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Microsoft at CHI 2024: Innovation in Human-Centered Design

Microsoft at CHI 2024: Innovation in Human-Centered Design


How people interact with a technology through its design and functionality determines its utility and acceptance in everyday use, setting the stage for widespread adoption. When computing tools and services respect the diversity of people's experiences and abilities, technology not only works, but is accessible to everyone. Human-Computer Interaction (HCI) plays a key role in this process, exploring how technology is integrated into our daily lives to meet our personal needs and enhance our interactions with the world. We are exploring ways to shape digital tools to

The ACM CHI Conference on Human Factors in Computing Systems is the premier forum for researchers and experts in the field, and Microsoft is proud to support CHI 2024 as a sponsor next year. We are pleased to announce that this year, 33 of his papers were accepted by Microsoft researchers and their collaborators, of which 4 received Best Paper awards and 7 received Honorable Mentions.

This research aims to redefine how people work, collaborate and play using technology, focusing on design innovations to create more personalized, engaging and effective interactions. That's what I mean. Several projects focus on customizing user experiences to better meet individual needs, such as exploring the potential of large-scale language models (LLMs) to reduce procrastination. Others are researching how touch can be used to create more immersive experiences and increase realism in virtual reality and mixed reality environments. Some research addresses the challenge of understanding how people interact with technology. These include applying psychology and cognitive science to explore the use of generative AI and social media, with the aim of using the insights to guide future research and design directions. This post will focus on these projects.

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Contributors: Renewable Energy Storage by Bichlien Nguyen and David Kwabi

Bichlien Nguyen, Ph.D., and David Kwabi, Ph.D., are using their research in flow batteries and machine learning to more effectively explore the vast organic chemistry space to identify compounds with properties ideal for hydropower and other renewable energy storage. Find out how it can help you identify.

Opens in new tab Best Paper Award Winners

DynaVis: Dynamically Composed UI Widgets for Visualization Editing Priyan Vaithilingam, Elena L. Glassman, Jeevana Priya Inala, Chenglong Wang may be restricted. To address this, the authors introduce his DynaVis, which combines a natural language interface with dynamically synthesized UI widgets, allowing users to initiate and adjust edits using natural language. .

Generative echo chambers? The impact of LLM-based search systems on diverse information seeking Nikhil Sharma, Q. Vera Liao, Ziang Xiao LLM-based conversational search systems can potentially improve traditional search methods , but the effects of LLM on increasing selective exposure and promoting echo chambers have not yet been investigated. This study suggests that LLM-driven conversational search can enhance biased information queries, especially when the output of his LLM strengthens the user's view, and suggests that these technologies Significant implications for development and regulation are highlighted.

Piet: Advancing Color Authoring for Motion Graphics Videos Xinyu Shi, Yinghou Wang, Yun Wang, Jian Zhao Motion graphics (MG) videos use animated visuals and color to effectively communicate complex ideas, but existing Color authoring tools are lacking. In this work, we introduce Piet, a tool prototype that provides interactive palettes and support for quick theming and controlled focus, greatly streamlining the color design process.

Metacognitive Demands and Opportunities for Generative AI Lev Tankelevitch, Viktor Kewenig, Auste Simkute, Ava Elizabeth Scott, Advait Sarkar, Abigail Sellen, Sean Rintel Generative AI systems offer unprecedented opportunities to transform professional and personal work. We offer them, but we have challenges with prompts and assessments. And depending on the output, optimize the workflow. This paper shows that metacognition, the psychological ability to monitor and control people's thoughts and actions, provides a valuable lens for understanding and designing for these usability challenges.

Honorable mention

Big or small, it's all in your head: Visual-tactile illusions of resizing using finger repositioning Myung Jin Kim, Eyal Ofek, Michel Pahud, Mike J. Sinclair, Andrea Bianchi In this study , we introduce a fixed-size VR controller that uses fingers to change position and create a visual-tactile illusion of dynamic size changes for handheld virtual objects, allowing users to view virtual objects that are significantly smaller or smaller than their real devices. Make it recognizable as if it were bigger.

LLMR: Real-time prompts for interactive worlds using large-scale language models Fernanda De La Torre, Cathy Mengying Fang, Han Huang, Andrzej Banburski-Fahey, Judith Amores, Jaron Lanier Large-scale language models (LLMR) for mixed reality , a real-world framework. -Create and modify interactive mixed reality experiences using LLM. Use new strategies to tackle difficult cases when ideal training data is lacking or when your design goals require integration of internal dynamics, intuitive analysis, or high interactivity.

Observer Effects in Social Media Use Koustuv Saha, Pranshu Gupta, Gloria Mark, Emre Kiciman, Munmun De ChoudhuryThis study investigates observer effects in behavioral assessments of social media use. The observer effect is a phenomenon in which an individual's behavior changes due to the awareness that he or she is being watched. The study, which was conducted retrospectively for an average of 82 months (approximately 7 years) and prospectively for 5 months using Facebook data, found that expected behavioral and linguistic deviations after study enrollment were based on individual psychological characteristics. It was found that it reflected The authors recommend ways to reduce observer effects in these scenarios.

Reading between the lines: Modeling user behavior and costs in AI-assisted programming Hussein Mozannar, Gagan Bansal, Adam Fourney, Eric Horvitz created CUPS, a taxonomy of programmer activities in This approach not only uncovers interaction patterns and inefficiencies, but can also drive more effective metrics for code recommendation systems and his UI design, with the aim of improving programmer productivity. .

SharedNeRF: Leveraging Photorealistic View-Dependent Rendering for Real-Time and Remote Collaboration Mose Saketa, Bala Kumaravel, Nicolai Marquardt, Andrew D. Wilson SharedNeRF, a synchronous remote collaboration system, leverages Neural Radiance Field (NeRF) technology. provides photorealistic rendering. Capture dynamic movement and change within shared spaces with perspective-specific rendering seamlessly integrated with point clouds. In a preliminary study, participants were able to successfully complete a flower arranging task using this high-fidelity, multi-view visualization, demonstrating its effectiveness.

Understanding the role of large-scale language models in personalization and scaffolding strategies to combat academic procrastination Ananya Bhattacharjee, Yucheng Zeng, Sara Yi Xu, Dana Kurzabaeva, Mingyi Ma, Rachel Kornfield, Saeed Ishtiaq Ahmed, Alex Mariakakis, Mary P. Czerwinsky, Anastasia Kuzminik, Michael Liut, Joseph Jay Williams Explore the potential of LLM to customize interventions. Their findings highlight the need for LLMs to provide systematic, deadline-oriented advice and adaptive questioning techniques, and provide important design insights for LLM-based tools as well as for therapeutic guidance. We emphasize caution in the use of .

Where are we so far? Understanding data storytelling tools from the perspective of human-AI collaboration Haotian Li, Yun Wang, Huamin Qu This paper evaluates data storytelling tools using a dual framework. , a step-by-step analysis of storytelling workflows, planning, implementation, communication, and the roles of humans and AI. At each stage: author, assistant, optimizer, reviewer, etc. This study identifies common collaboration patterns in existing tools, summarizes lessons from these patterns, and highlights opportunities for future research on human-AI collaboration in data storytelling.

To learn more about our commitment and contributions to CHI 2024, including a complete list of publications, please visit the conference webpage.

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