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Muzhi Zhou

Publications and source records attributed to Muzhi Zhou.

8 recordsLinked to original sources

Meflex: A Multi-agent Scaffolding System for Entrepreneurial Ideation Iteration via Nonlinear Business Plan Writing

Business plan (BP) writing plays a key role in entrepreneurship education by helping learners construct, evaluate, and iteratively refine their ideas. However, conventional BP writing remains a rigid, linear process that often fails to reflect the dynamic and recursive nature of entrepreneurial ideation. This mismatch is particularly challenging for novice entrepreneurial students, who struggle with the substantial cognitive demands of developing and refining ideas. While reflection and meta-reflection are critical strategies for fostering divergent and convergent thinking, existing writing tools rarely scaffold these higher-order processes. To address this gap, we present the Meflex System, a large language model (LLM)-based writing tool that integrates BP writing scaffolding with a nonlinear idea canvas to support iterative ideation through reflection and meta-reflection. We report findings from an exploratory user study with 30 participants that examined the system's usability and cognitive impact. Results show that Meflex effectively scaffolds BP writing, promotes divergent thinking through LLM-supported reflection, and enhances meta-reflective awareness while reducing cognitive load during complex idea development. These findings highlight the potential of non-linear LLM-based writing tools to foster deeper and coherent entrepreneurial thinking.

cs.HC

ParaTutor: Coordinating Parent and Child Math Tutoring through Role Separated LLM Scaffolding

Parent and child tutoring is a collaborative learning setting with asymmetric roles. Parents guide children s problem solving, while children are expected to remain actively engaged in understanding and reasoning. However, most LLM based learning systems are designed for single users or relatively symmetric collaboration, leaving parent and child tutoring with distinct instructional roles underexplored. Through a formative study, we found that parent and child math tutoring was often disrupted by cognitive misalignment, emotional escalation, and method mismatch. To address these challenges, we present ParaTutor, a multiple agents LLM based scaffolding system for home math word problem tutoring. ParaTutor distributes support across user roles by providing parents with strategy, language, repair, and phase scaffolds, while providing children with visual grounding for problem interpretation. We evaluated ParaTutor with 23 parent and child dyads (children aged 10 to 12) across four tutoring conditions that varied how LLM assistance was delivered. Results show that generic LLM assistance often provided useful explanations but did not consistently support parent led tutoring or children s active reasoning. In contrast, ParaTutor helped redistribute tutoring work across parents and children, increased children s engagement with word problems, supported shared understanding through visual grounding, and helped parents translate LLM generated methods into child facing tutoring moves. These findings suggest that in family learning, the value of LLM support depends not only on model capability, but also on how support is coordinated across users with different roles. Our work contributes design implications for LLM systems that support role sensitive scaffolding in parent and child learning.

cs.HC

Dream the Dream: Futuring Communication between LGBTQ+ and Cisgender Groups in Metaverse

Digital platforms frequently reproduce heteronormative norms and structural biases, limiting inclusive communication between LGBTQ+ and cisgender individuals. The Metaverse, with its affordances for identity fluidity, presence, and community governance, offers a promising site for reimagining such interactions. To investigate this potential, we conducted participatory design workshops involving LGBTQ+ and cisgender participants, situating them in speculative Metaverse contexts to surface barriers and co-create alternative futures. The workshops followed a three-phase process-identifying challenges, speculative problem-solving, and visualizing futures-yielding socio-spatial-technical solutions across four layers: activity, interaction, scene, and space. These findings highlight the importance of spatial cues and power dynamics in shaping digital encounters. We contribute by (1) articulating challenges of cross-group communication in virtual environments, (2) proposing inclusive design opportunities for the Metaverse, and (3) advancing principles for addressing power geometry in digital space. This work demonstrates futuring as a critical strategy for designing equitable, transformative communication infrastructures.

cs.HC

Unpacking Discourses on Childbirth and Parenthood in Popular Social Media Platforms Across China, Japan, and South Korea

Social media use has been shown to be associated with low fertility desires. However, we know little about the discourses surrounding childbirth and parenthood that people consume online. We analyze 219,127 comments on 668 short videos related to reproduction and parenthood from Douyin and Tiktok in China, South Korea, and Japan, a region famous for its extremely low fertility level, to examine the topics and sentiment expressed online. BERTopic model is used to assist thematic analysis, and a large language model QWen is applied to label sentiment. We find that comments focus on childrearing costs in all countries, utility of children, particularly in Japan and South Korea, and individualism, primarily in China. Comments from Douyin exhibit the strongest anti-natalist sentiments, while the Japanese and Korean comments are more neutral. Short video characteristics, such as their stances or account type, significantly influence the responses, alongside regional socioeconomic indicators, including GDP, urbanization, and population sex ratio. This work provides one of the first comprehensive analyses of online discourses on family formation via popular algorithm-fed video sharing platforms in regions experiencing low fertility rates, making a valuable contribution to our understanding of the spread of family values online.

cs.SI

EchoAid: Enhancing Livestream Shopping Accessibility for the DHH Community

Livestream shopping platforms often overlook the accessibility needs of the Deaf and Hard of Hearing (DHH) community, leading to barriers such as information inaccessibility and overload. To tackle these challenges, we developed \textit{EchoAid}, a mobile app designed to improve the livestream shopping experience for DHH users. \textit{EchoAid} utilizes advanced speech-to-text conversion, Rapid Serial Visual Presentation (RSVP) technology, and Large Language Models (LLMs) to simplify the complex information flow in live sales environments. We conducted exploratory studies with eight DHH individuals to identify design needs and iteratively developed the \textit{EchoAid} prototype based on feedback from three participants. We then evaluate the performance of this system in a user study workshop involving 38 DHH participants. Our findings demonstrate the successful design and validation process of \textit{EchoAid}, highlighting its potential to enhance product information extraction, leading to reduced cognitive overload and more engaging and customized shopping experiences for DHH users.

cs.HC

ChatGPT vs Social Surveys: Probing Objective and Subjective Silicon Population

Recent discussions about Large Language Models (LLMs) indicate that they have the potential to simulate human responses in social surveys and generate reliable predictions, such as those found in political polls. However, the existing findings are highly inconsistent, leaving us uncertain about the population characteristics of data generated by LLMs. In this paper, we employ repeated random sampling to create sampling distributions that identify the population parameters of silicon samples generated by GPT. Our findings show that GPT's demographic distribution aligns with the 2020 U.S. population in terms of gender and average age. However, GPT significantly overestimates the representation of the Black population and individuals with higher levels of education, even when it possesses accurate knowledge. Furthermore, GPT's point estimates for attitudinal scores are highly inconsistent and show no clear inclination toward any particular ideology. The sample response distributions exhibit a normal pattern that diverges significantly from those of human respondents. Consistent with previous studies, we find that GPT's answers are more deterministic than those of humans. We conclude by discussing the concerning implications of this biased and deterministic silicon population for making inferences about real-world populations.

cs.CY

Practices and Challenges of Online Love-seeking Among Deaf or Hard of Hearing People: A Case Study in China

People who are deaf or hard of hearing (DHH) in China are increasingly exploring online platforms to connect with potential partners. This research explores the online dating experiences of DHH communities in China, an area that has not been extensively researched. We interviewed sixteen participants who have varying levels of hearing ability and love-seeking statuses to understand how they manage their identities and communicate with potential partners online. We find that DHH individuals made great efforts to navigate the rich modality features to seek love online. Participants used both algorithm-based dating apps and community-based platforms like forums and WeChat to facilitate initial encounters through text-based functions that minimized the need for auditory interaction, thus fostering a more equitable starting point. Community-based platforms were found to facilitate more in-depth communication and excelled in fostering trust and authenticity, providing a more secure environment for genuine relationships. Design recommendations are proposed to enhance the accessibility and inclusiveness of online dating platforms for DHH individuals in China. This research sheds light on the benefits and challenges of online dating for DHH individuals in China and provides guidance for platform developers and researchers to enhance user experience in this area.

cs.HC

Sparkling Silence: Practices and Challenges of Livestreaming Among Deaf or Hard of Hearing Streamers

Understanding livestream platforms' accessibility challenges for minority groups, such as people with disabilities, is critical to increasing the diversity and inclusion of those platforms. While prior work investigated the experiences of streamers with vision or motor loss, little is known about the experiences of deaf or hard of hearing (DHH) streamers who must work with livestreaming platforms that heavily depend on audio. We conducted semi-structured interviews with DHH streamers to learn why they livestream, how they navigate livestream platforms and related challenges. Our findings revealed their desire to break the stereotypes towards the DHH groups via livestream and the intense interplay between interaction methods, such as sign language, texts, lip language, background music, and viewer characteristics. Major accessibility challenges include the lack of real-time captioning, the small sign language reading window, and misinterpretation of sign language. We present design considerations for improving the accessibility of the livestream platforms.

cs.HC