arXiv subjects

Wanqi Zhang

Publications and source records attributed to Wanqi Zhang.

5 recordsLinked to original sources

arXiv2026

Why That Robot? A Qualitative Analysis of Justification Strategies for Robot Color Selection Across Occupational Contexts

As robots increasingly enter the workforce, human-robot interaction (HRI) must address how implicit social biases influence user preferences. This paper investigates how users rationalize their selections of robots varying in skin tone and anthropomorphic features across different occupations. By qualitatively analyzing 4,146 open-ended justifications from 1,038 participants, we map the reasoning frameworks driving robot color selection across four professional contexts. We developed and validated a comprehensive, multidimensional coding scheme via human--AI consensus ($\kappa = 0.73$). Our results demonstrate that while utilitarian \textit{Functionalism} is the dominant justification strategy (52\%), participants systematically adapted these practical rationales that align with existing racial and occupational stereotypes. Furthermore, we reveal that bias frequently operates beneath conscious rationalization: exposure to racial stereotype primes significantly shifted participants' color choices, yet their spoken justifications remained masked by standard affective or task-related reasoning. We also found that demographic backgrounds significantly shape justification strategies, and that robot shape strongly modulates color interpretation. Specifically, as robots become highly anthropomorphic, users increasingly retreat from functional reasoning toward \textit{Machine-Centric} de-racialization. Through these empirical results, we provide design implications to help reduce the perpetuation of societal biases in future workforce robots.

cs.RO↗

arXiv2026

Tackling the Scaffolding Paradox: A Person-Centered Adaptive Robotic Interview Coach

Job interview anxiety is a prevalent challenge among university students and can undermine both performance and confidence in high-stakes evaluative situations. Social robots have shown promise in reducing anxiety through emotional support, yet how such systems should balance psychological safety with effective instructional guidance remains an open question. In this work, we present a three-phase iterative design study of a robotic interview coach grounded in Person-Centered Therapy (PCT) and instructional scaffolding theory. Across three weekly sessions (N=8), we systematically explored how different interaction strategies shape users' emotional experience, cognitive load, and perceived utility. Phase I demonstrated that a PCT-based robot substantially increased perceived psychological safety but introduced a Safety-Guidance Gap, in which users felt supported yet insufficiently coached. Phase II revealed a Scaffolding Paradox: immediate feedback improved clarity but disrupted conversational flow and increased cognitive load, whereas delayed feedback preserved realism but lacked actionable specificity. To resolve this tension, Phase III introduced an Agency-Driven Interaction Mode that allowed users to opt in to feedback dynamically. Qualitative findings indicated that user control acted as an anxiety buffer, restoring trust, reducing overload, and reframing the interaction as collaborative rather than evaluative. Quantitative measures further showed significant reductions in interview-related social and communication anxiety, while maintaining high perceived warmth and therapeutic alliance. We synthesize these findings into an Adaptive Scaffolding Ecosystem framework, highlighting user agency as a key mechanism for balancing emotional support and instructional guidance in social robot coaching systems.

cs.HC↗

arXiv2026

Bridging Psychological Safety and Skill Guidance: An Adaptive Robotic Interview Coach

Social robots hold promise for reducing job interview anxiety, yet designing agents that provide both psychological safety and instructional guidance remains challenging. Through a three-phase iterative design study (N = 8), we empirically mapped this tension. Phase I revealed a "Safety-Guidance Gap": while a Person-Centered Therapy (PCT) robot established safety (d = 3.27), users felt insufficiently coached. Phase II identified a "Scaffolding Paradox": rigid feedback caused cognitive overload, while delayed feedback lacked specificity. In Phase III, we resolved these tensions by developing an Agency-Driven Interaction Layer. Synthesizing our empirical findings, we propose the Adaptive Scaffolding Ecosystem, a conceptual framework that redefines robotic coaching not as a static script, but as a dynamic balance between affective support and instructional challenge, mediated by user agency.

cs.HC↗

arXiv2025

From Human Bias to Robot Choice: How Occupational Contexts and Racial Priming Shape Robot Selection

As artificial agents increasingly integrate into professional environments, fundamental questions have emerged about how societal biases influence human-robot selection decisions. We conducted two comprehensive experiments (N = 1,038) examining how occupational contexts and stereotype activation shape robotic agent choices across construction, healthcare, educational, and athletic domains. Participants made selections from artificial agents that varied systematically in skin tone and anthropomorphic characteristics. Our study revealed distinct context-dependent patterns. Healthcare and educational scenarios demonstrated strong favoritism toward lighter-skinned artificial agents, while construction and athletic contexts showed greater acceptance of darker-toned alternatives. Participant race was associated with systematic differences in selection patterns across professional domains. The second experiment demonstrated that exposure to human professionals from specific racial backgrounds systematically shifted later robotic agent preferences in stereotype-consistent directions. These findings show that occupational biases and color-based discrimination transfer directly from human-human to human-robot evaluation contexts. The results highlight mechanisms through which robotic deployment may unintentionally perpetuate existing social inequalities.

cs.RO↗

arXiv2020

Towards in-store multi-person tracking using head detection and track heatmaps

Computer vision algorithms are being implemented across a breadth of industries to enable technological innovations. In this paper, we study the problem of computer vision based customer tracking in retail industry. To this end, we introduce a dataset collected from a camera in an office environment where participants mimic various behaviors of customers in a supermarket. In addition, we describe an illustrative example of the use of this dataset for tracking participants based on a head tracking model in an effort to minimize errors due to occlusion. Furthermore, we propose a model for recognizing customers and staff based on their movement patterns. The model is evaluated using a real-world dataset collected in a supermarket over a 24-hour period that achieves 98% accuracy during training and 93% accuracy during evaluation.

cs.CV↗