Kangyu Yuan

Kangyu Yuan

Phd student in Hong Kong University of Science and Technology, SAR, China.

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publications

CriTrainer: An Adaptive Training Tool for Critical Paper Reading

Published in User Interface Software and Technology (UIST) (CCF-A), 2023

Abstract

Learning to read scientific papers critically, which requires first grasping their main ideas and then raising critical thoughts, is important yet challenging for novice researchers. The traditional ways to develop critical paper reading (CPR) skills, e.g., checking general tutorials or taking reading courses, often can not provide individuals with adaptive and accessible support. In this paper, we first derive user requirements of a CPR training tool based on literature and a survey study (N=52). Then, we develop CriTrainer, an interactive tool for CPR training. It leverages text summarization techniques to train readers’ skills in grasping the paper’s main ideas. It further utilizes template-based generated questions to help them learn how to raise critical thoughts. A mixed-design study (N=24) shows that compared to a baseline tool with general CPR guidance, students trained by CriTrainer perform better in independently raising critical thinking questions on a new paper. We conclude with design considerations for CPR training tools.

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

Published in Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (CCF-A), 2024

Abstract

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.

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(Co-author) Exploring the Evolvement of Artwork Descriptions in Online Creative Community under the Surge of Generative AI: A Case Study of DeviantArt

Published in Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CCF-A), 2024

Abstract

The rise of AI-generated content (AIGC) is transforming online creative communities (OCCs) and posing challenges to their regulation. Artwork description may reveal creators’ practice and motivation in creating and sharing artwork. Understanding the influence of AIGC on creators’ descriptions of shared artwork could be helpful for community regulation. In this work, we collect 235K posts from DeviantArt, a large creative community that allows uploading AIGC. We confirm the prevalence of AIGC in the community. Through an open coding on 800 randomly sampled posts, we identify five themes in artwork descriptions. We quantitatively examine how these themes are affected by the prevalence of AIGC via statistical analysis. Results indicate a shift towards commercial opportunities and a reduced focus on copyright since the prevalence of AIGC. Descriptions for AI-generated artworks are more likely to direct members to other creations than those for human-created artworks. Finally, we discuss insights for OCCs.

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