Hi, this is Shuai. I am a 3rd year PhD candidate in HCI Lab at Hong Kong University of Science and Technology (HKUST). I'm fortunate to be advised by Prof. Xiaojuan Ma. Previously, I was trained in HCI (my master's degree) from the HCI lab of Chinese Academy of Science, supervised by Prof. Feng Tian. I also luckily worked with Prof. Xiangmin Fan, Prof. Dakuo Wang, Prof. Ming Yin.
My research interest focuses on improving human-AI interaction experience with human-centered design. My previous work targets personalization, transparency, trust, and adaptability issues when humans interact with AI agents/systems.
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.
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.
Program Committee: ACM CHI '23 LBW
Conference Review: ACM CHI '23 (1)(2), '22, '20, '19, CSCW '23, UIST '22, CHI EA '23, '22, WWW '21
Journal Review: ACM TOCHI, ACM TiiS, CCF TOPCI
Volunteer: CHI' 22 (3), CHI' 23
(1) received Special Recognition for Outstanding Reviews for CHI 23
(2) (3) received Student Volunteer Award for CHI 22, 23