He Zhang

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.

Visual impairments affect upwards of 2.2 billion people worldwide. As AI systems increasingly support navigation for people with visual impairments, how uncertainty is communicated becomes critical. Prior work shows that communicating uncertainty can improve trust calibration and decision-making, yet it remains underexplored in assistive navigation. This project develops an uncertainty-aware assistive navigation architecture that integrates AI uncertainty into auditory guidance during real-time scene descriptions. Rather than using explicit confidence statements, the prototype embeds uncertainty into speech via variations in tone, pacing, and emphasis. The prototype combines a real-time collision-warning module with a semantic reasoning layer powered by a large language model (LLM). When generating scene descriptions, token-level uncertainty is mapped to auditory prosodic cues, enabling users to implicitly gauge the system’s confidence without disrupting navigational task flow. This work presents a high-fidelity prototype that treats AI confidence as an interaction design feature, illustrating how model uncertainty can be rendered perceptible in assistive navigation and reframed as a human-factors design parameter.

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.

Grounded in catharsis theory, expressive vocalization plays a critical role in emotional release, yet is often constrained by social norms or physical limitations. We present a VR demo, Wolfborn, that enables stress relief through embodied pseudo-vocalization without requiring audible speech. Prior VR animal-avatar systems primarily rely on visual and gestural embodiment, overlooking the cathartic role of vocal expression. Users embody a wolf and experience the release of howling through a multimodal pseudo-vocalization mechanism integrating visual, auditory, and tactile feedback, while preserving silence in the physical world. This design fosters a strong Sense of Agency (SoA) over vocal production and supports high-arousal emotional release through the wolf’s symbolic association with freedom. User studies show significant gains in embodiment and perceived emotional release compared to visual-only avatars. This demo highlights silent, socially acceptable pseudo-vocalization as a novel design space for inclusive VR-based emotional regulation.