arXiv subjects

Zhiqing Wang

Publications and source records attributed to Zhiqing Wang.

6 recordsLinked to original sources

arXiv2026

Disentangling Threads: Exploring the Potential of LLM-Supported Discussion Forum Analysis for Community Insight

Online discussion forums enable people from diverse backgrounds to share ideas, feedback, and perspectives. These organic discussions can help researchers understand communities' collective viewpoints, but insights are often difficult to uncover given their freeform reply structure. Large language models (LLMs) support qualitative text analysis but can misalign with researchers' analytical intent and miss key insights. To inform design considerations for forum sensemaking tools, we manually analyzed a forum discussion, synthesized an exploratory analysis framework from relevant literature, built a design probe, and interviewed 21 researchers to uncover perceived opportunities and barriers with LLM representations of collective discussions. We provide recommendations for community sensemaking tools to support flexible analytical goals grounded in raw user data and enable follow-up research processes, while balancing anonymous free expression with the desire for contextual information on commenters.

cs.HC↗

arXiv2026

Designing AI-Supported Focus Groups: A Role x Modality Playbook

Collecting participants' lived experiences is central to design research. Focus groups are uniquely valuable because participants not only share individual accounts but also respond to one another, surfacing comparison, disagreement, and collective sensemaking. However, focus groups are resource-intensive and highly sensitive to facilitation: moderators must probe for specificity, balance participation, manage topic flow, and sustain psychological safety, and subtle facilitation choices can shape what becomes salient. Recent HCI work and commercial meeting tools show that generative AI can scaffold live conversation through prompting, turn regulation, thematic mapping, and real-time summarization. Yet UXR teams lack a clear map of what these capabilities mean in focus groups and what methodological risks they introduce. We synthesize AI supports for live conversation and translate them into a focus-group-specific playbook organized by AI role (tool, co-host, host) and modality (text, voice, embodied).We synthesize prior work on AI-supported live conversation and propose a focus-group-specific playbook of AI supports organized by role (tool, co-host, host) and modality (text, voice, embodied). We characterize interactional trade-offs and identify open questions for evaluating AI-supported focus groups as methodological configurations.

cs.HC↗

arXiv2025

Exploring Community-Powered Conversational Agent for Health Knowledge Acquisition: A Case Study in Colorectal Cancer

Online communities have become key platforms where young adults, actively seek and share information, including health knowledge. However, these users often face challenges when browsing these communities, such as fragmented content, varying information quality and unfamiliar terminology. Based on a survey with 56 participants and follow-up interviews, we identify common challenges and expected features for learning health knowledge. In this paper, we develop a computational workflow that integrates community content into a conversational agent named CanAnswer to facilitate health knowledge acquisition. Using colorectal cancer as a case study, we evaluate CanAnswer through a lab study with 24 participants and interviews with six medical experts. Results show that CanAnswer improves the recalled gained knowledge and reduces the task workload of the learning session. Our expert interviews (N=6) further confirm the reliability and usefulness of CanAnswer. We discuss the generality of CanAnswer and provide design considerations for enhancing the usefulness and credibility of community-powered learning tools.

cs.HC↗

arXiv2025

Exploring the Usage of Generative AI for Group Project-Based Offline Art Courses in Elementary Schools

The integration of Generative Artificial Intelligence (GenAI) in K-6 project-based art courses presents both opportunities and challenges for enhancing creativity, engagement, and group collaboration. This study introduces a four-phase field study, involving in total two experienced K-6 art teachers and 132 students in eight offline course sessions, to investigate the usage and impact of GenAI. Specifically, based on findings in Phases 1 and 2, we developed AskArt, an interactive interface that combines DALL-E and GPT and is tailored to support elementary school students in their art projects, and deployed it in Phases 3 and 4. Our findings revealed the benefits of GenAI in providing background information, inspirations, and personalized guidance. However, challenges in query formulation for generating expected content were also observed. Moreover, students employed varied collaboration strategies, and teachers noted increased engagement alongside concerns regarding misuse and interface suitability. This study offers insights into the effective integration of GenAI in elementary education, presents AskArt as a practical tool, and provides recommendations for educators and researchers to enhance project-based learning with GenAI technologies.

cs.HC↗

arXiv2025

DesignWeaver: Dimensional Scaffolding for Text-to-Image Product Design

Generative AI has enabled novice designers to quickly create professional-looking visual representations for product concepts. However, novices have limited domain knowledge that could constrain their ability to write prompts that effectively explore a product design space. To understand how experts explore and communicate about design spaces, we conducted a formative study with 12 experienced product designers and found that experts -- and their less-versed clients -- often use visual references to guide co-design discussions rather than written descriptions. These insights inspired DesignWeaver, an interface that helps novices generate prompts for a text-to-image model by surfacing key product design dimensions from generated images into a palette for quick selection. In a study with 52 novices, DesignWeaver enabled participants to craft longer prompts with more domain-specific vocabularies, resulting in more diverse, innovative product designs. However, the nuanced prompts heightened participants' expectations beyond what current text-to-image models could deliver. We discuss implications for AI-based product design support tools.

cs.HC↗

arXiv2024

Dual Mean-Teacher: An Unbiased Semi-Supervised Framework for Audio-Visual Source Localization

Audio-Visual Source Localization (AVSL) aims to locate sounding objects within video frames given the paired audio clips. Existing methods predominantly rely on self-supervised contrastive learning of audio-visual correspondence. Without any bounding-box annotations, they struggle to achieve precise localization, especially for small objects, and suffer from blurry boundaries and false positives. Moreover, the naive semi-supervised method is poor in fully leveraging the information of abundant unlabeled data. In this paper, we propose a novel semi-supervised learning framework for AVSL, namely Dual Mean-Teacher (DMT), comprising two teacher-student structures to circumvent the confirmation bias issue. Specifically, two teachers, pre-trained on limited labeled data, are employed to filter out noisy samples via the consensus between their predictions, and then generate high-quality pseudo-labels by intersecting their confidence maps. The sufficient utilization of both labeled and unlabeled data and the proposed unbiased framework enable DMT to outperform current state-of-the-art methods by a large margin, with CIoU of 90.4% and 48.8% on Flickr-SoundNet and VGG-Sound Source, obtaining 8.9%, 9.6% and 4.6%, 6.4% improvements over self- and semi-supervised methods respectively, given only 3% positional-annotations. We also extend our framework to some existing AVSL methods and consistently boost their performance.

cs.CV↗