Walk in Their Shoes to Navigate Your Own Path: Learning About Procrastination Through A Serious Game
Procrastination, the voluntary delay of tasks despite potential negative consequences, has prompted numerous time and task management interventions in the HCI community. While these interventions have shown promise in addressing specific behaviors, psychological theories suggest that learning about procrastination itself may help individuals develop their own coping strategies and build mental resilience. However, little research has explored how to support this learning process through HCI approaches. We present ProcrastiMate, a text adventure game where players learn about procrastination’s causes and experiment with coping strategies by guiding in-game characters in managing relatable scenarios. Our field study with 27 participants revealed that ProcrastiMate facilitated learning and self-reflection while maintaining psychological distance, motivating players to integrate newly acquired knowledge in daily life. This paper contributes empirical insights on leveraging serious games to facilitate learning about procrastination and offers design implications for addressing psychological challenges through HCI approaches.[......] Align with Me, Not TO Me: How People Perceive Concept Alignment with LLM-Powered Conversational Agents
Concept alignment—building a shared understanding of concepts—is essential for human and human-agent communication. While large language models (LLMs) promise human-like dialogue capabilities for conversational agents, the lack of studies to understand people’s perceptions and expectations of concept alignment hinders the design of effective LLM agents. This paper presents results from two lab studies with human-human and human-agent pairs using a concept alignment task. Quantitative and qualitative analysis reveals and contextualizes potentially (un)helpful dialogue behaviors, how people perceived and adapted to the agent, as well as their preconceptions and expectations. Through this work, we demonstrate the co-adaptive and collaborative nature of concept alignment and identify potential design factors and their trade-offs, sketching the design space of concept alignment dialogues. We conclude by calling for designerly endeavors on understanding concept alignment with LLMs in context, as well as technical efforts to combine theory-informed and LLM-driven approaches. [......] CHI 2025 | CDI成员参会成果速览
人机交互领域的年度学术盛宴——ACM CHI Conference on Human Factors in Computing Systems (简称CHI 2025) 已于5月1日圆满落幕。作为全球人机交互领域最具影响力的标杆性学术会议、中国计算机学会（CCF）认证的A类会议，CHI在Core C[......]
实习 | AI代理技术研究与开发实习生
随着AI技术的快速发展，智能代理（AI Agent）正逐步成为人机交互的新范式。CDI实验室AI代理交互研究小组正在寻找{AI代理技术研究与开发实习生}，聚焦AI代理的核心技术链路开发，探索其在数字囤积等场景中的应用潜力。
如果你对AI代理技术充满好奇，渴望了解并找寻场景应用落地；如果你具备软[......]
实习 | CDI智能医疗研究小组
关于我们
在中国人口老龄化加剧的背景下，“康复师 + AI + 智能设备”的结合正在成为提高中老年健康管理效率的重要方式。CDI实验室智能医疗研究小组正在开发个性化病人康复教育平台，该平台将嵌入康复器械及患者治疗旅程，助力精准康复。如果你对医疗科技、人工智能、数据科学、交互设计充满热情，我们诚[......]
实习 | 服装设计&软件开发实习生
智能服装正在改变我们与科技互动的方式。CDI实验室智能织物研究小组正在开发结合时尚与功能的FashionTec智能服装，应用于医疗、运动和健康监测领域。我们现招募对 智能服装设计与数字化制版、织物运动捕捉工具平台 有热情的同学。你将参与从创意到成品的全过程，打造既美观又实用的[......]
Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning
An increasingly massive number of remote-sensing images spurs the development of extensible object detectors that can detect objects beyond training categories without costly collecting new labeled data. In this paper, we aim to develop open-vocabulary object detection (OVD) technique in aerial images that scales up object vocabulary size beyond training data. The performance of OVD greatly relies on the quality of class-agnostic region proposals and pseudo-labetls for novel object categories. To simultaneously generate high-quality proposals and pseudo-labels, we propose CastDet, a CLIP-activated student-teacher open-vocabulary object Detection framework. Our end-to-end framework following the student-teacher self-learning mechanism employs the RemoteCLIP model as an extra omniscient teacher with rich knowledge. By doing so, our approach boosts not only novel object proposals but also classification. Furthermore, we devise a dynamic label queue strategy to maintain high-quality pseudo labels during batch training. We conduct extensive experiments on multiple existing aerial object detection datasets, which are set up for the OVD task. Experimental results demonstrate our CastDet achieving superior open-vocabulary detection performance, e.g., reaching 46.5% mAP on VisDroneZSD novel categories, which outperforms the state-of-the-art open-vocabulary detectors by 21.0% mAP. To our best knowledge, this is the first work to apply and develop the open-vocabulary object detection technique for aerial images. The code is available at https://github.com/lizzy8587/CastDet.[......] Cocobo: Exploring Large Language Models as the Engine for End-User Robot Programming
End-user development allows everyday users to tailor service robots or applications to their needs. One user-friendly approach is natural language programming. However, it encounters challenges such as an expansive user expression space and limited support for debugging and editing, which restrict its application in end-user programming. The emergence of large language models (LLMs) offers promising avenues for the translation and interpretation between human language instructions and the code executed by robots, but their application in end-user programming systems requires further study. We introduce Cocobo, a natural language programming system with interactive diagrams powered by LLMs. Cocobo employs LLMs to understand users’ authoring intentions, generate and explain robot programs, and facilitate the conversion between executable code and flowchart representations. Our user study shows that Cocobo has a low learning curve, enabling even users with zero coding experience to customize robot programs successfully.
[......] 程瑶 / Yao Cheng
[email protected][......] 单润/Running
[email protected][......]