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 interest focuses on improving human‑AI collaboration (objectively and subjectively) with human‑centered design. My previous work leverages user research methods, builds upon theories from cognitive science and social science, targets personalization, transparency, and trust calibration issues when humans interact with AI agents/systems.
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)
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)
What will happen if AI participate equally in human group 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.
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.
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 '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