About

Gary Hsieh is a professor in the Department of Human Centered Design & Engineering at the University of Washington. His specialization includes Human-Computer Interaction, Social computing, Social media, Tailoring motivators, and Persuasive technology. His research focuses on understanding and designing social computing systems, with an emphasis on how social media and digital environments influence human behavior and motivation. As a faculty member, he contributes to advancing knowledge in social computing and human-centered technology design, integrating social and technical perspectives to develop innovative solutions that address real-world challenges.

Research topics

• Computer Science
• Sociology
• Political Science
• World Wide Web
• Psychology
• Social Science
• Artificial Intelligence
• Social psychology
• Process management
• Medicine
• Law
• Pedagogy
• Knowledge management
• Human–computer interaction
• Public relations
• Art
• Business
• Psychotherapist
• Engineering
• Economics
• Nursing
• Psychiatry

Selected publications

• ReFinE: Streamlining UI Mockup Iteration with Research Findings

  arXiv (Cornell University) · 2026-04-06

  preprintOpen accessSenior author

  Although HCI research papers offer valuable design insights, designers often struggle to apply them in design workflows due to difficulties in finding relevant literature, understanding technical jargon, the lack of contextualization, and limited actionability. To address these challenges, we present ReFinE, a Figma plugin that supports real-time design iteration by surfacing contextualized insights from research papers. ReFinE identifies and synthesizes design implications from HCI literature relevant to the mockup's design context, and tailors this research evidence to a specific design mockup by providing actionable visual guidance on how to update the mockup. To assess the system's effectiveness, we conducted a technical evaluation and a user study. Results show that ReFinE effectively synthesizes and contextualizes design implications, reducing cognitive load and improving designers' ability to integrate research evidence into UI mockups. This work contributes to bridging the gap between research and design practice by presenting a tool for embedding scholarly insights into the UI design process.

  PublisherDOI

• ReFinE: Streamlining UI Mockup Iteration with Research Findings

  ArXiv.org · 2026-04-06

  articleOpen accessSenior author

  Although HCI research papers offer valuable design insights, designers often struggle to apply them in design workflows due to difficulties in finding relevant literature, understanding technical jargon, the lack of contextualization, and limited actionability. To address these challenges, we present ReFinE, a Figma plugin that supports real-time design iteration by surfacing contextualized insights from research papers. ReFinE identifies and synthesizes design implications from HCI literature relevant to the mockup's design context, and tailors this research evidence to a specific design mockup by providing actionable visual guidance on how to update the mockup. To assess the system's effectiveness, we conducted a technical evaluation and a user study. Results show that ReFinE effectively synthesizes and contextualizes design implications, reducing cognitive load and improving designers' ability to integrate research evidence into UI mockups. This work contributes to bridging the gap between research and design practice by presenting a tool for embedding scholarly insights into the UI design process.

  PublisherOA PDFDOI

• Behind The Paper: A Workflow for Supporting Personal Research Reflection

  2026-04-13

  articleOpen access

  While researchers publish papers as their primary form of scientific output, the personal journey behind the research process is rarely communicated alongside these polished findings. We posit that these behind-the-scenes reflections on the research process itself might hold significant value, but how are they currently written and shared, if at all? To understand the current practice of research reflection—writing and sharing articles describing the personal journey of the research process—we interviewed n = 11 authors and collected a corpus of n = 15, 749 existing research reflections. We analyzed this corpus, derived design goals, and introduce a prototype of Behind The Paper, a system that scaffolds the research reflection process, guiding users from paper upload through interview-based elicitation to reflective writing. We discuss implications for supporting future research reflection and conclude with possible directions for future work.

  PublisherDOI

• ReFinE: Streamlining UI Mockup Iteration with Research Findings

  arXiv (Cornell University) · 2026-04-06

  articleOpen accessSenior author

  Although HCI research papers offer valuable design insights, designers often struggle to apply them in design workflows due to difficulties in finding relevant literature, understanding technical jargon, the lack of contextualization, and limited actionability. To address these challenges, we present ReFinE, a Figma plugin that supports real-time design iteration by surfacing contextualized insights from research papers. ReFinE identifies and synthesizes design implications from HCI literature relevant to the mockup's design context, and tailors this research evidence to a specific design mockup by providing actionable visual guidance on how to update the mockup. To assess the system's effectiveness, we conducted a technical evaluation and a user study. Results show that ReFinE effectively synthesizes and contextualizes design implications, reducing cognitive load and improving designers' ability to integrate research evidence into UI mockups. This work contributes to bridging the gap between research and design practice by presenting a tool for embedding scholarly insights into the UI design process.

  PublisherOA PDF

• Group Conversational Agents: A Review of Designs that Support and Shape Group Interaction

  HAL (Le Centre pour la Communication Scientifique Directe) · 2026-03-20

  preprintOpen access

  International audience

  PublisherOA PDF

• Usability of Automated External Defibrillators by untrained Bystanders in a Simulated Cardiac Arrest Scenario

  Research Square · 2026-02-11

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Understanding the Effects of Conversational Agent Personality on the Credibility of LLM-Based Conversational Search

  2026-02-28

  articleOpen accessSenior author

  The rise of Large Language Models (LLMs) has ushered in a wave of conversational search engines that allow people to engage in dialogues with LLM-infused chatbots to seek information. As people tend to infer personalities from digital social interactions, and given that personality cues have been shown to affect credibility, these perceptions of chatbot design may shape how users assess the credibility of information in conversational search. In this study, we conducted a controlled online study with 190 participants who assessed conversational search results with chatbots designed to exhibit different levels of personality traits. We found that in conversational search, personality can affect perceptions of credibility. Specifically, perceived conscientiousness and agreeableness of a chatbot can increase credibility, while perceived extraversion and neuroticism can decrease the credibility of the information. This research contributes to our understanding of how conversational interfaces and their personality and persona designs can impact credibility. We also provide design implications for conversational search interfaces based on our findings.

  PublisherDOI

• PosterMate: Audience-driven Collaborative Persona Agents for Poster Design

  2025-09-27 · 2 citations

  preprintOpen access

  Poster designing can benefit from synchronous feedback from target audiences. However, gathering audiences with diverse perspectives and reconciling them on design edits can be challenging. Recent generative AI models present opportunities to simulate human-like interactions, but it is unclear how they may be used for feedback processes in design. We introduce PosterMate, a poster design assistant that facilitates collaboration by creating audience-driven persona agents constructed from marketing documents. PosterMate gathers feedback from each persona agent regarding poster components, and stimulates discussion with the help of a moderator to reach a conclusion. These agreed-upon edits can then be directly integrated into the poster design. Through our user study (N=12), we identified the potential of PosterMate to capture overlooked viewpoints, while serving as an effective prototyping tool. Additionally, our controlled online evaluation (N=100) revealed that the feedback from an individual persona agent is appropriate given its persona identity, and the discussion effectively synthesizes the different persona agents' perspectives.

  PublisherDOI

• Synthetic Cognitive Walkthrough: Aligning Large Language Model Performance with Human Cognitive Walkthrough

  ArXiv.org · 2025-12-03

  preprintOpen accessSenior author

  Conducting usability testing like cognitive walkthrough (CW) can be costly. Recent developments in large language models (LLMs), with visual reasoning and UI navigation capabilities, present opportunities to automate CW. We explored whether LLMs (GPT-4 and Gemini-2.5-pro) can simulate human behavior in CW by comparing their walkthroughs with human participants. While LLMs could navigate interfaces and provide reasonable rationales, their behavior differed from humans. LLM-prompted CW achieved higher task completion rates than humans and followed more optimal navigation paths, while identifying fewer potential failure points. However, follow-up studies demonstrated that with additional prompting, LLMs can predict human-identified failure points, aligning their performance with human participants. Our work highlights that while LLMs may not replicate human behaviors exactly, they can be leveraged for scaling usability walkthroughs and providing UI insights, offering a valuable complement to traditional usability testing.

  PublisherOA PDFDOI

• Comparing Large Language Model AI and Human-Generated Coaching Messages for Behavioral Weight Loss

  Journal of Technology in Behavioral Science · 2025-02-24 · 9 citations

  articleOpen access

  Abstract Automated coaching messages for weight control can save time and costs, but their repetitive, generic nature may limit their effectiveness compared to human coaching. Large language model (LLM) based artificial intelligence (AI) chatbots, like ChatGPT, could offer more personalized and novel messages to address repetition with their data-processing abilities. While LLM AI demonstrates promise to encourage healthier lifestyles, studies have yet to examine the feasibility and acceptability of LLM-based BWL coaching. Eighty-seven adults in a weight-loss trial (BMI ≥ 27 kg/m 2 ) rated ten coaching messages’ helpfulness (five human-written, five ChatGPT-generated) using a 5-point Likert scale, providing additional open-ended feedback to justify their ratings. Participants also identified which messages they believed were AI-generated. The evaluation occurred in two phases: messages in Phase 1 were perceived as impersonal and negative, prompting revisions for messages in Phase 2. In Phase 1, AI-generated messages were rated less helpful than human-written ones, with 66% receiving a helpfulness rating of 3 or higher. However, in Phase 2, the AI messages matched the human-written ones regarding helpfulness, with 82% scoring three or above. Additionally, 50% were misidentified as human-written, suggesting AI’s sophistication in mimicking human-generated content. A thematic analysis of open-ended feedback revealed that participants appreciated AI’s empathy and personalized suggestions but found them more formulaic, less authentic, and too data-focused. This study reveals the preliminary feasibility and perceived helpfulness of LLM AIs, like ChatGPT, in crafting potentially effective weight control coaching messages. Our findings also underscore areas for future enhancement.

  PublisherOA PDFDOI

Recent grants

• CAREER: Advancing Social Computing with Tailored Motivators

  NSF · $498k · 2013–2019

Frequent coauthors

• Matthew P. Aylett

  Heriot-Watt University

  49 shared

• Mateusz Dubiel

  49 shared

• Anuschka Schmitt

  University of St. Gallen

  49 shared

• Zilin Ma

  49 shared

• Rafał Kocielnik

  10 shared

• Scott E. Hudson

  Carnegie Mellon University

  10 shared

• Herbert C. Duber

  Institute for Health Metrics and Evaluation

  9 shared

• Katharina Reinecke

  9 shared

Education

• PhD, Human Computer Interaction Institute

  Carnegie Mellon University

  2010

• B.S., Electrical Engineering & Computer Science

  University of California Berkeley

  2003

Awards & honors

• National Science Foundation Early CAREER Development Award (…