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Ruijian Hannah Guan

dblp:404/5783 · DBLP profile ↗
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3ranked-venue papers
0as first author
3since 2021 · last 2026
0009-0009-6636-7087ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 3 · 3 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
2 papers
Human-robot interaction · 48% Health and well-being technologies · 24% Design research and methods · 21%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Design research and methods › research methodology
meta-analysis
0.912025
Virtually the Same or Realistically Different?: A Meta-Analysis of Real vs. 'Not So Real' Robots · HRI 2025
Human-robot interaction › robot embodiment
physical embodiment
0.912025
Virtually the Same or Realistically Different?: A Meta-Analysis of Real vs. 'Not So Real' Robots · HRI 2025
Human-robot interaction
social robot
0.912025
Virtually the Same or Realistically Different?: A Meta-Analysis of Real vs. 'Not So Real' Robots · HRI 2025
Human-AI interaction › human-AI collaboration
clinician-AI collaboration
0.312026
Balancing Efficiency and Empathy: Healthcare Providers' Perspectives on AI-Supported Workflows for Serious Illness Conversations in the Emergency Department · CHI 2026
Human-robot interaction
anthropomorphism
0.312025
Virtually the Same or Realistically Different?: A Meta-Analysis of Real vs. 'Not So Real' Robots · HRI 2025

Methods — techniques the papers use, named apart from their topics

workflow analysis · 1.0semi-structured interviews · 1.0meta-analysis · 0.9
YearPublicationVenuePosition
2026 Balancing Efficiency and Empathy: Healthcare Providers' Perspectives on AI-Supported Workflows for Serious Illness Conversations in the Emergency Department
abstract
Serious Illness Conversations (SICs)—discussions about values and care preferences for patients with life-threatening illness—rarely occur in Emergency Departments (EDs), despite evidence that early conversations improve care alignment and reduce unnecessary interventions. We interviewed 11 ED providers to identify challenges in SICs and opportunities for technology support, with a focus on AI. Our analysis revealed a four-stage SIC workflow (identification, preparation, conduction, documentation) and barriers at each stage, including fragmented patient information, limited time and space, lack of conversational guidance, and burdensome documentation. Providers expressed interest in AI systems for synthesizing information, supporting real-time conversations, and automating documentation, but emphasized concerns about preserving human connection and clinical autonomy. This tension highlights the need for technologies that enhance efficiency without undermining the interpersonal nature of SICs. We propose design guidelines for ambient and peripheral AI systems to support providers while preserving the essential humanity of these conversations.
Menglin Zhao, Zhuorui Yong, Ruijian Hannah Guan, Kai-Wei Chang 0001, Adrian Haimovich, Kei Ouchi, Timothy W. Bickmore, Zhan Zhang 0008, Bingsheng Yao, Dakuo Wang, Smit Desai
CHI3
2025 Virtually the Same or Realistically Different?: A Meta-Analysis of Real vs. 'Not So Real' Robots
abstract
This study examined an important debate in Human-Robot Interaction (HRI) research: the suitability of non-physically non-collocated robots instead of physically collocated robots for HRI research. This meta-analysis ($\mathrm{N}=34$studies) examined the equivalence of physically and non-physically collocated robots in HRI research, focusing on anthropomorphism, social presence, and user engagement. No significant differences were found, suggesting that non-physical representations are viable alternatives. However, observed heterogeneity indicates potential moderating factors (e.g., task complexity, user characteristics, design features) warranting further investigation. These findings inform choices in resource-constrained environments.
Connor Esterwood, Ruijian Hannah Guan, Xin Ye 0027, Lionel P. Robert Jr.
HRI2
2025 Exploring Collaborative GenAI Agents in Synchronous Group Settings: Eliciting Team Perceptions and Design Considerations for the Future of Work
abstract
While generative artificial intelligence (GenAI) is finding increased adoption in workplaces, current tools are primarily designed for individual use. Prior work established the potential for these tools to enhance personal creativity and productivity towards shared goals; however, we don't know yet how to best take into account the nuances of group work and team dynamics when deploying GenAI in work settings. In this paper, we investigate the potential of collaborative GenAI agents to augment teamwork in synchronous group settings through an exploratory study that engaged 25 professionals across 6 teams in speculative design workshops and individual follow-up interviews. Our workshops included a mixed reality provotype to simulate embodied collaborative GenAI agents capable of actively participating in group discussions. Our findings suggest that, if designed well, collaborative GenAI agents offer valuable opportunities to enhance team problem-solving by challenging groupthink, bridging communication gaps, and reducing social friction. However, teams' willingness to integrate GenAI agents depended on its perceived fit across a number of individual, team, and organizational factors. We outline the key design tensions around agent representation, social prominence, and engagement and highlight the opportunities spatial and immersive technologies could offer to modulate GenAI influence on team outcomes and strike a balance between augmentation and agency.
Janet G. Johnson, Macarena Peralta, Mansanjam Kaur, Ruijie Sophia Huang, Sheng Zhao 0001, Ruijian Hannah Guan, Shwetha Rajaram, Michael Nebeling
Proc. ACM Hum. Comput. Interact.6