Knowledge gaps often arise during communication due to diverse backgrounds, knowledge bases, and vocabularies. With recent LLM developments, providing real-time knowledge support is increasingly viable, but is challenging due to shared and individual cognitive limitations (e.g., attention, memory, and comprehension) and the difficulty in understanding the user's context and internal knowledge. To address these challenges, we explore the key question of understanding how people want to receive real-time knowledge support. We built StopGap -- a prototype that provides real-time knowledge support for explaining jargon words in videos -- to conduct a design probe study (N=24) that explored multiple visual knowledge representation formats. Our study revealed individual differences in preferred representations and highlighted the importance of user agency, personalization, and mixed-initiative assistance. Based on our findings, we map out six key design dimensions for real-time LLM knowledge support systems and offer insights for future research in this space.

Digital media platforms (e.g., science blogs) offer opportunities to communicate scientific content to general audiences at scale. However, these audiences vary in their scientific expertise, literacy levels, and personal backgrounds, making effective science communication challenging. To address this challenge, we designed TranSlider, an AI-powered tool that generates personalized translations of scientific text based on individual user profiles (e.g., hobbies, location, and education). Our tool features an interactive slider that allows users to steer the degree of personalization from 0 (weakly relatable) to 100 (strongly relatable), leveraging LLMs to generate the translations with chosen degrees. Through an exploratory study with 15 participants, we investigated both the utility of these AI-personalized translations and how interactive reading features influenced users' understanding and reading experiences. We found that participants who preferred higher degrees of personalization appreciated the relatable and contextual translations, while those who preferred lower degrees valued concise translations with subtle contextualization. Furthermore, participants reported the compounding effect of multiple translations on their understanding of scientific content. Drawing on these findings, we discuss several implications for facilitating science communication and designing steerable interfaces to support human-AI alignment.

Humans have the ability to anticipate what will happen in their environment based on perceived information. Their anticipation is often manifested as an externally observable behavioral reaction, which cues other people in the environment that something bad might happen. As robots become more prevalent in human spaces, robots can leverage these visible anticipatory responses to assess whether their own actions might be "a bad idea?" In this study, we delved into the potential of human anticipatory reaction recognition to predict outcomes. We conducted a user study wherein 30 participants watched videos of action scenarios and were asked about their anticipated outcome of the situation shown in each video ("good" or "bad"). We collected video and audio data of the participants reactions as they were watching these videos. We then carefully analyzed the participants’ behavioral anticipatory responses; this data was used to train machine learning models to predict anticipated outcomes based on human observable behavior. Reactions are multimodal, compound and diverse, and we find significant differences in facial reactions. Model performances are around 0.5-0.6 test accuracy, and increase notably when nonreactive participants are excluded from the dataset. We discuss the implications of these findings and future work. This research offers insights into improving the safety and efficiency of human-robot interactions, contributing to the evolving field of robotics and human-robot collaboration.

Inaccessible urban infrastructure creates and reinforces systemic exclusion of people with disabilities and impacts public health, physical activity, and quality of life for all. To improve the design of our cities and to enable more equitable policies and location-centric technology designs, we need new data collection techniques, data standards, and accessibility-infused analytic tools and interactive maps focused on the quality, safety, and accessibility of pathways, transit ecosystems, and buildings. In this workshop, we bring together leading experts in human mobility, urban design, disability, and accessible computing to discuss pressing urban access challenges across the world and brainstorm solutions. We invite contributions from practitioners, transit officials, disability advocates, and researchers.

Urban accessibility assessments are challenging: they involve varied stakeholders across decision-making contexts while serving a diverse population of people with disabilities. To better support urban accessibility assessment using data visualizations, we conducted a three-part interview study with 25 participants across five stakeholder groups using map visualization probes. We present a multi-stakeholder analysis of visualization needs and sensemaking processes to explore how interactive visualizations can support stakeholder decision making. In particular, we elaborate how stakeholders’ varying levels of familiarity with accessibility, geospatial analysis, and specific geographic locations influences their sensemaking needs. We then contribute 10 design considerations for geovisual analytic tools for urban accessibility communication, planning, policymaking, and advocacy.

Traditionally, urban accessibility is defined as the ease of reaching destinations. Studies on urban accessibility for pedestrians with mobility disabilities (e.g., wheelchair users) have primarily focused on understanding the challenges that the built environment imposes and how they overcome them. In this paper, we move beyond physical barriers and focus on socio-political challenges in the civic ecosystem that impedes accessible infrastructure development. Using a multi-stakeholder approach, we interviewed five primary stakeholder groups (N=25): (1) people with mobility disabilities, (2) caregivers, (3) accessibility advocates, (4) department officials, and (5) policymakers. We discussed their current accessibility assessment and decision-making practices. We identified the key needs and desires of each group, how they differed, and how they interacted with each other in the civic ecosystem to bring about change. We found that people, politics, and money were intrinsically tied to underfunded accessibility improvement projects "without continued support from the public and the political leadership, existing funding may also disappear. Using the insights from these interviews, we explore how may technology enhance our stakeholders" decision-making processes and facilitate accessible infrastructure development.

Despite the major role of Global Positioning Systems (GPS) as a navigation tool for people with visual impairments (VI), a crucial missing aspect of point-to-point navigation with these systems is the last-few-meters wayfinding problem. Due to GPS inaccuracy and inadequate map data, systems often bring a user to the vicinity of a destination but not to the exact location, causing challenges such as difficulty locating building entrances or a specific storefront from a series of stores. In this paper, we study this problem space in two parts: (1) A formative study (N=22) to understand challenges, current resolution techniques, and user needs; and (2) A design probe study (N=13) using a novel, vision-based system called Landmark AI to understand how technology can better address aspects of this problem. Based on these investigations, we articulate a design space for systems addressing this challenge, along with implications for future systems to support precise navigation for people with VI.

We introduce Project Sidewalk, a new web-based tool that enables online crowdworkers to remotely label pedestrian-related accessibility problems by virtually walking through city streets in Google Street View. To train, engage, and sustain users, we apply basic game design principles such as interactive onboarding, mission-based tasks, and progress dashboards. In an 18-month deployment study, 797 online users contributed 205,385 labels and audited 2,941 miles of Washington DC streets. We compare behavioral and labeling quality differences between paid crowdworkers and volunteers, investigate the effects of label type, label severity, and majority vote on accuracy, and analyze common labeling errors. To complement these findings, we report on an interview study with three key stakeholder groups (N=14) soliciting reactions to our tool and methods. Our findings demonstrate the potential of virtually auditing urban accessibility and highlight tradeoffs between scalability and quality compared to traditional approaches.

Walkability indices such as walkscore.com model the proximity and density of walkable destinations within a neighborhood. While these metrics have gained widespread use (e.g., incorporated into real-estate tools), they do not integrate accessibility-related features such as sidewalk conditions or curb ramps-thereby excluding a significant portion of the population. In this poster paper, we explore the initial design and implementation of neighborhood accessibility models and visualizations for people with mobility impairments. We are able to overcome previous data availability challenges by using the Project Sidewalk API, which provides access to 255,000+ labels about the accessibility and location of DC sidewalks.

We present Project Sidewalk, a new online tool that allows anyone-from motivated citizens to government workers-to remotely label accessibility problems by virtually walking through city streets. Basic game design principles such as interactive onboarding, mission-based tasks, and stats dashboards are used to train, engage, and sustain users. We describe the current Project Sidewalk system, present results of a pilot public deployment with 581 users, and discuss open questions and future work.

The recent integration of thermal cameras with commodity smartphones presents an opportunity to engage the public in evaluating energy-efficiency issues in the built environment. However, it is unclear how novice users without professional experience or training approach thermographic energy auditing activities. In this paper, we recruited 10 participants for a four-week field study of end-user behavior exploring novice approaches to semi-structured thermographic energy auditing tasks. We analyze thermographic imagery captured by participants as well as weekly surveys and post-study debrief interviews. Our findings suggest that while novice users perceived thermal cameras as useful in identifying energy-efficiency issues in buildings, they struggled with interpretation and confidence. We characterize how novices perform thermographic-based energy auditing, synthesize key challenges, and discuss implications for design.