About

Yun Huang is an Associate Professor in the Information Sciences department at the University of Illinois. His research areas include Interactive Computing, Human-Computer Interaction, and Social Computing. He has taught courses such as Introduction to Human-Computer Interaction, Social Computing, and Generative AI for Human-AI Collaboration. Huang is involved in projects aimed at improving conference experiences through app development and is part of an OpenAI-funded project focused on representing underserved groups in AI development. In 2025–2026, he was named a Linowes Fellow by the Cline Center for Advanced Social Research at the University of Illinois.

Research topics

• Materials science
• Nanotechnology
• Computer science
• Composite material
• Biomedical engineering

Selected publications

• AReframedChair: Reframing the Empty Chair through Dyadic and Triadic AR-Mediated Self-Embodiment

  2026-04-13 · 1 citations

  articleOpen access

  Immersive technologies are increasingly applied in therapeutic and well-being practices, yet most AR systems focus on dyadic client–avatar interactions and overlook richer therapeutic structures that involve therapists. We introduce AReframedChair, an AR system that reimagines the traditional Empty Chair technique by enabling self-dialogue with a personalized avatar representing one’s past or future self. In a between-subjects study with 60 adults, we compared the traditional Empty Chair method with two AR-reframed modes: Dyadic (client–avatar) and Triadic (client–avatar– therapist). Participants’ survey responses showed that the Dyadic mode elicited greater positive affect and self-compassion in the past-self scenarios, whereas the Triadic mode produced stronger gains in motivation and reflections in future-self scenarios. Thematic analysis further revealed distinct roles: the Avatar facilitated emotional entry, reassurance, and cognitive reframing, while the Therapists intervened at critical moments to down-regulate intensity, redirect attention, and enhance reflection. These findings open up new design pathways for mental health technologies.

  PublisherDOI

• Follow the Signs or the Crowd? Effects of Environmental Load and Crowd Dynamics in VR Evacuation

  IEEE Transactions on Visualization and Computer Graphics · 2026-03-31

  article

  Emergency evacuation in VR must balance realism with clear guidance. However, most prior studies strengthen either sensory or social factors in isolation, leaving equal-geometry causal estimates of load versus crowd still lacking. We present SAFE-VR, a controlled testbed that orthogonally varies Environmental Load (low vs. high) and Crowd Dynamics (orderly vs. chaotic) while keeping layout, signage, and spawn constant. In a preregistered 2 x 2 between-subjects experiment (N=80), we analyzed time-to-exit, frame-coded behavior, presence, and workload to distangle sensory from social effects. Both factors impaired egress, with the High×Chaotic condition performing worst overall. For time-to-exit, effects were additive (no reliable Load×Crowd interaction); in contrast, Temporal demand showed a crossed interaction. High load increased effort, frustration, and object contacts; while chaotic flow increased route deviations, human contacts, and slowed exits. These patterns align with reliability-weighted cueing: as guidance becomes harder to perceive, participants may shift toward crowd-following, especially when flow is unstable. SAFE-VR thus delineates how load and crowd structure jointly shape route fidelity, collisions, and evacuation time, and highlights conditions where subjective time pressure diverges from objective delay.

  PublisherDOI

• Empowered XR through Generative AI: Balancing Superpowers and Risks

  2026-04-13

  articleOpen accessSenior author

  The integration of generative AI with Extended Reality (XR) technologies has unlocked unprecedented capabilities, empowering users with enhanced cognitive, sensory, and environmental control – effectively enabling "superpowers" in immersive digital spaces. This paper explores both the benefits and potential risks. We make two contributions: (i) a synthesized taxonomy of LLM-enabled XR superpowers and their associated risks, and (ii) a set of design guidelines and a forward research agenda derived from that synthesis. We conduct a multi-phase analysis of 135 recent advancements and studies in the field to examine the superpowers granted by these technologies, alongside their associated risks. We categorize the superpowers into internal (cognitive and sensory enhancements) and external (environmental and social manipulations), illustrating how they amplify human abilities in domains such as healthcare, education, and professional training. We then analyze the risks specific to each superpower, revealing critical vulnerabilities in user autonomy, data security, and ethical transparency. This research aims to guide stakeholders in harnessing the potential of XR while mitigating the socio-technical risks of this emerging landscape.

  PublisherDOI

• EvAlignUX: Advancing UX Evaluation through LLM-Supported Metrics Exploration

  2025-04-24 · 8 citations

  articleOpen accessSenior author
  PublisherDOI

• Aligning AI with Public Values: Deliberation and Decision-Making for Governing Multimodal LLMs in Political Video Analysis

  Proceedings of the AAAI/ACM Conference on AI Ethics and Society · 2025-10-15

  articleOpen access

  How AI models should deal with political topics has been discussed, but it remains challenging and requires better governance. This paper examines the governance of large language models through individual and collective deliberation, focusing on politically sensitive videos. We conducted a two-step study: interviews with 10 journalists established a baseline understanding of expert video interpretation; 114 individuals through deliberation using Inclusive.AI, a platform that facilitates democratic decision-making through decentralized autonomous organization (DAO) mechanisms. Our findings reveal distinct differences in interpretative priorities: while experts emphasized emotion and narrative, general public prioritized factual clarity, objectivity, and emotional neutrality. Furthermore, we examined how different governance mechanisms - quadratic vs. weighted voting and equal vs. 20/80 voting power - shape users' decision-making regarding AI behavior. Results indicate that voting methods significantly influence outcomes, with quadratic voting reinforcing perceptions of liberal democracy and political equality. Our study underscores the necessity of selecting appropriate governance mechanisms to better capture user perspectives and suggests decentralized AI governance as a potential way to facilitate broader public engagement in AI development, ensuring that varied perspectives meaningfully inform design decisions.

  PublisherOA PDFDOI

• Lessons from Real-World Settings: What Makes It Uniquely Difficult to Design Cognitive Training Programs for Children with Autism Spectrum Disorder and Other Developmental Disabilities

  2025-04-25 · 1 citations

  articleOpen accessSenior author

  Despite the prevalence of autism spectrum disorder (ASD) and other developmental disabilities (DD) worldwide, children with ASD and DD face tremendous difficulties receiving support due to physical, financial, and psychological barriers to onsite health and education clinics. As a result, researchers and practitioners have designed software solutions aimed at providing accessible support to meet users’ needs. However, we have limited knowledge of whether these solutions indeed work in real-world settings. To address this gap, we conducted a case study on a cognitive training program called Dubupang, designed by Dubu Inc. From in-depth interviews with multiple stakeholders and field observations of children with ASD and DD, we identify Dubu Inc.’s internal development processes, the critical design issues that emerged through a series of field trials (e.g., instructional design and feedback), and the key implications (e.g., importance of caregivers’ strategic human interventions) for design that better supports both children with ASD and DD and their caregivers.

  PublisherOA PDFDOI

• Understanding Generative AI Risks for Youth: A Taxonomy Based on Empirical Data

  ArXiv.org · 2025-02-22 · 1 citations

  preprintOpen access

  Generative AI (GAI) is reshaping the way young users engage with technology. This study introduces a taxonomy of risks associated with youth-GAI interactions, derived from an analysis of 344 chat transcripts between youth and GAI chatbots, 30,305 Reddit discussions concerning youth engagement with these systems, and 153 documented AI-related incidents. We categorize risks into six overarching themes, identifying 84 specific risks, which we further align with four distinct interaction pathways. Our findings highlight emerging concerns, such as risks to mental wellbeing, behavioral and social development, and novel forms of toxicity, privacy breaches, and misuse/exploitation that are not fully addressed in existing frameworks on child online safety or AI risks. By systematically grounding our taxonomy in empirical data, this work offers a structured approach to aiding AI developers, educators, caregivers, and policymakers in comprehending and mitigating risks associated with youth-GAI interactions.

  PublisherOA PDFDOI

• Improving Emotional Support Delivery in Text-Based Community Safety Reporting Using Large Language Models

  Proceedings of the ACM on Human-Computer Interaction · 2025-05-02 · 4 citations

  articleSenior author

  Emotional support is a crucial aspect of communication between community members and police dispatchers during incident reporting. However, there is a lack of understanding about how emotional support is delivered through text-based systems, especially in various non-emergency contexts. In this study, we analyzed two years of chat logs comprising 57,114 messages across 8,239 incidents from 130 higher education institutions. Our empirical findings revealed significant variations in emotional support provided by dispatchers, influenced by the type of incident, service time, and a noticeable decline in support over time across multiple organizations. To improve the consistency and quality of emotional support, we developed and implemented a fine-tuned Large Language Model (LLM), named dispatcherLLM, designed to suggest replies through simulating human dispatchers' languages with appropriate emotional support. We evaluated dispatcherLLM by comparing its generated responses to those of human dispatchers and other off-the-shelf models using real chat messages. Additionally, we conducted a human evaluation to assess the perceived effectiveness of the support provided by dispatcherLLM. This study not only contributes new empirical understandings of emotional support in text-based dispatch systems but also demonstrates the significant potential of generative AI in improving service delivery.

  PublisherDOI

• YouthSafe: A Youth-Centric Safety Benchmark and Safeguard Model for Large Language Models

  2025-11-19 · 2 citations

  articleOpen access

  Large Language Models (LLMs) are increasingly used by teenagers and young adults in everyday life, ranging from emotional support and creative expression to educational assistance. However, their unique vulnerabilities and risk profiles remain under-examined in current safety benchmarks and moderation systems, leaving this population disproportionately exposed to harm. In this work, we present Youth AI Risk (YAIR), the first benchmark dataset designed to evaluate and improve the safety of youth–LLM interactions. YAIR consists of 12,449 annotated conversation snippets spanning 78 fine-grained risk types, grounded in a taxonomy of youth-specific harms such as grooming, boundary violation, identity confusion, and emotional overreliance. We systematically evaluate widely adopted moderation models on YAIR and find that existing approaches substantially underperform in detecting youth-centered risks, often missing contextually subtle yet developmentally harmful interactions. To address these gaps, we introduce YouthSafe, a real-time risk detection model optimized for youth–GenAI contexts. YouthSafe significantly outperforms prior systems across multiple metrics on risk detection and classification, offering a concrete step toward safer and more developmentally appropriate AI interactions for young users.

  PublisherDOI

• “It is more pernicious than opium”: morphine consumption and control in late Qing China, c.1871-1909

  Modern Chinese History · 2025-05-09

  article1st authorCorresponding
  PublisherDOI

Frequent coauthors

• John A. Rogers

  436 shared

• John A. Rogers

  Northwestern University

  343 shared

• Yihui Zhang

  Xi'an Jiaotong University

  150 shared

• Jizhou Song

  Zhejiang University

  148 shared

• Ao Wang

  Central South University

  126 shared

• Ha Uk Chung

  Sibel (United States)

  90 shared

• Xue Feng

  90 shared

• Zhaoqian Xie

  89 shared

Labs

• Siebel School of Computing and Data SciencePI

Education

• Ph.D., Computer Science

  University of Illinois at Urbana-Champaign

  2006

• M.S., Computer Science

  University of Illinois at Urbana-Champaign

  2002

• B.S., Computer Science

  University of Science and Technology of China

  1999

Awards & honors

• 2025–2026 Linowes Fellow by the Cline Center for Advanced So…