GAMES Webinar 2024 – 363期(跨界叙事革命：从游戏线索重构到医疗人机协作，AI如何重塑多领域交互范式) | Xiyuan Wang(ShanghaiTech University)，Yuansong Xu(ShanghaiTech University), ，Hanfang Lyu(HKUST), Dingdong LIU(HKUST)
【GAMES Webinar 2025-363期】(可视化专题-跨界叙事革命：从游戏线索重构到医疗人机协作，AI如何重塑多领域交互范式)
报告嘉宾：Xiyuan Wang(ShanghaiTech University)
报告时间：2025年4月17号星期四晚上8:00-8:15（北京时间）
报告题目：ClueCart: Supporting Game Story Interpretation and Narrative Inference from Fragmented Clues
报告摘要：
Indexical storytelling is gaining popularity in video games, where the narrative unfolds through fragmented clues. This approach fosters player-generated content and discussion, as story interpreters piece together the overarching narrative from these scattered elements. However, the fragmented and non-linear nature of the clues makes systematic categorization and interpretation challenging, potentially hindering efficient story reconstruction and creative engagement. To address these challenges, we first proposed a hierarchical taxonomy to categorize narrative clues, informed by a formative study. Using this taxonomy, we designed ClueCart, a creativity support tool aimed at enhancing creators’ ability to organize story clues and facilitate intricate story interpretation. We evaluated ClueCart through a between-subjects study (N=40), using Miro as a baseline. The results showed that ClueCart significantly improved creators’ efficiency in organizing and retrieving clues, thereby better supporting their creative processes. Additionally, we offer design insights for future studies focused on player-centric narrative analysis.
讲者简介：
Xiyuan Wang（王希元）is a third-year master student majoring in Computer Science at ShanghaiTech University supervised by Prof. Quan Li. Her research interests lies in human-computer interaction (HCI) and large data visualization (VIS). Besides, she is currently exploring research in game storytelling. For more information, see https://horanny.github.io/
讲者主页：https://horanny.github.io/
报告嘉宾：Yuansong Xu(ShanghaiTech University)
报告时间：2025年4月17号星期四晚上8:15-8:30（北京时间）
报告题目：Advancing Problem-Based Learning with Clinical Reasoning for Improved Differential Diagnosis in Medical Education
报告摘要：
Medical education increasingly emphasizes students’ ability to apply knowledge in real-world clinical settings, focusing on evidencebased clinical reasoning and differential diagnoses. Problem-based learning (PBL) addresses traditional teaching limitations by embedding learning into meaningful contexts and promoting active participation. However, current PBL practices are often confined to medical instructional settings, limiting students’ ability to selfdirect and refine their approaches based on targeted improvements. Additionally, the unstructured nature of information organization during analysis poses challenges for record-keeping and subsequent review. Existing research enhances PBL realism and immersion but overlooks the construction of logic chains and evidence-based reasoning. To address these gaps, we designed e-MedLearn, a learnercentered PBL system that supports more efficient application and practice of evidence-based clinical reasoning. Through controlled study (N=19) and testing interviews (N=13), we gathered data to assess the system’s impact. The findings demonstrate that e-MedLearn improves PBL experiences and provides valuable insights for advancing clinical reasoning-based learning.
讲者简介：
Yuansong Xu （徐源松）is a second-year master’s student at the School of Information Science and Technology, ShanghaiTech University, supervised by Prof. Quan Li (李权）. Yuansong also earned a B.S. degree from the same university. His research centers on human-computer interaction (HCI) and data visualization (VIS), with a recent focus on exploring their applications in medical education. More information is in https://yansen-xu.github.io/
讲者主页：https://yansen-xu.github.io/
报告嘉宾：Hanfang Lyu(The Hong Kong University of Science and Technology)
报告时间：2025年4月17号星期四晚上8:30-8:45（北京时间）
报告题目：Signaling Human Intentions to Service Robots: Understanding the Use of Social Cues during In-Person Conversations
报告摘要：
As social service robots become commonplace, it is essential for them to effectively interpret human signals, such as verbal, gesture, and eye gaze, when people need to focus on their primary tasks to minimize interruptions and distractions. Toward such a socially acceptable Human-Robot Interaction, we conducted a study (N=24) in an AR-simulated context of a coffee chat. Participants elicited social cues to signal intentions to an anthropomorphic, zoomorphic, grounded technical, or aerial technical robot waiter when they were speakers or listeners. Our findings reveal common patterns of social cues over intentions, the effects of robot morphology on social cue position and conversational role on social cue complexity, and users’ rationale in choosing social cues. We offer insights into understanding social cues concerning perceptions of robots, cognitive load, and social context. Additionally, we discuss design considerations on approaching, social cue recognition, and response strategies for future service robots.
讲者简介：
Hanfang Lyu (吕涵放) is a third-year PhD student in Computer Science Department of The Hong Kong University of Science and Technology supervised by Prof. Xiaojuan Ma (麻晓娟). Her research interests lie in human computer interaction (HCI) and human robot interaction (HRI). She is currently exploring human robot value alignment.
报告嘉宾：Dingdong LIU(The Hong Kong University of Science and Technology)
报告时间：2025年4月17号星期四晚上8:45-9:00（北京时间）
报告题目：Scaffolded Turns and Logical Conversations: Designing Humanized LLM-Powered Conversational Agents for Hospital Admission Interviews
报告摘要：
Hospital admission interviews are critical for patient care but strain nurses’ capacity due to time constraints and staffing shortages. While LLM-powered conversational agents (CAs) offer automation potential, their rigid sequencing and lack of humanized communication skills risk misunderstandings and incomplete data capture. Through participatory design with clinicians and volunteers, we identified essential communication strategies and developed a novel CA that implements these strategies through: (1) dynamic topic management using graph-based conversation flows, and (2) context-aware scaffolding with few-shot prompt tuning. Technical evaluation on an admission interview dataset showed our system achieving performance comparable to or surpassing human-written ground truth, while outperforming prompt-engineered baselines. A between-subject study (N=44) demonstrated significantly improved user experience and data collection accuracy compared to existing solutions. We contribute a framework for humanizing medical CAs by translating clinician expertise into algorithmic strategies, alongside empirical insights for balancing efficiency and empathy in healthcare interactions, and considerations for generalizability.
讲者简介：
Dingdong LIU (刘定东) is a third-year PhD student in Computer Science Department of The Hong Kong University of Science and Technology supervised by Prof. Xiaojuan Ma (麻晓娟). His research interests lie in human computer interaction (HCI) and human robot interaction (HRI), with a focus on interaction mode between human and AI agents. He is currently exploring equipping AI agents with humans’ empirical skills.
主持人简介：
李权，上海科技大学信息科学与技术学院助理教授（终身教授序列）、研究员、博士生导师，从事人工智能及可视分析、可解释性机器学习以及人机交互技术的研究。他博士毕业于香港科技大学计算机科学与工程学系。任中国图象图形学学会可视化与可视分析专委会委员，IEEE VIS Paper程序委员会委员、ChinaVis论文国际程序委员会委员、IEEE VIS, EuroVis, PacificVis, ChinaVis, ACM CHI/CSCW及TVCG等顶级学术会议期刊审稿人，他曾任美国佐治亚理工学院计算机科学与工程学院的访问研究员、微众银行人工智能部资深研究员及网易游戏资深研究员。他的学术成果发表在IEEE VIS, EuroVis, IEEE PacificVis, ACM CHI, CSCW, UIST, IUI, CGF, TVCG等可视化及人机交互顶级期刊和会议。主持国家自然科学基金面上项目。更多信息见https://faculty.sist.shanghaitech.edu.cn/liquan/
石楚涵，东南大学计算机科学与工程学院副教授，2023年获得香港科技大学计算机专业博士学位。研究方向包括数据可视化、可视分析、人智协同及其在自然科学、精准医疗等领域的应用，在相关领域的IEEE TVCG、ACM CHI、ACM CSCW等国际顶级期刊和会议发表论文20余篇。任中国图象图形学学会可视化与可视分析专委会委员、中国计算机学会人机交互专委会委员，任ACM CHI、ACM CSCW、PacificVis VisNotes等国际权威会议的程序委员会委员，以及ACM CHI、ACM UIST、VIS等会议审稿人。更多信息见：https://shichuhan.github.io/
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