Shuai Ma Human–AI Collaboration · HCI · Human-Aware AI
Open Positions

Join us to work on exciting HCI and human-AI collaboration research.

I am currently recruiting Master/MPhil Students, Research Assistants, interns, and Postdoctoral researchers. We welcome motivated students and researchers who are interested in Human-Centered AI, human-AI/Agent collaboration, and publishing impactful research papers.

Positions

  • Master/MPhil Students
  • Research Assistants
  • Interns
  • Postdoctoral Researcher (unlimited headcount)

Research Areas

Human–Centered AI Human-AI/Agent Collaboration Agentic UI LLM-Powered Human Modeling/Simulation

How to Apply

Please send your CV to mashuai@iscas.ac.cn with the subject line:

[Master/RA/Postdoc Application] + Your Name

I am an Associate Professor at Institute of Software, Chinese Academy of Sciences (ISCAS). Before joining ISCAS, I was a Postdoctoral Researcher at the Computational Behavior Lab of Aalto University and Finnish AI Center, where I worked with Prof. Antti Oulasvirta. Previously, I received my PhD from the HCI Lab at the Hong Kong University of Science and Technology (HKUST), where I was advised by Prof. Xiaojuan Ma.

My research lies in human-computer interaction, with a particular emphasis on human-AI collaboration systems that advance hybrid intelligence and improve complementary team performance. I study both fundamental mechanisms and interactive systems for human-aware AI.

Foundational research Human-AI collaboration, user modeling, trust calibration, explainability, and adaptive interaction.
Application domains Decision making, education and learning, work and creation, healthcare and wellbeing.
View Publications Email: mashuai[AT]iscas.ac.cn

Human–AI Collaboration

New frameworks and collaboration paradigms for stronger human–AI team performance.

User Modeling

Capturing preferences, capabilities, beliefs, and behaviors for adaptive AI systems.

Human-Aware AI

Building AI that can interpret human states and support interaction more intelligently.

Real-world Impact

Interactive AI systems for education, decision support, work, healthcare, and wellbeing.

Selected Publications

My work spans human-AI collaboration, user modeling, education, healthcare, and interactive systems design. Below are selected publications and representative projects.

Research focus chart
Adaptive Prompt Elicitation for Text-to-Image Generation
IUI 2026

Adaptive Prompt Elicitation for Text-to-Image Generation

Xinyi Wen, Lena Hegemann, Xiaofu Jin, Shuai Ma, Antti Oulasvirta.

When we describe our needs to LLMs, we often know what we want internally, but struggle to articulate our needs and intentions clearly and precisely through language. To address this challenge, we propose Adaptive Prompt Elicitation, which adaptively selects visual queries to help AI quickly refine human intent. This approach significantly improves human intention alignment while introducing no additional workload.

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Echoes of Norms
CHI 2026

PriorWeaver: Prior Elicitation via Iterative Dataset Construction

Yuwei Xiao, Shuai Ma, Antti Oulasvirta, Eunice Jun.

Expressing prior beliefs is a critical step in Bayesian analysis—but translating intuitive knowledge into statistical distributions is notoriously difficult. We introduce PriorWeaver, an interactive system that lets analysts visually construct datasets to express their assumptions. By turning intuitive beliefs into data and iteratively refining them through prior predictive checks, PriorWeaver helps analysts produce priors that better match their expectations.

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Echoes of Norms
CHI 2026

Echoes of Norms: Investigating Counterspeech Bots' Influence on Bystanders in Online Communities

Mengyao Wang, Shuai Ma, Nuo Li, Peng Zhang, Chenxin Li, Ning Gu, Tun Lu.

Hate speech moderation often focuses on offenders and targets—but what about the silent majority watching the conversation? We build Civilbot, a counterspeech bot designed to intervene in online discussions, and study how it influences bystanders. Our results show that reason-based counterspeech delivered with a positive tone is most effective, revealing how well-designed bots can subtly mobilize bystanders and shape healthier online discourse.

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Echoes of Norms
CHI 2026

"Shall We Dig Deeper?": Designing and Evaluating Strategies for LLM Agents to Advance Knowledge Co-Construction in Asynchronous Online Discussions

Yuanhao Zhang, Wenbo Li, Xiaoyu Wang, Kangyu Yuan, Shuai Ma, Xiaojuan Ma.

Online discussions promise collective knowledge building—but many stall at shallow exchanges. We design an LLM-powered facilitation agent that strategically intervenes to help conversations dig deeper. Through a study with 60 participants across multiple discussions, we show that AI interventions can push discussions from surface-level sharing toward deeper reasoning and synthesis, revealing how AI can sustain more meaningful online discourse.

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When Traditional Medicine Meets AI
CSCW 2025

When Traditional Medicine Meets AI: Critical Considerations for AI-Empowered Clinical Support in Traditional Medicine

Yuling Sun, Wenjing Yue, Xiaofu Jin, Shuai Ma, Xiaojuan Ma, Xiaoling Wang.

Traditional Medicine (TM) has long served as a cornerstone of healthcare, yet its integration with AI remains limited. Through interviews with 16 TM clinicians, we explore how AI-powered clinical systems are perceived and used in practice. Our findings reveal low adoption due to TM’s unique diagnostic logic and data challenges. We discuss these barriers and propose ways to better align AI innovations with the realities of traditional medicine.

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Towards Human-AI Deliberation
CHI 2025 Honorable Mention

Towards Human-AI Deliberation: Design and Evaluation of LLM-Empowered Deliberative AI for AI-Assisted Decision-Making

Shuai Ma, Qiaoyi Chen, Xinru Wang, Chengbo Zheng, Zhenhui Peng, Ming Yin, Xiaojuan Ma.

What happens when you disagree with your AI collaborator during decision-making? We explore how to resolve such conflicts by leveraging the power of Retrieval-Augmented Generation. Acting as a communication bridge, a large language model enables deliberation between human decision-makers and domain-specific smaller models, significantly improving decision quality in the face of disagreement.

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AI Afterlife as Digital Legacy
CHI 2025 Honorable Mention

"AI Afterlife" as Digital Legacy: Perceptions, Expectations, and Concerns

Ying Lei, Shuai Ma*, Yuling Sun, Xiaojuan Ma. (* corresponding author)

Can AI-generated digital humans serve as a form of digital legacy—allowing us to “live on” in this world? Our comprehensive qualitative analysis investigates the full lifecycle of the “AI afterlife,” shedding light on how society envisions—and grapples with—this future.

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Signaling Human Intentions to Service Robots
CHI 2025 Honorable Mention

Signaling Human Intentions to Service Robots: Understanding the Use of Social Cues during In-Person Conversations

Hanfang Lyu, Xiaoyu Wang, Nandi Zhang, Shuai Ma, Qian Zhu, Yuhan Luo, Fugee Tsung, Xiaojuan Ma.

How do humans and service robots naturally express intentions through interactive actions in social settings? Our work offers valuable insights into natural human-robot interactions in the era of embodied intelligence, revealing how subtle, intuitive behaviors can serve as effective communicative cues.

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DBox
CHI 2025

DBox: Scaffolding Algorithmic Programming Learning through Learner-LLM Co-Decomposition

Shuai Ma, Junling Wang, Yuanhao Zhang, Xiaojuan Ma, April Yi Wang.

Can LLMs be used to help learners learn algorithmic programming? We propose a Learner-LLM Co-Decomposition approach to make LLMs scaffold such learning process.

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Scaffolded Turns and Logical Conversations
CHI 2025

Scaffolded Turns and Logical Conversations: Designing Humanized LLM-Powered Conversational Agents for Hospital Admission Interviews

Dingdong Liu, Yujing Zhang, Bolin Zhao, Shuai Ma, Chuhan Shi, Xiaojuan Ma.

We present a humanized conversational agent for hospital admissions that balances efficiency and empathy. Co-designed with clinicians, our system uses dynamic graph-based conversation flows and context-aware scaffolding to adaptively guide interviews. It outperforms existing solutions in both data accuracy and user experience, offering a promising path for AI-assisted healthcare interactions.

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以双向理解促进人智协同
CCCF 2025

以双向理解促进人智协同——以人机合作决策为例

Shuai Ma, Xiaojuan Ma, Chuhan Shi, Chengbo Zheng.

We promote human-AI hybrid intelligence from the perspective of mutual understanding between humans and AI. This paper systematically introduces our previous work on enabling human-AI hybrid intelligence by enhancing human understanding of AI, AI’s understanding of humans, and humans’ self-understanding.

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Beyond Recommender
ArXiv 2024

Beyond Recommender: An Exploratory Study of the Effects of Different AI Roles in AI-Assisted Decision Making

Shuai Ma, Chenyi Zhang, Xinru Wang, Xiaojuan Ma, Ming Yin.

AI is often used as a recommender in decision-making, but this can reduce human analytical thinking and cause over-reliance on AI. This paper examines three AI roles—Recommender, Analyzer, and Devil’s Advocate—at two performance levels, revealing their unique strengths and weaknesses and informing the design of adaptive AI assistants.

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Are You Really Sure
CHI 2024

"Are You Really Sure?" Understanding the Effects of Human Self-Confidence Calibration in AI-Assisted Decision Making

Shuai Ma, Xinru Wang, Ying Lei, Chuhan Shi, Ming Yin, Xiaojuan Ma.

This paper investigates human self-confidence calibration in AI-assisted decision-making, conducting three user studies to analyze its effect on human-AI collaboration. Findings indicate that proper self-confidence calibration improves rational behavior and appropriate reliance in human-AI teams.

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Charting the Future of AI in Project-Based Learning
CHI 2024

Charting the Future of AI in Project-Based Learning: A Co-Design Exploration with Students

Chengbo Zheng, Kangyu Yuan, Bingcan Guo, Reza Hadi Mogavi, Zhenhui Peng, Shuai Ma, Xiaojuan Ma.

This study investigates using AI usage data for learning assessment in project-based education. Through workshops with college students, we explored how AI data can reflect students’ skills and contributions, informing the development of future educational tools.

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Late-life Migration
CHI 2024

Unpacking ICT-supported Social Connections and Support of Late-life Migration: From the Lens of Social Convoys

Ying Lei, Shuai Ma, Yuling Sun.

This paper explores the ICT-mediated social connections of late-life migrants, focusing on the dynamic changes in their social networks and the roles of ICT in these shifts. Our findings offer in-depth insights and design implications for future ICT-based support systems for this demographic.

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Feature Engineering with Human and AI Knowledge
DIS 2024

Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Workers' Perceptions in Human&AI-Assisted Feature Engineering Design

Qian Zhu, Dakuo Wang, Shuai Ma, April Wang, Zixin Chen, Udayan Khurana, Xiaojuan Ma.

How do data scientists view suggestions from peers and AI when performing feature engineering? We designed a human-AI collaborative feature engineering framework and conducted an empirical study to uncover key opportunities and limitations in current AI support.

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Human-centered Design of XAI
ArXiv 2024

Towards Human-centered Design of Explainable Artificial Intelligence (XAI): A Survey of Empirical Studies

Shuai Ma

We conduct a survey study on XAI from empirical study perspectives, highlighting the importance of designing human-centered XAI methods from the explainee’s perspective.

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SelfGauge
UIST 2024 Adjunct

SelfGauge: An Intelligent Tool to Support Student Self-assessment in GenAI-enhanced Project-based Learning

Chengbo Zheng, Zeyu Huang, Shuai Ma, Xiaojuan Ma.

Project-based learning in the age of generative AI makes assessment challenging. SelfGauge supports student self-assessment by analyzing GenAI usage and project artifacts, helping students define criteria, seek feedback, and reflect on performance.

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Who Should I Trust
CHI 2023

Who Should I Trust: AI or Myself? Leveraging Human and AI Correctness Likelihood to Promote Appropriate Trust in AI-Assisted Decision-Making

Shuai Ma, Ying Lei, Xinru Wang, Chengbo Zheng, Chuhan Shi, Ming Yin, Xiaojuan Ma.

We proposed to promote humans’ appropriate trust based on the correctness likelihood of both sides at a task-instance level. Results from a between-subjects experiment showed that our strategies promoted more appropriate human trust in AI compared with only using AI confidence.

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RetroLens
CHI 2023

RetroLens: A Human-AI Collaborative System for Multi-step Retrosynthetic Route Planning

Chuhan Shi, Yicheng Hu, Shenan Wang, Shuai Ma, Chengbo Zheng, Xiaojuan Ma, Qiong Luo.

Targeting a multi-step human-AI collaboration task for chemists, we proposed RetroLens through a participatory design process. AI contributes by both joint action and algorithm-in-the-loop collaboration.

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Competent but Rigid
CHI 2023

Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making

Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, Xiaojuan Ma.

We studied what happens when AI participates equally in human group decision-making. Although the voice of AI is considered valuable, it still plays a secondary role because it cannot fully follow discussion dynamics or make progressive contributions.

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Robot Learning Engagement
TOCHI 2022

Modeling Adaptive Expression of Robot Learning Engagement and Exploring its Effects on Human Teachers

Shuai Ma, Mingfei Sun, Xiaojuan Ma.

For human-robot teaching scenarios, we propose an adaptive modeling and expression method to facilitate transparent communication of robots’ learning statuses during human demonstration.

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Glancee
CHI 2022

Glancee: An Adaptable System for Instructors to Grasp Student Learning Status in Synchronous Online Classes

Shuai Ma, Taichang Zhou, Fei Nie, Xiaojuan Ma.

Focusing on synchronous online classes, Glancee addresses instructors’ difficulty in observing students’ learning status when students are unwilling to show video. It provides adaptable support tailored to instructors’ needs.

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CASS
CSCW 2021

CASS: Towards Building A Social-Support Chatbot for Online Health Community

Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Shuai Ma, Mo Yu, Xiaojuan Ma, Hongan Wang.

We investigated how chatbots can be designed to provide information and emotional support for pregnant women in an online health community.

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SmartEye
CHI 2019 Honorable Mention

SmartEye: Assisting Instant Photo Taking via Integrating User Preference with Deep View Proposal Network

Shuai Ma, Zijun Wei, Feng Tian, Xiangmin Fan, Jianming Zhang, Xiaohui Shen, Zhe Lin, Jin Huang, Radomir Mech, Dimitris Samaras, Hongan Wang.

How to effectively personalize a general model? We proposed a user preference modeling method based on interactive machine learning and designed a confidence-based integration framework to personalize a deep neural network for individual photo composition preferences.

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Pre-screen
UIST 2019 Adjunct

Pre-screen: Assisting Material Screening in Early-stage of Video Editing

Qian Zhu*, Shuai Ma*, Cuixia Ma. (* equal contribution)

Video editing is challenging due to the time-consuming task of screening useful clips from raw footage. Based on surveys and interviews, we developed Pre-screen, a tool that offers global and detailed video analysis and intelligent material screening.

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Reminder
UIST 2019 Adjunct

What Did I Miss? Assisting User-adaptive Missed Content Reviewing in MOOC Learning

Qian Zhu*, Shuai Ma*. (* equal contribution)

This paper introduces Reminder, a system that detects divided attention in MOOCs using cameras on PC and mobile devices. It predicts attention scores, adapts to users, and offers visualizations to help learners review missed content.

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Human-AI Interaction in Healthcare
CHI 2019 Workshop

Human-AI Interaction in Healthcare: Three Case Studies About How Patient(s) And Doctors Interact with AI in a Multi-Tiers Healthcare Network

Yunzhi Li, Liuping Wang, Shuai Ma, Xiangmin Fan, Zijun Wang, Junfeng Jiao, Dakuo Wang.

This position paper introduces three ongoing research projects focused on designing, developing, and evaluating systems for human-AI interaction in healthcare across a multi-tier healthcare network.

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Parkinson's Disease Detection
CHI 2018 LBW

Implicit detection of motor impairment in Parkinson's disease from everyday smartphone interactions

Jing Gao, Feng Tian, Junjun Fan, Dakuo Wang, Xiangmin Fan, Yicheng Zhu, Shuai Ma, Jin Huang, Hongan Wang.

We explored the feasibility and accuracy of detecting motor impairment in Parkinson’s disease via implicitly sensing and analyzing users’ everyday smartphone interactions, achieving strong discrimination performance in a 42-subject study.

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mirrorU
CHI 2018 LBW

mirrorU: scaffolding emotional reflection via in-situ assessment and interactive feedback

Liuping Wang, Xiangmin Fan, Feng Tian, Lingjia Deng, Shuai Ma, Jin Huang, Hongan Wang.

We present mirrorU, a mobile system that supports users in reflecting on and writing about daily emotional experiences through in-situ assessment and interactive feedback.

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