arXiv subjects

Vivienne Bihe Chi

Publications and source records attributed to Vivienne Bihe Chi.

7 recordsLinked to original sources

arXiv2026

What Users Cannot See: Evaluating LLM Emotional Support Beyond User Preference

People increasingly turn to LLMs for emotional support, yet common evaluations reward responses that feel helpful and may miss consequential response behaviors. We introduce a theory-informed measurement framework that decomposes LLM emotional-support responses into Soothe (affective comfort), Reframe (cognitive perspective-shift), and Endorse (agreement with a user's causal or moral framing). Across 9,000 GPT-5.6 responses to 3,000 venting and advice-seeking Reddit posts, friend- and therapist-style personas both increased Soothe relative to default but moved Reframe and Endorse in opposite directions: friend prompting increased Endorse and reduced Reframe, a pattern that could reinforce users' existing appraisals and plausibly contribute to escalation risk, whereas therapist prompting did the reverse. Two independent LLM judges substantially agreed with clinically trained raters, yet lay raters detected only 60% of expert-confirmed Endorse instances and rated the personas similarly helpful and desirable. Preference-based evaluation thus has a blind spot: supportive presentation can obscure appraisal-reinforcing, potentially escalatory behavior.

cs.HC↗

arXiv2026

Optimized but Unowned: How AI-Authored Goals Undermine the Motivation They Are Meant to Drive

As AI tools become embedded in productivity and self-improvement contexts, a pressing question emerges: what happens when AI does the goal-setting for us? In a preregistered experiment (N = 470), we compared self-authored goals against LLM-authored goals derived from a personal reflection. LLM-generated goals scored higher on SMART criteria (|d| = 2.26), yet participants in the LLM condition reported lower psychological ownership (|d| = 1.38), commitment (|d| = 1.19), and perceived importance (|d| = 1.13). At two-week follow-up, 72.8% of self-authored participants had acted on two or more of their goals, compared to 46.6% in the LLM condition. Psychological ownership, not goal quality, mediated every downstream motivational outcome. Individuals low in trait self-efficacy, those most likely to seek AI assistance, experienced the steepest ownership erosion. These findings reveal a quality-motivation dissociation in AI-assisted goal-setting and identify authorship preservation as a design priority for AI tools deployed in identity-relevant, behavior-dependent tasks.

cs.HC↗

arXiv2026

Narrative Sharpens Gender Gaps: Surveying Film Characters with LLM Agents

Mainstream film is one of the richest sources of cultural content that AI systems learn from. Yet we have few tools for measuring the gender values it encodes. We present a proof-of-concept framework that turns fictional film characters into surveyable LLM agents. Using 160 U.S. films (1990--2019), we build 734 character agents from script dialogue and scene descriptions, condense their personas via expert-style reflections, and simulate World Values Survey gender-attitude responses. Agents reproduce systematic gender differences without explicit demographic prompting, suggesting attitudes emerge from behavior rather than identity labels. Benchmarked against historical survey data, agents exaggerate gender gaps and show greater decade-to-decade volatility than real populations. Narrative sharpens rather than homogenizes gender contrasts, complicating the consistent-input assumption underlying cultivation theory's mainstreaming mechanism. AI systems trained on such corpora may inherit this stylization before any model-level amplification occurs.

cs.HC↗

arXiv2026

What Does a Meow Mean? In Search of Intuitively Understandable Communication by a Nonverbal Companion Robot

Older adults living alone have a number of challenges, and robots can help with some of them--by providing reminders, initiating activity, or offering comfort. As part of developing a cat robot with limited assistive functions, we designed a set of nonverbal communication signals, both auditory (cat sounds) and visual (icons on a small display). To evaluate these signals we used a mixed-methods, user-centered approach. After a pilot study, a focus group with older adults suggested revisions to the initial signal set. A large-sample online experiment then tested whether adults over the age of 65 could accurately infer the robot's communicative intentions. When both visual and auditory signals were present, accuracy was high. When visual signals were absent, accuracy often decreased; when auditory signals were absent, accuracy sometimes increased. So the auditory signals were less helpful, except when the robot conveyed strong sentiments (e.g., purring while being petted).

cs.HC↗

arXiv2026

Voice-Based Chatbots for English Speaking Practice in Multilingual Low-Resource Indian Schools: A Multi-Stakeholder Study

Spoken English proficiency is a powerful driver of economic mobility for low-income Indian youth, yet opportunities for spoken practice remain scarce in schools. We investigate the deployment of a voice-based chatbot for English conversation practice across four low-resource schools in Delhi. Through a six-day field study combining observations and interviews, we captured the perspectives of students, teachers, and principals. Findings confirm high demand across all groups, with notable gains in student speaking confidence. Our multi-stakeholder analysis surfaced a tension in long-term adoption vision: students favored open-ended conversational practice, while administrators emphasized curriculum-aligned assessment. We offer design recommendations for voice-enabled chatbots in low-resource multilingual contexts, highlighting the need for more intelligible speech output for non-native learners, one-tap interactions with simplified interfaces, and actionable analytics for educators. Beyond language learning, our findings inform the co-design of future AI-based educational technologies that are socially sustainable within the complex ecosystem of low-resource schools.

cs.HC↗

arXiv2024

More than just a Tool: People's Perception and Acceptance of Prosocial Delivery Robots as Fellow Road Users

Service robots are increasingly deployed in public spaces, performing functional tasks such as making deliveries. To better integrate them into our social environment and enhance their adoption, we consider integrating social identities within delivery robots along with their functional identity. We conducted a virtual reality-based pilot study to explore people's perceptions and acceptance of delivery robots that perform prosocial behavior. Preliminary findings from thematic analysis of semi-structured interviews illustrate people's ambivalence about dual identity. We discussed the emerging themes in light of social identity theory, framing effect, and human-robot intergroup dynamics. Building on these insights, we propose that the next generation of delivery robots should use peer-based framing, an updated value proposition, and an interactive design that places greater emphasis on expressing intentionality and emotional responses.

cs.HC↗

arXiv2024

Should I Help a Delivery Robot? Cultivating Prosocial Norms through Observations

We propose leveraging prosocial observations to cultivate new social norms to encourage prosocial behaviors toward delivery robots. With an online experiment, we quantitatively assess updates in norm beliefs regarding human-robot prosocial behaviors through observational learning. Results demonstrate the initially perceived normativity of helping robots is influenced by familiarity with delivery robots and perceptions of robots' social intelligence. Observing human-robot prosocial interactions notably shifts peoples' normative beliefs about prosocial actions; thereby changing their perceived obligations to offer help to delivery robots. Additionally, we found that observing robots offering help to humans, rather than receiving help, more significantly increased participants' feelings of obligation to help robots. Our findings provide insights into prosocial design for future mobility systems. Improved familiarity with robot capabilities and portraying them as desirable social partners can help foster wider acceptance. Furthermore, robots need to be designed to exhibit higher levels of interactivity and reciprocal capabilities for prosocial behavior.

cs.RO↗