arXiv subjects

Qingxiao Zheng

Publications and source records attributed to Qingxiao Zheng.

9 recordsLinked to original sources

arXiv2026

A Meat-Summer Night's Dream: A Tangible Design Fiction Exploration of Eating Biohybrid Flying Robots

What if future dining involved eating robots? We explore this question through a playful and poetic experiential dinner theater: a tangible design fiction staged as a 2052 Paris restaurant where diners consume a biohybrid flying robot in place of the banned delicacy of ortolan bunting. Moving beyond textual or visual speculation, our ``dinner-in-the-drama'' combined performance, ritual, and multisensory immersion to provoke reflection on sustainability, ethics, and cultural identity. Six participants from creative industries engaged as diners and role-players, responding with curiosity, discomfort, and philosophical debate. They imagined biohybrids as both plausible and unsettling -- raising questions of sentience, symbolism, and technology adoption that extend beyond conventional sustainability framings of synthetic meat. Our contributions to HCI are threefold: (i) a speculative artifact that stages robots as food, (ii) empirical insights into how people negotiate cultural and ethical boundaries in post-natural eating, and (iii) a methodological advance in embodied, multisensory design fiction.

cs.HC↗

arXiv2025

MORA: AI-Mediated Story-Based practice for Speech Sound Disorder from Clinic to Home

Speech sound disorder is among the most common communication challenges in preschool children. Home-based practice is essential for effective therapy and for acquiring generalization of target sounds, yet sustaining engaging and consistent practice remains difficult. Existing story-based activities, despite their potential for sound generalization and educational benefits, are often underutilized due to limited interactivity. Moreover, many practice tools fail to sufficiently integrate speech-language pathologists into the process, resulting in weak alignment with clinical treatment plans. To address these limitations, we present MORA, an interactive story-based practice system. MORA introduces three key innovations. First, it embeds target sounds and vocabulary into dynamic, character-driven conversational narratives, requiring children to actively produce speech to progress the story, thereby creating natural opportunities for exposure, repetition, and generalization. Second, it provides visual cues, explicit instruction, and feedback, allowing children to practice effectively either independently or with caregivers. Third, it supports an AI-in-the-loop workflow, enabling SLPs to configure target materials, review logged speech with phoneme-level scoring, and adapt therapy plans asynchronously -- bridging the gap between clinic and home practice while respecting professional expertise. A formative study with six licensed SLPs informed the system's design rationale, and an expert review with seven SLPs demonstrated strong alignment with established articulation-based treatments, as well as potential to enhance children's engagement and literacy. Furthermore, discussions highlight the design considerations for professional support and configurability, adaptive and multimodal child interaction, while highlighting MORA's broader applicability across speech and language disorders.

cs.HC↗

arXiv2025

Sub-Sequential Physics-Informed Learning with State Space Model

Physics-Informed Neural Networks (PINNs) are a kind of deep-learning-based numerical solvers for partial differential equations (PDEs). Existing PINNs often suffer from failure modes of being unable to propagate patterns of initial conditions. We discover that these failure modes are caused by the simplicity bias of neural networks and the mismatch between PDE's continuity and PINN's discrete sampling. We reveal that the State Space Model (SSM) can be a continuous-discrete articulation allowing initial condition propagation, and that simplicity bias can be eliminated by aligning a sequence of moderate granularity. Accordingly, we propose PINNMamba, a novel framework that introduces sub-sequence modeling with SSM. Experimental results show that PINNMamba can reduce errors by up to 86.3\% compared with state-of-the-art architecture. Our code is available at https://github.com/miniHuiHui/PINNMamba.

cs.LG↗

arXiv2025

EvAlignUX: Advancing UX Evaluation through LLM-Supported Metrics Exploration

Evaluating UX in the context of AI's complexity, unpredictability, and generative nature presents unique challenges. How can we support HCI researchers to create comprehensive UX evaluation plans? In this paper, we introduce EvAlignUX, a system powered by large language models and grounded in scientific literature, designed to help HCI researchers explore evaluation metrics and their relationship to research outcomes. A user study with 19 HCI scholars showed that EvAlignUX improved the perceived quality and confidence in UX evaluation plans while prompting deeper consideration of research impact and risks. The system enhanced participants' thought processes, leading to the creation of a ``UX Question Bank'' to guide UX evaluation development. Findings also highlight how researchers' backgrounds influence their inspiration and concerns about AI over-reliance, pointing to future research on AI's role in fostering critical thinking. In a world where experience defines impact, we discuss the importance of shifting UX evaluation from a ``method-centric'' to a ``mindset-centric'' approach as the key to meaningful and lasting design evaluation.

cs.HC↗

arXiv2025

Automating Intervention Discovery from Scientific Literature: A Progressive Ontology Prompting and Dual-LLM Framework

Identifying effective interventions from the scientific literature is challenging due to the high volume of publications, specialized terminology, and inconsistent reporting formats, making manual curation laborious and prone to oversight. To address this challenge, this paper proposes a novel framework leveraging large language models (LLMs), which integrates a progressive ontology prompting (POP) algorithm with a dual-agent system, named LLM-Duo. On the one hand, the POP algorithm conducts a prioritized breadth-first search (BFS) across a predefined ontology, generating structured prompt templates and action sequences to guide the automatic annotation process. On the other hand, the LLM-Duo system features two specialized LLM agents, an explorer and an evaluator, working collaboratively and adversarially to continuously refine annotation quality. We showcase the real-world applicability of our framework through a case study focused on speech-language intervention discovery. Experimental results show that our approach surpasses advanced baselines, achieving more accurate and comprehensive annotations through a fully automated process. Our approach successfully identified 2,421 interventions from a corpus of 64,177 research articles in the speech-language pathology domain, culminating in the creation of a publicly accessible intervention knowledge base with great potential to benefit the speech-language pathology community.

cs.CL↗

arXiv2025

Learning Through AI-Clones: Enhancing Self-Perception and Presentation Performance

This study examines the impact of AI-generated digital clones with self-images on enhancing perceptions and skills in online presentations. A mixed-design experiment with 44 international students compared self-recording videos (self-recording group) to AI-clone videos (AI-clone group) for online English presentation practice. AI-clone videos were generated using voice cloning, face swapping, lip-syncing, and body-language simulation, refining the repetition, filler words, and pronunciation of participants' original presentations. Through the lens of social comparison theory, the results showed that AI clones functioned as positive "role models" for facilitating social comparisons. When comparing the effects on self-perceptions, speech qualities, and self-kindness, the self-recording group showed an increase in pronunciation satisfaction. However, the AI-clone group exhibited greater self-kindness, broader observational coverage, and a meaningful transition from a corrective to an enhancive approach in self-critique. Moreover, machine-rated scores revealed immediate performance gains only within the AI-clone group. Considering individual differences, aligning interventions with participants' regulatory focus significantly enhanced their learning experience. These findings highlight the theoretical, practical, and ethical implications of AI clones in supporting emotional and cognitive skill development.

cs.HC↗

arXiv2023

Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agents

This empirical study serves as a primer for interested service providers to determine if and how Large Language Models (LLMs) technology will be integrated for their practitioners and the broader community. We investigate the mutual learning journey of non-AI experts and AI through CoAGent, a service co-creation tool with LLM-based agents. Engaging in a three-stage participatory design processes, we work with with 23 domain experts from public libraries across the U.S., uncovering their fundamental challenges of integrating AI into human workflows. Our findings provide 23 actionable "heuristics for service co-creation with AI", highlighting the nuanced shared responsibilities between humans and AI. We further exemplar 9 foundational agency aspects for AI, emphasizing essentials like ownership, fair treatment, and freedom of expression. Our innovative approach enriches the participatory design model by incorporating AI as crucial stakeholders and utilizing AI-AI interaction to identify blind spots. Collectively, these insights pave the way for synergistic and ethical human-AI co-creation in service contexts, preparing for workforce ecosystems where AI coexists.

cs.HC↗

arXiv2022

Facing the Illusion and Reality of Safety in Social VR

The ethical design of social Virtual Reality (VR) is not a new topic, but "safety" concerns of using social VR are escalated to a different level given the heat of the Metaverse. For example, it was reported that nearly half of the female-identifying VR participants have had at least one instance of virtual sexual harassment. Feeling safe is a basic human right - in any place, regardless in real or virtual spaces. In this paper, we are seeking to understand the discrepancy between user concerns and designs in protecting user safety in social VR applications. We study safety concerns on social VR experience first by analyzing Twitter posts and then synthesize practices on safety protection adopted by four mainstream social VR platforms. We argue that future research and platforms should explore the design of social VR with boundary-awareness.

cs.HC↗

arXiv2022

UX Research on Conversational Human-AI Interaction: A Literature Review of the ACM Digital Library

Early conversational agents (CAs) focused on dyadic human-AI interaction between humans and the CAs, followed by the increasing popularity of polyadic human-AI interaction, in which CAs are designed to mediate human-human interactions. CAs for polyadic interactions are unique because they encompass hybrid social interactions, i.e., human-CA, human-to-human, and human-to-group behaviors. However, research on polyadic CAs is scattered across different fields, making it challenging to identify, compare, and accumulate existing knowledge. To promote the future design of CA systems, we conducted a literature review of ACM publications and identified a set of works that conducted UX (user experience) research. We qualitatively synthesized the effects of polyadic CAs into four aspects of human-human interactions, i.e., communication, engagement, connection, and relationship maintenance. Through a mixed-method analysis of the selected polyadic and dyadic CA studies, we developed a suite of evaluation measurements on the effects. Our findings show that designing with social boundaries, such as privacy, disclosure, and identification, is crucial for ethical polyadic CAs. Future research should also advance usability testing methods and trust-building guidelines for conversational AI.

cs.HC↗