My research focuses on human-AI interaction.
I employ human-centered design principles to design, develop, and evaluate interactive AI systems aimed at augmenting humans in seeking, synthesizing, and leveraging information.
My work also encompasses speculative design, design fictions, and co-design methodologies to envision and explore the future role of AI in contexts such as decision making and learning.
🔥 News
2024.08: ✏️ I will serve as an associate chair (AC) in the computational interaction subcommittee at CHI 2025!
2024.08: 👨🎓 Successfully passed my PhD thesis defense!
2024.07: 🎉 Our paper on graph layout in immersive environments has been accepted for the conference track at ISMAR 2024!
Creating presentation slides is a critical but time-consuming task for data scientists. While researchers have proposed many AI techniques to lift data scientists’ burden on data preparation and model selection, few have targeted the presentation creation task. Based on the needs identified from a formative study, this paper presents NB2Slides, an AI system that facilitates users to compose presentations of their data science work. NB2Slides uses deep learning methods as well as example-based prompts to generate slides from computational notebooks, and take users’ input (e.g., audience background) to structure the slides. NB2Slides also provides an interactive visualization that links the slides with the notebook to help users further edit the slides. A follow-up user evaluation with 12 data scientists shows that participants believed NB2Slides can improve efficiency and reduces the complexity of creating slides. Yet, participants questioned the future of full automation and suggested a human-AI collaboration paradigm.
CHI ’23
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, and Xiaojuan
Ma
In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems , Hamburg, Germany, 2023
Existing research on human-AI collaborative decision-making focuses mainly on the interaction between AI and individual decision-makers. There is a limited understanding of how AI may perform in group decision-making. This paper presents a wizard-of-oz study in which two participants and an AI form a committee to rank three English essays. One novelty of our study is that we adopt a speculative design by endowing AI equal power to humans in group decision-making. We enable the AI to discuss and vote equally with other human members. 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.
CHI ’24
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, and Xiaojuan
Ma
In Proceedings of the CHI Conference on Human Factors in Computing Systems , Honolulu, HI, USA, 2024
Students’ increasing use of Artificial Intelligence (AI) presents new challenges for assessing their mastery of knowledge and skills in project-based learning (PBL). This paper introduces a co-design study to explore the potential of students’ AI usage data as a novel material for PBL assessment. We conducted workshops with 18 college students, encouraging them to speculate an alternative world where they could freely employ AI in PBL while needing to report this process to assess their skills and contributions. Our workshops yielded various scenarios of students’ use of AI in PBL and ways of analyzing such usage grounded by students’ vision of how educational goals may transform. We also found that students with different attitudes toward AI exhibited distinct preferences in how to analyze and understand their use of AI. Based on these findings, we discuss future research opportunities on student-AI interactions and understanding AI-enhanced learning.
UIST ’24
DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration
Chengbo
Zheng, Yuanhao
Zhang, Zeyu
Huang, Chuhan
Shi, Minrui
Xu, and Xiaojuan
Ma
In the upcoming UIST ’24 , Pittsburgh, PA, USA, 2024
Interdisciplinary studies often require researchers to explore literature in diverse branches of knowledge. Yet, navigating through the highly scattered knowledge from unfamiliar disciplines poses a significant challenge. In this paper, we introduce DiscipLink, a novel interactive system that facilitates collaboration between researchers and large language models (LLMs) in interdisciplinary information seeking (IIS). Based on users’ topic of interest, DiscipLink initiates exploratory questions from the perspectives of possible relevant fields of study, and users can further tailor these questions. DiscipLink then supports users in searching and screening papers under selected questions by automatically expanding queries with disciplinary-specific terminologies, extracting themes from retrieved papers, and highlighting the connections between papers and questions. Our evaluation, comprising a within-subject comparative experiment and an open-ended exploratory study, reveals that DiscipLink can effectively support researchers in breaking down disciplinary boundaries and integrating scattered knowledge in diverse fields. The findings underscore the potential of LLM-powered tools in fostering information-seeking practices and bolstering interdisciplinary research.
🐈 Cats
My girlfriend 🍊 (huge credits!) and I raise a lovely short-hair silver gradient cat with green eyes named Sylvia ❄️🌲 (雪松 in Chinese).