Large language models (LLMs) enable conversational agents (CAs) to express distinctive personalities, raising new questions about how such designs shape user perceptions. This study investigates how personality expression levels and user-agent personality alignment influence perceptions in goal-oriented tasks. In a between-subjects experiment (N=150), participants completed travel planning with CAs exhibiting low, medium, or high expression across the Big Five traits, controlled via our novel Trait Modulation Keys framework. Results revealed an inverted-U relationship: medium expression produced the most positive evaluations across Intelligence, Enjoyment, Anthropomorphism, Intention to Adopt, Trust, and Likeability, significantly outperforming both extremes. Personality alignment further enhanced outcomes, with Extraversion and Emotional Stability emerging as the most influential traits. Cluster analysis identified three distinct compatibility profiles, with "Well-Aligned" users reporting substantially positive perceptions. These findings demonstrate that personality expression and strategic trait alignment constitute optimal design targets for CA personality, offering design implications as LLM-based CAs become increasingly prevalent.

Serious Illness Conversations (SICs)—discussions about values and care preferences for patients with life-threatening illness—rarely occur in Emergency Departments (EDs), despite evidence that early conversations improve care alignment and reduce unnecessary interventions. We interviewed 11 ED providers to identify challenges in SICs and opportunities for technology support, with a focus on AI. Our analysis revealed a four-stage SIC workflow (identification, preparation, conduction, documentation) and barriers at each stage, including fragmented patient information, limited time and space, lack of conversational guidance, and burdensome documentation. Providers expressed interest in AI systems for synthesizing information, supporting real-time conversations, and automating documentation, but emphasized concerns about preserving human connection and clinical autonomy. This tension highlights the need for technologies that enhance efficiency without undermining the interpersonal nature of SICs. We propose design guidelines for ambient and peripheral AI systems to support providers while preserving the essential humanity of these conversations.

In the rapidly evolving field of artificial intelligence (AI) agents, designing the agent's characteristics is crucial for shaping user experience.This workshop aims to establish a research community focused on AI agent persona design for various contexts, such as in-car assistants, educational tools, and smart home environments.We will explore critical aspects of persona design, such as voice, embodiment, and demographics, and their impact on user satisfaction and engagement.Through discussions and hands-on activities, we aim to propose practices and standards that enhance the ecological validity of agent personas.Topics include the design of conversational interfaces, the influence of agent personas on user experience, and approaches for creating contextually appropriate AI agents.This workshop will provide a platform for building a community dedicated to developing AI agent personas that better fit diverse, everyday interactions.

Little research has investigated the design of conversational styles of voice assistants (VA) for adults in their later adulthood with varying personalities. In this Wizard of Oz experiment, 34 middle-aged (50 to 64 years old) and 24 older adults (65 to 80 years old) participated in a user study at a simulated home, interacting with a VA using either formal or informal language. Older adults with higher agreeableness perceived VA as being more likable than middle-aged adults. Middle-aged adults showed similar technology acceptance toward the informal and formal VA, and older adults preferred using informal VA, especially those with low agreeableness. Further, while both middle-aged and older adults frequently anthropomorphized VAs by using human metaphors for them, older adults compared formal VA with professionals (e.g., librarians, teachers) and informal VA with their close ones (e.g., spouses, relatives). Overall, the conversational style showed differential effects on the perceptions of middle-aged and older adults, suggesting personalized design implications.

In this paper, we present Health Buddy, a voice agent integrated into commercially available Voice User Interfaces (VUIs) to support informal self-regulated learning (SRL) of health-related topics through multiple learning strategies and examine the efficacy of Health Buddy on learning outcomes for younger and older adults. We conducted a mixed-factorial-design experiment with 26 younger and 25 older adults, assigned to three SRL strategies (within-subjects): monologue, dialogue-based scaffolding building, and conceptual diagramming. We found that while younger adults benefit more from scaffolding building and conceptual diagramming, both younger and older adults showed equivalent learning outcomes. Furthermore, interaction fluency (operationalized by the number of conversational breakdowns) was associated with learning outcomes regardless of age. While older adults did not experience less fluent conversations, interaction fluency affected their technology acceptance toward VUIs more than younger ones. Our study discusses age-related learning differences and has implications for designing VUI-based learning programs for older adults.

Voice User Interfaces (VUIs) are popular among older adults, who find them easy to use and perceive them as social companions. However, there is a lack of research on voice-based applications to support physical activities for older adults. To address this gap, we present "Workout Pal," a voice agent designed to deliver physical activities to older adults. We conducted a mixed-methods study involving ten older adults to understand their perceptions and design priorities when interacting with Workout Pal. Questionnaires and semi-structured interviews were used to assess their experience, while experience-based co-design sessions facilitated collaboration in exploring the design space and identifying design requirements. Our findings highlight the feasibility and design preferences for smart speaker-based physical activity programs. The study contributes by sharing the design and development of Workout Pal, illustrating a novel co-design approach with older adults, and providing design implications tailored to the specific needs of older adults. The design guidelines emphasize the importance of sociability, voice design, and individualization. This research supports the development of elder-friendly VUIs helping older adults live independently and engage in physical activities.

We explore a range of different metaphors used for Voice User Interfaces (VUIs) by designers, end-users, manufacturers, and researchers using a novel framework derived from semi-structured interviews and a literature review. We focus less on the well-established idea of metaphors as a way for interface designers to help novice users learn how to interact with novel technology, and more on other ways metaphors can be used. We find that metaphors people use are contextually fluid, can change with the mode of conversation, and can reveal differences in how people perceive VUIs compared to other devices. Not all metaphors are helpful, and some may be offensive. Analyzing this broader class of metaphors can help understand, perhaps even predict problems. Metaphor analysis can be a low-cost tool to inspire design creativity and facilitate complex discussions about sociotechnical issues, enabling us to spot potential opportunities and problems in the situated use of technologies.