies when delivered by a conversational robot versus a human partner, examined moderation by worry type, and explored associations with recipients’ emotional states. Japanese young-old adults aged 65–74 (final N = 55) completed an online scenario-based survey. Participants were randomly assigned to a human or conversational robot condition, each combining an identity label with distinct voice characteristics (a human-recorded voice or a robot-recorded voice). They recalled one physical and one psychological daily worry and rated perceived encouragement for five strategy types: reassurance and affirmation, expressing concern, encouragement to act, offering specific actions, and distraction. Within-subject standardized ratings were analyzed using linear mixed-effects models with a participant random intercept. Positive and Negative Affect Schedule (PANAS) were examined exploratorily. Results showed that perceived effectiveness differed by strategy type and notably by condition. In the human condition, offering specific actions showed the highest relative effectiveness, with descriptively clearer differentiation among strategies. In the robot condition, reassurance and affirmation ranked highest, and differentiation among strategies was descriptively reduced. Distraction was consistently least preferred. The present study did not observe a statistically significant effect of worry type on the overall ranking patterns. Exploratory analyses suggested that positive affect was associated with encouragement evaluation at the raw-score level in the robot condition, whereas associations involving negative affect did not survive correction for multiple comparisons and are therefore interpreted descriptively rather than as statistically significant findings. These findings suggest that interpersonal encouragement strategy preferences may not directly transfer to human–robot interaction as operationalized in the present study. The observed differences between conditions may reflect the combined effect of the identity label and voice characteristics rather than the robot’s identity alone. The results highlight the need for partner-specific, context-adaptive encouragement design in conversational robots for older adults.
Lingxuan Xiang, Marin Nishimura, H. Kikuchi· Frontiers in Robotics and AI· 0 citations
The rapid adoption of conversational AI among adolescents has sparked growing debate about its implications for development and well-being. This article applies a Self-Determination Theory lens to explore how and under which conditions adolescents' conversational AI usage supports or frustrates their basic psychological needs for competence, relatedness, and autonomy. Conversational AI may enhance competence through learning assistance, relatedness through emotional and relational support, and autonomy through increased independence. At the same time, it may also promote the risk of superficial competence, displace human relationships, and constrain authentic autonomy. Overall, the role conversational AI plays in adolescents' development is unlikely to be universally positive or negative for all adolescents, but instead depends on how, why, and in which context these technologies are used, highlighting the need for nuanced research and developmentally sensitive design.
Lauren C. Dedecker, Y. Chen, Gaëlle Vanhoffelen et al.· Child and Adolescent Mental...· 1 citation
This paper aims to offer a worked methodological demonstration of an integrated socioemotional selectivity theory (SST)–self-determination theory (SDT) reflexive thematic analysis (RTA), examining how urban, community-dwelling older adults in India are illustrated as using and emotionally experiencing generative AI and how this relates to autonomy, competence, relatedness and healthy ageing.
An RTA, organised using a Gioia-style structure, was conducted on an illustrative data set of 16 urban older adults (aged 61–83, mean 70.6) across 10 Indian cities: 5 full transcripts and 11 condensed summaries. As the data set was constructed for teaching purposes rather than collected under ethics-approved fieldwork, findings are a hypothesis-generating exercise rather than generalisable evidence.
Six aggregate dimensions were derived: everyday instrumental and cognitive integration; emotional relief primarily through reduction of uncertainty rather than direct companionship; differentiated relational meaning ranging from simulated companionship to explicitly non-relational, intellectual partnership; autonomy and competence gained through self-directed problem-solving; a near-universal positioning of AI as bounded against human relationships; and countercurrents of privacy unease, calibrated trust, literacy barriers and fear of overdependence. The integrated SST–SDT chain fit unevenly: several accounts were better explained by an informational, cognitive-appraisal pathway than an emotional one, and relatedness, unlike autonomy and competence, was not consistently reported.
This paper provides a transparent, reflexive worked example of applying an integrated SST–SDT framework with Gioia-structured RTA to a novel, under-researched population, and specifies what a genuine empirical study in this domain would require.
Aniket Godse, Saikat Deb, Avishek Ghosal et al.· Working with Older People· 0 citations
