Hey, this is Shuai. I am a PhD candidate in HCI Lab at Hong Kong University of Science and Technology (HKUST). I'm fortunate to be advised by Prof. Xiaojuan Ma.
My research goal is to design human-centered interactive systems that users can easily understand and use, appropriately rely on, and effectively collaborate with.
Shuai Ma, Xinru Wang, Ying Lei, Chuhan Shi, Ming Yin, Xiaojuan Ma. (CHI 2024)
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This paper investigates human self-confidence calibration in AI-assisted decision-making, conducting three user studies to analyze its effect on human-AI collaboration. The research examines the impact of self-confidence on AI reliance and tests three calibration mechanisms. Findings indicate that proper self-confidence calibration improves rational behavior and appropriate reliance in human-AI teams, offering insights into enhancing collaboration efficiency.
Chengbo Zheng, Kangyu Yuan, Bingcan Guo, Reza Hadi Mogavi, Zhenhui Peng, Shuai Ma, Xiaojuan Ma. (CHI 2024)
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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. The findings highlight the potential and challenges of integrating AI data in educational assessments, informing the development of future educational tools for data analysis and presentation.
Ying Lei, Shuai Ma, Yuling Sun. (CHI 2024)
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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. Utilizing the social convoy model, we examine the evolving support roles within migrants' networks, alongside the challenges and expectations related to ICT-supported social connections. Our findings offer in-depth insights into the social connections and support systems of late-life migrants, culminating in design implications for future ICT-based support systems for this demographic.
Shuai Ma, Ying Lei, Xinru Wang, Chengbo Zheng, Chuhan Shi, Ming Yin, Xiaojuan Ma. (CHI 2023)
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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 (N=293) showed that our CL exploitation strategies promoted more appropriate human trust in AI, compared with only using AI confidence.
Chuhan Shi, Yicheng Hu, Shenan Wang, Shuai Ma, Chengbo Zheng, Xiaojuan Ma, Qiong Luo. (CHI 2023)
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Targeting Multi-step Human-AI Collaboration task for chemists, we proposed a human-AI collaborative system, RetroLens, through a participatory design process. AI can contribute by two approaches: joint action and algorithm-inthe-loop.
Chengbo Zheng, Yuheng Wu, Chuhan Shi, Shuai Ma, Jiehui Luo, Xiaojuan Ma. (CHI 2023)
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What will happen if AI participate equally in human group decision-making? We studied this problem in teacher-AI collaborative decision-making. We find that although the voice of AI is considered valuable, AI still plays a secondary role in the group because it cannot fully follow the dynamics of the discussion and make progressive contributions. Moreover, the divergent opinions of our participants regarding an "equal AI" shed light on the possible future of human-AI relations.
Shuai Ma, Mingfei Sun, Xiaojuan Ma. (TOCHI 2022)
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For human-robot teaching scenario, we propose an adaptive modeling and expression method to facilitate the transparent communication of robots' learning statuses during human demonstration.
Shuai Ma, Taichang Zhou, Fei Nie, Xiaojuan Ma. (CHI 2022)
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Focusing on synchronous online classes (e.g., real-time Zoom-based classes), Glancee address instructors’ difficulty to observe students’ learning status due to students’ unwillingness to show their videos. Specifically, we mitigate the gap that lack of empirical investigation on instructors’ preferences and lack of exploration of designing adaptable systems to meet the needs of individual instructors.
Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Shuai Ma, Mo Yu, Xiaojuan Ma, Hongan Wang. (CSCW 2021)
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We investigated how chatbots can be designed to provide information and emotional support for pregnant women in an online health community.
Shuai Ma, Zijun Wei, Feng Tian, Xiangmin Fan, Jianming Zhang, Xiaohui Shen, Zhe Lin, Jin Huang, Radomir Mech, Dimitris Samaras, Hongan Wang. (CHI 2019)
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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 to cater to users' individual preferences in photo composition. Based on the proposed algorithm, we designed SmartEye, which can gradually learn users' preferences as the interaction goes on.
Program Committee: ACM FAccT '24, ACM CHI '24 LBW, ACM CHI '23 LBW
Conference Review: ACM CHI '24 (1), '23 (2), '22, '20, '19, CSCW '23, UIST '22, CHI EA '23, '22, WWW '21
Journal Review: ACM TOCHI, ACM TiiS, CCF TOPCI
Volunteer: CHI' 23 (3), CHI' 22 (4)
(1) (2) received Special Recognition for Outstanding Reviews for CHI 2023, CHI 2024
(3) (4) received Student Volunteer Award for CHI 2022, CHI 2023