Generative AI has shown the potential to support older adults to reminisce about the past by producing personalized memory-related content despite the person's varied ability to elaborate or the lack of memory cues. We present two studies to investigate how generative AI can support older adults in individual and group reminiscence. In Study 1, we conducted individual co‑design sessions with 16 older adults, during which participants created textile collages inspired by personal memories and then used generative AI to transform these creations into memory‑related video content. In the second study, we incorporate the textile collages and AI-generated videos into an interactive artifact, Reminiscope, and introduce it in a series workshops with 15 participants (with 14 returning participants from Study 1) to support group reminiscence. Findings from these studies reveal how older adults’ perspectives towards collaborating with generative AI for creating memory-related content, and their experiences of engaging with an AI‑enhanced interactive artifact during shared reminiscence activities. Our work contributes to the emerging trend of leveraging generative AI to support reminiscence in older adults, and provide design implications for future reminiscence technologies.
Designing Conversational AI systems to support older adults requires these systems to explain their behavior in ways that align with older adults’ preferences and context. While prior work has emphasized the importance of AI explainability in building user trust, relatively little is known about older adults’ requirements and perceptions of AI-generated explanations. To address this gap, we conducted an exploratory Speed Dating study with 23 older adults to understand their responses to contextually grounded AI explanations. Our findings reveal the highly context-dependent nature of explanations, shaped by conversational cues such as the content, tone, and framing of explanation. We also found that explanations are often interpreted as interactive, multi-turn conversational exchanges with the AI, and can be helpful in calibrating urgency, guiding actionability, and providing insights into older adults’ daily lives for their family members. We conclude by discussing implications for designing context-sensitive and personalized explanations in Conversational AI systems.
Multisensory stimulation promises in improving older adults’ affective experiences, yet its effectiveness depends on seamless affective congruency across sensory cues. This study investigated how visual, auditory, and kinetics correspondence and congruency shape affective experiences through two experiments. Experiment I examined timbre–color associations, showing that affective alignment strengthens perceived correspondence. Experiment II explored auditory–kinetics synchrony in a cross-modal art system, revealing no significant differences across conditions but indicating that older adults with lower cognitive abilities reported higher pleasure than higher-ability peers. Building on these results, an artificial intelligence (Al)-infused mode was integrated to transform strokes into real-time ink-style artworks, reducing cognitive effort, sustaining engagement. Findings demonstrate that AI enhances positive affect (pleasure, surprise, valence, and arousal) and mitigates negative affect (sadness, anger), with effects maximized by high sensory synchrony, providing compensatory support for users with lower cognitive abilities. These findings inform multisensory system design for older adults’ cognitive and affective needs.
As Pakistan’s population ages and traditional care structures evolve, there is increasing interest in technological solutions for supporting older adults at home. This study investigates the potential of social assistive robots (SARs) in Pakistani homes, focusing on cultural values, inter-generational living, and limited access to such technologies. In a 3-day mixed-methods home study, 14 older adults interacted with a SAR communicating in Urdu. Through observations, interviews, and questionnaires, we evaluated the robot's acceptability, engagement, and cultural compatibility. The results highlighted the need for culturally sensitive design, emphasising the role of robots as companions rather than replacements for human care, and the importance of robot’s ability to communicate in Urdu. We discuss how SARs could be designed to reflect the characteristics of Pakistani households, including faith, family values, everyday routines, and environmental factors. Our design considerations can benefit research on deploying SARs to support older adults in Pakistan and similar cultures.
Inclusive computer literacy education efforts, broadening the participation of blind or visually impaired (BVI) individuals, have gained traction in recent years. Existing literature investigating these efforts primarily draws evidence from affluent Global North contexts, where accessibility resources and legal frameworks are relatively more mature. Little is known about the in-situ teaching and learning challenges faced by trainers and BVI students, respectively, in resource-constrained, multicultural Global South countries like India. To address this knowledge gap, we conducted a four-month contextual inquiry at two computer training centers catering to 94 BVI students in India. We notably observed a rigid, experience-driven training environment and a visually-centric curriculum that discounts the lived experiences of BVI learners and inadvertently undermines their learning self-efficacy. Informed by the findings, we discuss moving beyond functional accessibility-centered teaching toward a more culturally responsive computing pedagogy, facilitated by locally adaptable contextual scaffolds tailored for BVI students in developing societies like India.
The HCI research community has witnessed a growing body of research on accessibility and disability driven by efforts to improve access. Yet, the concept of access reveals its limitations when examined within broader ableist structures. Drawing on an autoethnographic method, this study shares the co-first author Zhang's experiences at two higher-education institutions in China, including a specialized program exclusively for blind and low-vision students and a mainstream university where he was the first blind student admitted. Our analysis revealed tensions around access in both institutions: they either marginalized blind students within society at large or imposed pressures to conform to sighted norms. Both institutions were further constrained by systemic issues, including limited accessible resources, pervasive ableist cultures, and the lack of formalized policies. In response to these tensions, we conceptualize access as a contradictory construct and argue for understanding accessibility as an ongoing, exploratory practice within ableist structures.
Older adults are increasingly turning to chatbot-based mental health support, yet adoption remains limited by barriers in accessibility, privacy, security, and trust. We present a two-phase study on the co-design of MESA-Bot (Mental and Emotional Support Assistant for Older Adults), a non-diagnostic chatbot tailored to later-life needs. In Phase I, we analyzed ten leading mental health chatbots and conducted co-design sessions with N1=10 older adults to identify challenges and inform an accessible, privacy-aware prototype. In Phase II, we evaluated MESA-Bot with N2=28 older adults using semi-structured interviews and structured technical assessments. Participants emphasized transparent consent, supportive tone, and fine-grained data control. Features such as revocable consent, role-based access control, customizable data visibility, and simplified dialogue flows increased trust and usability; 86% found MESA-Bot easy to use. We offer design insights for inclusive, trustworthy mental health technologies that integrate accessibility with verifiable privacy and security protections.