arXiv subjects

Yueyan Liu

Publications and source records attributed to Yueyan Liu.

5 recordsLinked to original sources

arXiv2026

Ethics and Social Responsibility in AI-Assisted Interviewing: An LLM-in-the-Loop Study of AI-Generated Follow-Up Questions

Semi-structured interviews rely on timely, context-sensitive follow-up questions, yet interviewers' cognitive load and limited domain familiarity can constrain probing depth. We report findings from an LLM-in-the-loop Wizard-of-Oz (WoZ) study that simulates an AI follow-up assistant in live interviewing while preserving human oversight. In our setup, a co-interviewer selectively relayed and could edit AI-generated follow-up questions (AGQs) produced in real time by GPT-4o, enabling a realistic approximation of deployment without fully automating the interaction. Across 17 interviewers with varied qualitative-method expertise, participants raised five interlocking concerns: (1) harmful or discriminatory language and unpredictable interaction harms, (2) undermining interviewees' sense of respect through divided attention and missing nonverbal cues, (3) technology-based participation inequality, (4) unclear responsibility when harms occur, and (5) privacy, disclosure, and compliance risks when AI listens, records, or transcribes sensitive content. We translate these concerns into design and governance implications for safer, more respectful, and more accountable AI-assisted interviewing.

cs.HC↗

arXiv2026

Twitch Third-Party Developers' Support Seeking and Provision Practices on Discord

Third-party developers (TPDs) often turn to online communities for support when they can't get immediate responses from the platform. Twitch, as a leading live streaming platform, attracted many TPDs and formed an online support community on Discord. This study explores TPDs' support practices via mixed method (a topic modeling to identify topics related to support seeking and provision first and a follow-up in-depth qualitative analysis with these topics) and found that: (1) TPDs' support-seeking practices around social, technical, and policy matters are highly dependent on Twitch, and this dependence acts as a form of platform labor; (2) TPDs need to switch between Discord and Twitch regarding seeking and provision, exacerbating TPDs' platform labor; (3) TPDs' flexible role practices reflect the community's flourishing on Discord but require roles to bridge the two platforms and transfer informal support seeking to possible formal support from Twitch. We propose implications for effectively managing support seeking and provision between formal and informal spaces to improve the development of TPDs. We also contribute to community support practice and to platform ecology work in CSCW.

cs.HC↗

arXiv2025

Harnessing the Power of AI in Qualitative Research: Role Assignment, Engagement, and User Perceptions of AI-Generated Follow-Up Questions in Semi-Structured Interviews

Semi-structured interviews highly rely on the quality of follow-up questions, yet interviewers' knowledge and skills may limit their depth and potentially affect outcomes. While many studies have shown the usefulness of large language models (LLMs) for qualitative analysis, their possibility in the data collection process remains underexplored. We adopt an AI-driven "Wizard-of-Oz" setup to investigate how real-time LLM support in generating follow-up questions shapes semi-structured interviews. Through a study with 17 participants, we examine the value of LLM-generated follow-up questions, the evolving division of roles, relationships, collaborative behaviors, and responsibilities between interviewers and AI. Our findings (1) provide empirical evidence of the strengths and limitations of AI-generated follow-up questions (AGQs); (2) introduce a Human-AI collaboration framework in this interview context; and (3) propose human-centered design guidelines for AI-assisted interviewing. We position LLMs as complements, not replacements, to human judgment, and highlight pathways for integrating AI into qualitative data collection.

cs.HC↗

arXiv2025

Customer Service Representative's Perception of the AI Assistant in an Organization's Call Center

The integration of various AI tools creates a complex socio-technical environment where employee-customer interactions form the core of work practices. This study investigates how customer service representatives (CSRs) at the power grid service customer service call center perceive AI assistance in their interactions with customers. Through a field visit and semi-structured interviews with 13 CSRs, we found that AI can alleviate some traditional burdens during the call (e.g., typing and memorizing) but also introduces new burdens (e.g., earning, compliance, psychological burdens). This research contributes to a more nuanced understanding of AI integration in organizational settings and highlights the efforts and burdens undertaken by CSRs to adapt to the updated system.

cs.HC↗

arXiv2025

Exploring the Privacy and Security Challenges Faced by Migrant Domestic Workers in Chinese Smart Homes

The growing use of smart home devices poses considerable privacy and security challenges, especially for individuals like migrant domestic workers (MDWs) who may be surveilled by their employers. This paper explores the privacy and security challenges experienced by MDWs in multi-user smart homes through in-depth semi-structured interviews with 26 MDWs and 5 staff members of agencies that recruit and/or train domestic workers in China. Our findings reveal that the relationships between MDWs, their employers, and agencies are characterized by significant power imbalances, influenced by Chinese cultural and social factors (such as Confucianism and collectivism), as well as legal ones. Furthermore, the widespread and normalized use of surveillance technologies in China, particularly in public spaces, exacerbates these power imbalances, reinforcing a sense of constant monitoring and control. Drawing on our findings, we provide recommendations to domestic worker agencies and policymakers to address the privacy and security challenges facing MDWs in Chinese smart homes.

cs.HC↗