arXiv subjects

Xiaojuan Ma

Publications and source records attributed to Xiaojuan Ma.

At least 19 recordsLinked to original sources

arXiv2026

Moving Phones, Active Peers: Exploring the Effect of Animated Phones as Facilitators in In-Person Group Discussion

In today's in-person group discussions, smartphones are integrated as intelligent workstations; yet given their co-presence in such face-to-face interactions, whether and how they may enhance people's behavioral engagement with others remains underexplored. This work investigates how animating personal smartphones to move expressively, without compromising regular functions, can transform them into embodied facilitators for co-located group interaction. In the four-stranger small-group discussion setting, guided by Tuckman's group-development theory, we conducted a design workshop (n=12) to identify problematic group-work circumstances and design expressive, attention-efficient animated phone facilitations. Subsequently, we developed AnimaStand, a movement-enabled phone stand that animates phones to deliver facilitation cues according to conversation dynamics. In a between-subjects Wizard-of-Oz study (n=56) with four-stranger group discussions, where everyone's phone was on an AnimaStand, the facilitations re-engaged inactive members, balancing interpersonal dynamics and potentially enhancing relationships, without improving task performance. We finally discuss prospects for more generalizable animated personal device facilitation.

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arXiv2026

TactiPlay: Multi-Granularity Tactical Parsing and Video-Anchored Match Review for Amateur Badminton Players

Amateur badminton players increasingly record matches, yet existing tools provide only aggregate statistics or generic summaries, leaving most unable to extract tactical insights without expert guidance. A formative study (N=8) reveals the need for multi-granularity, video-anchored tactical analysis centered on rallies. We derive a taxonomy of performance issues from national-level athletes' annotations and present TactiPlay, an interactive system that instantiates an expert-taxonomy-guided, rally-level, video-anchored review workflow. The system's analytical pipeline organizes match events into taxonomy-grounded feedback, while its interface links structured reports to rally summaries, video evidence, and court visualizations. A within-subjects study (N=16) shows that TactiPlay elicits more frequent, concrete, actionable, and appropriate reflections than a report-and-statistics baseline. These findings show how organizing reviewed match evidence into a taxonomy-guided, video-linked workflow can support amateur players' tactical reflection.

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arXiv2026

CoNewsReader: Supporting Comprehensive Understanding and Raising Critical Thoughts on Social Media News Through Comments

Critical news reading (CNR), which requires grasping the holistic ideas of and raising critical thoughts on the news, is beneficial yet challenging for general people who usually get information on daily social media. Comments under the news can aid CNR by providing complementary information and other readers' diverse and critical thoughts. However, it is under-investigated how to leverage these comments to support users in CNR. In this paper, we first derive user requirements for a comment-based CNR tool from literature and a formative study (N=12). Then, we develop CoNewsReader, a comment-based interactive CNR tool powered by a large language model. CoNewsReader supports users in grasping the news idea with complementary information from comments, filtering useful comments for CNR, and getting questions generated based on the comments to conduct critical thinking. Our within-subjects study with 24 university students indicates that compared to a baseline news reading interface in social media, participants with CoNewsReader have a more engaging CNR experience and perform better on comprehending the news and raising critical thoughts. We discuss design considerations for supporting reading tasks with user- and machine-generated content.

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arXiv2026

The Speculative Future of Conversational AI for Neurocognitive Disorder Screening: a Multi-Stakeholder Perspective

Neurocognitive disorders (NCDs), such as Alzheimer's disease, are globally prevalent and require scalable screening methods for proactive management. Prior research has explored the potential of technologies like conversational AI (CAI) to administer NCD screening tests. However, challenges remain in designing CAI-based solutions that make routine NCD screening socially acceptable, engaging, and capable of encouraging early medical consultation. In this study, we conducted interviews with 36 participants, including clinicians, individuals at risk of NCDs, and their caregivers, to explore the speculative future of adopting CAI for NCD screening. Our findings reveal shared expectations, such as deploying CAI in home or community settings to reduce social stress. Nonetheless, conflicts emerged among stakeholders, for example, users' need for emotional support may conflict with clinicians' preference for CAI's professional and standardized administration. Then, we look into the user journey of NCD screening based on the current practice of manual screening and the expected CAI-supported screening. Finally, leveraging the human-centered approach, we provide actionable implications for future CAI design in NCD screening.

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arXiv2026

HeartSway: Exploring Biodata as Poetic Traces in Public Space

Human traces scattered across urban landscapes can signify our everyday lives and societal vibrancy in subtle and poetic forms. In this paper, we explore how designed technology can engage biodata as evocative traces. To this end, we present the design, implementation, and evaluation of HeartSway, an interactive hammock that captures a user's heart rate and micro-movements as traces and replays them as an embodied experience for the next visitor. Through a qualitative field study (N=10), we find that HeartSway evokes feelings of connection, curiosity about prior users, and appreciation for shared human vitality. Our work contributes to understanding anonymous archival biodata as a design material for experiential urban traces. We offer design considerations for intimate asynchronous encounters between strangers in public spaces and for reimagining public amenities.

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arXiv2026

Exploring the Grassroots Understanding and Practices of Collective Memory Co-Contribution in a University Community

Collective memory -- community members' interconnected memories and impressions of the group -- is essential to the community's culture and identity. Its development requires members' continuous participatory contribution and sensemaking. However, existing works mainly adopt a holistic sociological perspective to analyze well-developed collective memory, less focusing on member-level conceptualization of this possession or what the co-contribution practices can be. Therefore, this work alternatively adopts the latter perspective and probes such interpretative and interactional patterns with two mobile systems. With one being a locative narrative and exploration system condensed from existing literature's design frameworks, and the other being a conventional online forum representing current practices, they served as the anchors of observation for our two-week, mixed-methods field study (n=38) on a university campus. A core debate we have identified was to retrospectively contemplate or document the presence as a history for the future. This also subsequently impacted the narrative focuses, expectations of collective memory constituents, and the ways participants seek inspiration from the group. We further extracted design considerations that could better embrace the diverse conceptualizations of collective memory and bond different community members together. Lastly, revisiting and reflecting on our design, we provided extra insights on designing devoted locative narrative experiences for community-driven UGC platforms.

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arXiv2026

Schemex: Discovering Structural Abstractions from Examples

Creative and communicative work is often underpinned by implicit structures, such as the Hero's Journey in storytelling, design patterns in software, or chord progressions in music. People often learn these structures from examples - a process known as schema induction. However, because schemas are abstract and implicit, they are difficult to discover: shared structural patterns are obscured by surface-level variation, and balancing generality with specificity is challenging. We present Schemex, an interactive AI workflow that systematically supports schema induction by decomposing it into three tractable stages: clustering examples, abstracting candidate schemas, and contrastively refining them by generating new instances and comparing against originals. Studies show that Schemex produces more actionable schemas than a frontier baseline without sacrificing generalizability, with participants uncovering deep and nuanced structural patterns. We also discuss design implications for the cognitive role of interactive process in structure discovery.

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arXiv2026

Designing AI-Infused Interactive Systems for Online Communities: A Systematic Literature Review

AI-infused systems have demonstrated remarkable capabilities in addressing diverse human needs within online communities. Their widespread adoption has shaped user experiences and community dynamics at scale. However, designing such systems requires a clear understanding of user needs, careful design decisions, and robust evaluation. While research on AI-infused systems for online communities has flourished in recent years, a comprehensive synthesis of this space remains absent. In this work, we present a systematic review of 77 studies, analyzing the systems they propose through three lenses: the challenges they aim to address, their design functionalities, and the evaluation strategies employed. The first two dimensions are organized around four core aspects of community participation: contribution, consumption, mediation, and moderation. Our analysis identifies common design and evaluation patterns, distills key design considerations, and highlights opportunities for future research on AI-infused systems in online communities.

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arXiv2026

Exploring a Gamified Personality Assessment Method through Interaction with LLM Agents Embodying Different Personalities

The low-intrusion and automated personality assessment is receiving increasing attention in psychology and human-computer interaction fields. This study explores an interactive approach for personality assessment, focusing on the multiplicity of personality representation. We propose a framework of Gamified Personality Assessment through Multi-Personality Representations (Multi-PR GPA). The framework leverages Large Language Models to empower virtual agents with different personalities. These agents elicit multifaceted human personality representations through engaging in interactive games. Drawing upon the multi-type textual data generated throughout the interaction, it achieves personality assessments with interpretable insights. Grounded in the classic Big Five personality theory, we developed a prototype system and conducted a user study to evaluate the efficacy of Multi-PR GPA. The results affirm the effectiveness of our approach in personality assessment and demonstrate its superior performance when considering the multiplicity of personality representation. Error structure analysis further revealed systematic assessment biases in LLMs, which multi-context aggregation partially mitigated.

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arXiv2026

"Without AI, I Would Never Share This Online": Unpacking How LLMs Catalyze Women's Sharing of Gendered Experiences on Social Media

Sharing gendered experiences on social media has been widely recognized as supporting women's personal sense-making and contributing to digital feminism. However, there are known concerns, such as fear of judgment and backlash, that may discourage women from posting online. In this study, we examine a recurring practice on Xiaohongshu, a popular Chinese social media platform, in which women share their gendered experiences alongside screenshots of conversations with LLMs. We conducted semi-structured interviews with 20 women to investigate whether and how interactions with LLMs might support women in articulating and sharing gendered experiences. Our findings reveal that, beyond those external concerns, women also hold self-imposed standards regarding what feels appropriate and worthwhile to share publicly. We further show how interactions with LLMs help women meet these standards and navigate such concerns. We conclude by discussing how LLMs might be carefully and critically leveraged to support women's everyday expression online.

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arXiv2026

When LLMs Enter Everyday Feminism on Chinese Social Media: Opportunities and Risks for Women's Empowerment

Everyday digital feminism refers to the ordinary, often pragmatic ways women articulate lived experiences and cultivate solidarity in online spaces. In China, such practices flourish on RedNote through discussions under hashtags like ''women's growth''. Recently, DeepSeek-generated content has been taken up as a new voice in these conversations. Given widely recognized gender biases in LLMs, this raises critical concerns about how LLMs interact with everyday feminist practices. Through an analysis of 430 RedNote posts, 139 shared DeepSeek responses, and 3211 comments, we found that users predominantly welcomed DeepSeek's advice. Yet feminist critical discourse analysis revealed that these responses primarily encouraged women to self-optimize and pursue achievements within prevailing norms rather than challenge them. By interpreting this case, we discuss the opportunities and risks that LLMs introduce for everyday feminism as a pathway toward women's empowerment, and offer design implications for leveraging LLMs to better support such practices.

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arXiv2026

ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactions

From deciding on a PhD program to buying a new camera, unfamiliar decisions--decisions without domain knowledge--are frequent and significant. The complexity and uncertainty of such decisions demand unique approaches to information seeking, understanding, and decision-making. Our formative study highlights that users want to start by discovering broad and relevant domain information evenly and simultaneously, quickly address emerging inquiries, and gain personalized standards to assess information found. We present ChoiceMates, an interactive multi-agent system designed to address these needs by enabling users to engage with a dynamic set of LLM agents each presenting a unique experience in the domain. Unlike existing multi-agent systems that automate tasks with agents, the user orchestrates agents to assist their decision-making process. Our user evaluation (n=12) shows that ChoiceMates enables a more confident, satisfactory decision-making with better situation understanding than web search, and higher decision quality and confidence than a commercial multi-agent framework. This work provides insights into designing a more controllable and collaborative multi-agent system.

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arXiv2026

DietGlance: Dietary Monitoring and Personalized Analysis at a Glance with Knowledge-Empowered AI Assistant

Growing awareness of wellness has prompted people to consider whether their dietary patterns align with their health and fitness goals. In response, researchers have introduced various wearable dietary monitoring systems and dietary assessment approaches. However, these solutions are either limited to identifying foods with simple ingredients or insufficient in providing an analysis of individual dietary behaviors with domain-specific knowledge. In this paper, we present DietGlance, a system that automatically monitors dietary behaviors in daily routines and delivers personalized analysis from knowledge sources. DietGlance first detects ingestive episodes from multimodal inputs using eyeglasses, capturing privacy-preserving meal images of various dishes being consumed. Based on the inferred food items and consumed quantities from these images, DietGlance further provides nutritional analysis and personalized dietary suggestions, empowered by the retrieval-augmented generation module on a reliable nutrition library. A short-term user study (N=33) and a four-week longitudinal study (N=16) demonstrate the usability and effectiveness of DietGlance, offering insights and implications for future AI-assisted dietary monitoring and personalized healthcare intervention systems using eyewear.

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arXiv2026

How Do Human Creators Embrace Human-AI Co-Creation? A Perspective on Human Agency of Screenwriters

Generative AI has greatly transformed creative work in various domains, such as screenwriting. To understand this transformation, prior research often focused on capturing a snapshot of human-AI co-creation practice at a specific moment, with less attention to how humans mobilize, regulate, and reflect to form the practice gradually. Motivated by Bandura's theory of human agency, we conducted a two-week study with 19 professional screenwriters to investigate how they embraced AI in their creation process. Our findings revealed that screenwriters not only mindfully planned, foresaw, and responded to AI usage, but, more importantly, through reflections on practice, they developed themselves and human-AI co-creation paradigms, such as cognition, strategies, and workflows. They also expressed various expectations for how future AI should better support their agency. Based on our findings, we conclude this paper with extensive discussion and actionable suggestions to screenwriters, tool developers, and researchers for sustainable human-AI co-creation.

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arXiv2026

DuoDrama: Supporting Screenplay Refinement Through LLM-Assisted Human Reflection

AI has been increasingly integrated into screenwriting practice. In refinement, screenwriters expect AI to provide feedback that supports reflection across the internal perspective of characters and the external perspective of the overall story. However, existing AI tools cannot sufficiently coordinate the two perspectives to meet screenwriters' needs. To address this gap, we present DuoDrama, an AI system that generates feedback to assist screenwriters' reflection in refinement. To enable DuoDrama, based on performance theories and a formative study with nine professional screenwriters, we design the Experience-Grounded Feedback Generation Workflow for Human Reflection (ExReflect). In ExReflect, an AI agent adopts an experience role to generate experience and then shifts to an evaluation role to generate feedback based on the experience. A study with fourteen professional screenwriters shows that DuoDrama improves feedback quality and alignment and enhances the effectiveness, depth, and richness of reflection. We conclude by discussing broader implications and future directions.

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arXiv2026

"Shall We Dig Deeper?": Designing and Evaluating Strategies for LLM Agents to Advance Knowledge Co-Construction in Asynchronous Online Discussions

Asynchronous online discussions enable diverse participants to co-construct knowledge beyond individual contributions. This process ideally evolves through sequential phases, from superficial information exchange to deeper synthesis. However, many discussions stagnate in the early stages. Existing AI interventions typically target isolated phases, lacking mechanisms to progressively advance knowledge co-construction, and the impacts of different intervention styles in this context remain unclear and warrant investigation. To address these gaps, we conducted a design workshop to explore AI intervention strategies (task-oriented and/or relationship-oriented) throughout the knowledge co-construction process, and implemented them in an LLM-powered agent capable of facilitating progression while consolidating foundations at each phase. A within-subject study (N=60) involving five consecutive asynchronous discussions showed that the agent consistently promoted deeper knowledge progression, with different styles exerting distinct effects on both content and experience. These findings provide actionable guidance for designing adaptive AI agents that sustain more constructive online discussions.

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arXiv2026

InkIdeator: Supporting Chinese-Style Visual Design Ideation via AI-Infused Exploration of Chinese Paintings

Visual designers often seek inspiration from Chinese paintings when tasked with creating Chinese-style illustrations, posters, etc. Our formative study (N=10) reveals that during ideation, designers learn the cultural symbols, emotions, compositions, and styles in Chinese paintings but face challenges in searching, analyzing, and integrating these dimensions. This paper leverages multi-modal large models to annotate the value of each dimension in 16,315 Chinese paintings, built on which we propose InkIdeator, an ideation support system for Chinese-style visual designs. InkIdeator suggests cultural symbols associated with the task theme, provides dimensional keywords to help analyze Chinese paintings, and generates visual examples integrating user-selected keywords. Our within-subjects study (N=12) using a baseline system without extracted dimensional keywords, along with two extended use cases by Chinese painters, indicates InkIdeator's effectiveness in creative ideation support, helping users efficiently explore cultural dimensions in Chinese paintings and visualize their ideas. We discuss implications for supporting culture-related visual design ideation with generative AI.

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arXiv2026

Legitimizing, Developing, and Sustaining Feminist HCI in East Asia: Challenges and Opportunities

Feminist HCI has been rapidly developing in East Asian contexts in recent years. The region's unique cultural and political backgrounds have contributed valuable, situated knowledge, revealing topics such as localized digital feminism practices, or women's complex navigation among social expectations. However, the very factors that ground these perspectives also create significant survival challenges for researchers in East Asia. These include a scarcity of dedicated funding, the stigma of being perceived as less valuable than productivity-oriented technologies, and the lack of senior researchers and established, resilient communities. Grounded in these challenges and our prior collective practices, we propose this meet-up with two focused goals: (1) to provide a legitimized channel for Feminist HCI researchers to connect and build community, and (2) to facilitate an action-oriented dialogue on how to legitimize, develop, and sustain Feminist HCI in the East Asian context. The website for this meet-up is: https://feminist-hci.github.io/

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