arXiv subjects

Xinning Gui

Publications and source records attributed to Xinning Gui.

14 recordsLinked to original sources

arXiv2026

Gaming Together on Discord: Teen Gamer's Cross-Platform Practices

Discord is one of the most popular communication platforms among gamers. While prior research has highlighted its role in community building, relatively little attention has been paid to its original gaming context-how it shapes gameplay and social experiences. To address this gap, we conducted semi-structured interviews with 16 teenage Discord users. Through reflexive thematic analysis, we show how players leverage Discord to create more collaborative and socially enriched experiences that extend beyond the game itself. However, gaming together on Discord also resulted in social and security risks. We conceptualize gaming together on Discord as a cross-platform practice that extends gameplay beyond a game and supports players' social needs. Additionally, cross-platform practice also introduces the 'platform gap,' where fragmented governance between platforms exposed players to risks. To address this tension, we propose design implications aimed at bridging the platform gap, strengthening communication channels, and supporting safer gaming experiences.

cs.HC↗

arXiv2026

Children's Online Safety Risks and Ethical Considerations in XR Games

Emerging extended reality technologies are reshaping how children play, learn, and socialize. Yet, they also present serious safety risks. Gaming, a primary form of entertainment for children, is also one of the key applications of XR. While XR platforms offer immersive and engaging gaming experiences, recent news has highlighted safety concerns such as car accidents, lower judgment for real-world situations, and exposure to disturbing content like virtual rape. This research examines how XR game design may lead to online safety risks for children. Through analysis of player forums, game developer forums, and interviews with child players, we identify harmful XR design patterns, explore how developers collaboratively generate and implement risky game ideas, and document children's firsthand experiences of online safety risks. Existing ethical frameworks often fail to address the immersive and socially dynamic nature of XR games. We advocate for a child-centered, design-aware approach to ethical considerations in XR games, urging platforms and policymakers to prioritize children's developmental needs. Our work aims to help shape safer, more inclusive XR environments through research and cross-sector collaboration.

cs.HC↗

arXiv2026

Teen Vigilance: Navigating Risky Social Interactions on Discord

Teenagers are avid users of Discord, a fast growing platform for synchronous communication where they often interact with strangers. Because Discord combines private DMs, semi-private voice channels, and public servers in one place, it creates a hybrid environment that can produce complex and underexplored safety risks for teenagers. Drawing on 16 interviews with teenage Discord users, this study examines their strategies for navigating risky social interactions in the platform. Our findings reveal that when teenagers encounter risks during social interactions, they exercise vigilance by evaluating suspicious interactions before forming friendships, using safety tools, and engaging in controlled risk-taking to safeguard their privacy and security. At the community level, they mitigate risks through selective participation in servers, a practice supported by vigilant governance structures. We discuss how vigilance enables teenagers to act during risky encounters to protect themselves, advancing understanding of teenagers' agency in risk navigation and informing teen-centered designs for safer online environments.

cs.HC↗

arXiv2026

Improving Family Co-Play Experiences through Family-Centered Design

Cooperative play (co-play) is often positioned as a family-beneficial practice that can strengthen parent-child bonds and support parental mediation in games. Yet co-play in user-generated virtual worlds (UGVWs) can be disrupted by real-time harms that parents cannot easily prevent. Roblox, a platform with millions of user-generated virtual worlds and a large child player base, illustrates this challenge. Prior work on harmful UGVW design highlights risks beyond content problems, including manipulative monetization prompts, unmoderated social interactions, emergent in-world behaviors, and narrative designs that may normalize harmful ideologies. Current governance and moderation approaches, largely adapted from social media, focus on static artifacts and often fail to capture interactive and emergent harms in virtual worlds. This workshop paper asks: how might UGVWs and their platforms be designed to minimize harms that specifically impair family co-play experiences?

cs.HC↗

arXiv2026

Privacy Control in Conversational LLM Platforms: A Walkthrough Study

Large language models (LLMs) are increasingly integrated into daily life through conversational interfaces, processing user data via natural language inputs and exhibiting advanced reasoning capabilities, which raises new concerns about user control over privacy. While much research has focused on potential privacy risks, less attention has been paid to the data control mechanisms these platforms provide. This study examines six conversational LLM platforms, analyzing how they define and implement features for users to access, edit, delete, and share data. Our analysis reveals an emerging paradigm of data control in conversational LLM platforms, where user data is generated and derived through interaction itself, natural language enables flexible yet often ambiguous control, and multi-user interactions with shared data raise questions of co-ownership and governance. Based on these findings, we offer practical insights for platform developers, policymakers, and researchers to design more effective and usable privacy controls in LLM-powered conversational interactions.

cs.HC↗

arXiv2025

Customizing Emotional Support: How Do Individuals Construct and Interact With LLM-Powered Chatbots

Personalized support is essential to fulfill individuals' emotional needs and sustain their mental well-being. Large language models (LLMs), with great customization flexibility, hold promises to enable individuals to create their own emotional support agents. In this work, we developed ChatLab, where users could construct LLM-powered chatbots with additional interaction features including voices and avatars. Using a Research through Design approach, we conducted a week-long field study followed by interviews and design activities (N = 22), which uncovered how participants created diverse chatbot personas for emotional reliance, confronting stressors, connecting to intellectual discourse, reflecting mirrored selves, etc. We found that participants actively enriched the personas they constructed, shaping the dynamics between themselves and the chatbot to foster open and honest conversations. They also suggested other customizable features, such as integrating online activities and adjustable memory settings. Based on these findings, we discuss opportunities for enhancing personalized emotional support through emerging AI technologies.

cs.HC↗

arXiv2024

Understanding and Mitigating Harmful Design in User-Generated Virtual Worlds

Virtual space offers innovative ways for individuals to engage with one another in a digital setting. Prominent virtual social platforms, such as Facebook Spaces, VR Chat, and AltspaceVR, facilitate social connections, allowing users to interact seamlessly. Additionally, certain video games, like Second Life and World of Warcraft, are set within these virtual spaces as well, providing immersive player experiences. As the popularity of virtual space grows, various companies have begun to democratize the process of creating these spaces, shifting the development from skilled professionals to hobbyist creators. Platforms like Minecraft, Roblox, and RecRoom enable users to create and publish their own virtual environments, hosting a wide range of interactions and narratives. This shift echoes the rise of user-generated content, where content creators create and publish content on platforms, such as social media platforms [6]. For example, YouTubers upload videos on YouTube and Reddit users post text-based content on Reddit. For a long time, user-generated content has predominantly contained text, videos, and images. However, with the emergence of virtual spaces, some platforms now allow creators to create and publish their own virtual spaces, leading to the emergence of user-generated virtual worlds.

cs.HC↗

arXiv2024

"At the end of the day, I am accountable": Gig Workers' Self-Tracking for Multi-Dimensional Accountability Management

Tracking is inherent in and central to the gig economy. Platforms track gig workers' performance through metrics such as acceptance rate and punctuality, while gig workers themselves engage in self-tracking. Although prior research has extensively examined how gig platforms track workers through metrics -- with some studies briefly acknowledging the phenomenon of self-tracking among workers -- there is a dearth of studies that explore how and why gig workers track themselves. To address this, we conducted 25 semi-structured interviews, revealing how gig workers self-tracking to manage accountabilities to themselves and external entities across three identities: the holistic self, the entrepreneurial self, and the platformized self. We connect our findings to neoliberalism, through which we contextualize gig workers' self-accountability and the invisible labor of self-tracking. We further discuss how self-tracking mitigates information and power asymmetries in gig work and offer design implications to support gig workers' multi-dimensional self-tracking.

cs.HC↗

arXiv2023

AutoML in The Wild: Obstacles, Workarounds, and Expectations

Automated machine learning (AutoML) is envisioned to make ML techniques accessible to ordinary users. Recent work has investigated the role of humans in enhancing AutoML functionality throughout a standard ML workflow. However, it is also critical to understand how users adopt existing AutoML solutions in complex, real-world settings from a holistic perspective. To fill this gap, this study conducted semi-structured interviews of AutoML users (N=19) focusing on understanding (1) the limitations of AutoML encountered by users in their real-world practices, (2) the strategies users adopt to cope with such limitations, and (3) how the limitations and workarounds impact their use of AutoML. Our findings reveal that users actively exercise user agency to overcome three major challenges arising from customizability, transparency, and privacy. Furthermore, users make cautious decisions about whether and how to apply AutoML on a case-by-case basis. Finally, we derive design implications for developing future AutoML solutions.

cs.HC↗

arXiv2022

User Experience of Symptom Checkers: A Systematic Review

This review reports the user experience of symptom checkers, aiming to characterize users studied in the existing literature, identify the aspects of user experience of symptom checkers that have been studied, and offer design suggestions. Our literature search resulted in 31 publications. We found that (1) most symptom checker users are relatively young; (2) eight relevant aspects of user experience have been explored, including motivation, trust, acceptability, satisfaction, accuracy, usability, safety or security, and functionality; (3) future symptom checkers should improve their accuracy, safety, and usability.

cs.HC↗

arXiv2021

Data Engagement Reconsidered: A Study of Automatic Stress Tracking Technology in Use

In today's fast-paced world, stress has become a growing health concern. While more automatic stress tracking technologies have recently become available on wearable or mobile devices, there is still a limited understanding of how they are actually used in everyday life. This paper presents an empirical study of automatic stress-tracking technologies in use in China, based on semi-structured interviews with 17 users. The study highlights three challenges of stress-tracking data engagement that prevent effective technology usage: the lack of immediate awareness, the lack of pre-required knowledge, and the lack of corresponding communal support. Drawing on the stress-tracking practices uncovered in the study, we bring these issues to the fore, and unpack assumptions embedded in related works on self-tracking and how data engagement is approached. We end by calling for a reconsideration of data engagement as part of self-tracking practices with technologies rather than simply looking at the user interface.

cs.HC↗

arXiv2021

Self-Diagnosis through AI-enabled Chatbot-based Symptom Checkers: User Experiences and Design Considerations

Recently, there has been a growing interest in developing AI-enabled chatbot-based symptom checker (CSC) apps in the healthcare market. CSC apps provide potential diagnoses for users and assist them with self-triaging based on Artificial Intelligence (AI) techniques using human-like conversations. Despite the popularity of such CSC apps, little research has been done to investigate their functionalities and user experiences. To do so, we conducted a feature review, a user review analysis, and an interview study. We found that the existing CSC apps lack the functions to support the whole diagnostic process of an offline medical visit. We also found that users perceive the current CSC apps to lack support for a comprehensive medical history, flexible symptom input, comprehensible questions, and diverse diseases and user groups. Based on these results, we derived implications for the future features and conversational design of CSC apps.

cs.HC↗

arXiv2021

The Medical Authority of AI: A Study of AI-enabled Consumer-facing Health Technology

Recently, consumer-facing health technologies such as Artificial Intelligence (AI)-based symptom checkers (AISCs) have sprung up in everyday healthcare practice. AISCs solicit symptom information from users and provide medical suggestions and possible diagnoses, a responsibility that people usually entrust with real-person authorities such as physicians and expert patients. Thus, the advent of AISCs begs a question of whether and how they transform the notion of medical authority in everyday healthcare practice. To answer this question, we conducted an interview study with thirty AISC users. We found that users assess the medical authority of AISCs using various factors including automated decisions and interaction design patterns of AISC apps, associations with established medical authorities like hospitals, and comparisons with other health technologies. We reveal how AISCs are used in healthcare delivery, discuss how AI transforms conventional understandings of medical authority, and derive implications for designing AI-enabled health technology.

cs.HC↗

arXiv2020

Mediating Community-AI Interaction through Situated Explanation: The Case of AI-Led Moderation

Artificial intelligence (AI) has become prevalent in our everyday technologies and impacts both individuals and communities. The explainable AI (XAI) scholarship has explored the philosophical nature of explanation and technical explanations, which are usually driven by experts in lab settings and can be challenging for laypersons to understand. In addition, existing XAI research tends to focus on the individual level. Little is known about how people understand and explain AI-led decisions in the community context. Drawing from XAI and activity theory, a foundational HCI theory, we theorize how explanation is situated in a community's shared values, norms, knowledge, and practices, and how situated explanation mediates community-AI interaction. We then present a case study of AI-led moderation, where community members collectively develop explanations of AI-led decisions, most of which are automated punishments. Lastly, we discuss the implications of this framework at the intersection of CSCW, HCI, and XAI.

cs.HC↗