Smit Desai

Xiuqi Tommy Zhu, Xiaoan Liu, Casper Harteveld et al.

Non-Display Smart Glasses hold the potential to support everyday activities by combining continuous environmental sensing with voice-only interaction powered by large language models (LLMs). Understanding how conversational successes and breakdowns arise in everyday contexts can better inform the design of future voice-only interfaces. To investigate this, we conducted a month-long collaborative autoethnography (n=2) to identify patterns of successes and breakdowns when using such devices. We then compare these patterns with prior findings on voice-only interactions to highlight the unique affordances and opportunities offered by non-display smart glasses.

Smit Desai, Jessie Chin, Dakuo Wang et al.

Metaphors play a critical role in shaping user experiences with Voice User Interfaces (VUIs), yet existing designs often rely on static, human-centric metaphors that fail to adapt to diverse contexts and user needs. This paper introduces Metaphor-Fluid Design, a novel approach that dynamically adjusts metaphorical representations based on conversational use-contexts. We compare this approach to a Default VUI, which characterizes the present implementation of commercial VUIs commonly designed around the persona of an assistant, offering a uniform interaction style across contexts. In Study 1 (N=130), metaphors were mapped to four key use-contexts-commands, information seeking, sociality, and error recovery-along the dimensions of formality and hierarchy, revealing distinct preferences for task-specific metaphorical designs. Study 2 (N=91) evaluates a Metaphor-Fluid VUI against a Default VUI, showing that the Metaphor-Fluid VUI enhances perceived intention to adopt, enjoyment, and likability by aligning better with user expectations for different contexts. However, individual differences in metaphor preferences highlight the need for personalization. These findings challenge the one-size-fits-all paradigm of VUI design and demonstrate the potential of Metaphor-Fluid Design to create more adaptive and engaging human-AI interactions.

Menglin Zhao, Zhuorui Yong, Ruijia Guan et al.

Serious illness conversations (SICs), discussions between clinical care teams and patients with serious, life-limiting illnesses about their values, goals, and care preferences, are critical for patient-centered care. Without these conversations, patients often receive aggressive interventions that may not align with their goals. Clinical care teams face significant barriers when conducting serious illness conversations with older adult patients in Emergency Department (ED) settings, where most older adult patients lack documented treatment goals. To understand current practices and identify AI support opportunities, we conducted interviews with two domain experts and nine ED clinical care team members. Through thematic analysis, we characterized a four-phase serious illness conversation workflow (identification, preparation, conduction, documentation) and identified key needs and challenges at each stage. Clinical care teams struggle with fragmented EHR data access, time constraints, emotional preparation demands, and documentation burdens. While participants expressed interest in AI tools for information synthesis, conversational support, and automated documentation, they emphasized preserving human connection and clinical autonomy. We present design guidelines for AI tools supporting SIC workflows that fit within existing clinical practices. This work contributes empirical understanding of ED-based serious illness conversations and provides design considerations for AI in high-stakes clinical environments.

Nima Zargham, Mateusz Dubiel, Smit Desai et al.

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.

Niharika Mathur, Hasibur Rahman, Smit Desai

Large Language Model-based Voice Assistants (LLM-VAs) are increasingly deployed in assistive settings for older adults, yet little is known about how an agent's personality shapes user perceptions of its explanations. This paper presents a mixed factorial experiment (N=140) examining how agreeableness and extraversion in an LLM-VA ("Robin") influence older adults' perceptions across seven measures: empathy, likeability, trust, reliance, satisfaction, intention to adopt, and perceived intelligence. Results reveal that high agreeableness drove stronger empathy perceptions, while low agreeableness consistently penalized likeability. Importantly, perceived intelligence remained unaffected by personality, suggesting that personality shapes sociability without altering competence perceptions. Real-time environmental explanations outperformed conversational history explanations on five measures, with advantages concentrated in emergency contexts. Notably, highly agreeable participants were especially critical of low-agreeableness agents, revealing a user-agent personality congruence effect. These findings offer design implications for personality-aware, context-sensitive LLM-VAs in assistive settings.

Hasibur Rahman, Smit Desai

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.