Extended abstracts on Human factors in computing systems. CHI Conference最新文献

MindScape aims to study the benefits of integrating time series behavioral patterns (e.g., conversational engagement, sleep, location) with Large Language Models (LLMs) to create a new form of contextual AI journaling, promoting self-reflection and well-being. We argue that integrating behavioral sensing in LLMs will likely lead to a new frontier in AI. In this Late-Breaking Work paper, we discuss the MindScape contextual journal App design that uses LLMs and behavioral sensing to generate contextual and personalized journaling prompts crafted to encourage self-reflection and emotional development. We also discuss the MindScape study of college students based on a preliminary user study and our upcoming study to assess the effectiveness of contextual AI journaling in promoting better well-being on college campuses. MindScape represents a new application class that embeds behavioral intelligence in AI.

With advances in AI, computer vision, and interface understanding, there is the potential to offload much of the work currently spent by companies' developers in making products accessible. There is also the potential to move our major accessibility approach from an 'inclusively-designed-products-plus-AT focus to a 'universal-interface-transformer focus. This would be a major reversal of approach and have significant ramifications for legislation, regulation, and the established large-scale accessibility industries that have grown up around them. Such a disruption would require concrete evidence that such a change would, in fact, be better for people with disabilities. It would also require a path from the former to the latter. This paper presents the case for such a shift, some of the benefits and ramifications, and the developments necessary to make the shift. It also outlines a hybrid approach between inclusive design and bespoke custom interfaces.

Although clinical training in implicit bias is essential for healthcare equity, major gaps remain both for effective educational strategies and for tools to help identify implicit bias. To understand the perspectives of clinicians on the design of these needed strategies and tools, we conducted 21 semi-structured interviews with primary care clinicians about their perspectives and design recommendations for tools to improve patient-centered communication and to help mitigate implicit bias. Participants generated three types of solutions to improve communication and raise awareness of implicit bias: digital nudges, guided reflection, and data-driven feedback. Given the nuance of implicit bias communication feedback, these findings illustrate innovative design directions for communication training strategies that clinicians may find acceptable. Improving communication skills through individual feedback designed by clinicians for clinicians has the potential to improve healthcare equity.

The last several years have seen a strong growth of telerobotic technologies with promising implications for many areas of learning. HCI has contributed to these discussions, mainly with studies on user experiences and user interfaces of telepresence robots. However, only a few telerobot studies have addressed everyday use in real-world learning environments. In the post-COVID 19 world, sociotechnical uncertainties and unforeseen challenges to learning in hybrid learning environments constitute a unique frontier where robotic and immersive technologies can mediate learning experiences. The aim of this workshop is to set the stage for a new wave of HCI research that accounts for and begins to develop new insights, concepts, and methods for use of immersive and telerobotic technologies in real-world learning environments. Participants are invited to collaboratively define an HCI research agenda focused on robot-mediated learning in the wild, which will require examining end-user engagements and questioning underlying concepts regarding telerobots for learning.

There has been a growing interest in HCI to understand the specific technological needs of people with dementia and supporting them in self-managing daily activities. One of the most difficult challenges to address is supporting the fluctuating accessibility needs of people with dementia, which vary with the specific type of dementia and the progression of the condition. Researchers have identified auto-personalized interfaces, and more recently, Artificial Intelligence or AI-driven personalization as a potential solution to making commercial technology accessible in a scalable manner for users with fluctuating ability. However, there is a lack of understanding on the perceptions of people with dementia around AI as an aid to their everyday technology use and its role in their overall self-management systems, which include other non-AI technology, and human assistance. In this paper, we present future directions for the design of AI-based systems to personalize an interface for dementia-related changes in different types of memory, along with expectations for AI interactions with the user with dementia.

User-centered design is typically framed around meeting the preferences and needs of populations involved in the design process. However, when designing technology for people with disabilities, in particular dementia, there is also a moral imperative to ensure that human rights of this segment of the population are consciously integrated into the process and respectfully included in the product. We introduce a human rights-based user-centered design process which is informed by the United Nations Convention on the Rights of Persons with Disabilities (CRPD). We conducted two editions of a three-day-long design workshop during which undergraduate students and dementia advocates came together to design technology for people with dementia. This case study demonstrates our novel approach to user-centered design that centers human rights through different stages of the workshop and actively involves people with dementia in the design process.

Introducing technology support in a complex, team-based work setting requires a study of teamwork effects on technology use. In this paper, we present our initial analysis of team communications in a trauma resuscitation setting, where we deployed a digital checklist to support trauma team leaders in guiding patient care. By analyzing speech transcripts, checklist interaction logs, and videos of 15 resuscitations, we identified several tensions that arose from the use of a checklist in a team-based process with multi-step tasks. The tensions included incorrect markings of in-progress tasks as completed, failure to mark completed tasks due to missed communications, failure to record planned tasks, and difficulties in recording dynamic values. From these findings, we discuss design implications for checklist design for dynamic, team-based activities.

Communities in Indian Country experience severe behavioral health inequities [11, 12]. Based on recent research investigating scalable behavioral health interventions and therapeutic best practices for Native American (NA) communities, we propose ARORA, a social and emotional learning intervention delivered over a networked mobile game that uses geosocial gaming mechanisms enhanced with augmented reality technology. Focusing on the Navajo community, we take a community-based participatory research approach to include NA psychologists, community health workers, and educators as co-designers of the intervention activities and gaming mechanisms. Critical questions involve operation of the application across low-infrastructure landscapes as well scalability of design practices to be inclusive of the many diverse NA cultural communities in Indian Country.

A significant number of young Americans are vulnerable to excess weight gain, especially during the college years. While technology-based weight loss interventions have the potential to be very engaging, short-term approaches showed limited success. In our work we aim to better understand the impact of long-term, multimodal, technology-based weight loss interventions, and study their potential for greater effect among college students. In this paper we lay the basis for our approach towards a multimodal health intervention for young adults: we present formative work based on interviews and a design workshop with 26 young adults. We discuss our intervention at the intersection of user feedback, empirical evidence from previous work, and behavior change theory.