Geovisualizations are powerful tools for communicating spatial information, but are inaccessible to screen-reader users. To address this limitation, we present GeoVisA11y, an LLM-based question-answering system that makes geovisualizations accessible through natural language interaction. The system supports map reading, analysis, interpretation and navigation by handling analytical, geospatial, visual, and contextual queries. Through user studies with six screen-reader users and six sighted participants, we demonstrate that GeoVisA11y effectively bridges accessibility gaps while revealing distinct interaction patterns between user groups. We contribute: (1) an open-source, accessible geovisualization system, (2) empirical findings on query and navigation differences, and (3) a dataset of geospatial queries to inform future research on accessible data visualization.

The output quality of large language models (LLMs) can be improved via “reasoning”: generating segments of chain-of-thought (CoT) content to further condition the model prior to producing user-facing output. While these chains contain valuable information, they are verbose and lack explicit organization, making them tedious to review. Moreover, they lack opportunities for user feedback, such as removing unwanted considerations, adding desired ones, or clarifying unclear assumptions. We introduce Interactive Reasoning, an interaction design that visualizes chain-of-thought outputs as a hierarchy of topics and enables user review and modification. We implement interactive reasoning in Hippo, a prototype for AI-assisted decision making in the face of uncertain trade-offs. In a user study with 16 participants, we find that interactive reasoning in Hippo allows users to quickly identify and interrupt erroneous generations, efficiently steer the model towards customized responses, and better understand both model reasoning and model outputs. Our work contributes to a new paradigm that incorporates user oversight into LLM reasoning processes.

Psychological research has identified different patterns individuals have while making decisions, such as vigilance (making decisions after thorough information gathering), hypervigilance (rushed and anxious decision-making), and buckpassing (deferring decisions to others). We examine whether these decision-making patterns affect peoples’ engagement with AI-generated information in decision-making. In an online experiment with 810 participants tasked with distinguishing food facts from myths, we found that a higher buckpassing tendency was positively correlated with the likelihood of seeking AI information and reported reliance on AI, while being negatively correlated with the time spent reading AI explanations. In contrast, the higher a participant tended towards vigilance, the more carefully they scrutinized the AI’s information, as indicated by an increased time spent looking through the AI’s explanations. These findings suggest that a person’s decision-making pattern plays a significant role in their interactions with AI suggestions, which provides a new understanding of individual differences in AI-assisted decision-making.

Figure 1: We introduce GeoQA 3 , a novel accessible AI-based question-answering system for geovisualizations designed for screen-reader users.(A) Through a custom query pipeline, we combine geo-statistical analysis with an LLM to balance accuracy and performance.(B) Users can navigate the map through natural language commands or keyboard controls and (C) zoom in to view county-level data.The AI Chat system is context-aware, taking into account user interactions.See video for demonstration.

Despite diverse mobility needs worldwide, existing mapping tools fail to address the varied experiences of different mobility device users. This paper presents a large-scale online survey exploring how five mobility groups -- users of canes, walkers, mobility scooters, manual wheelchairs, and motorized wheelchairs -- perceive sidewalk barriers. Using 52 sidewalk barrier images, respondents evaluated their confidence in navigating each scenario. Our findings (N=190) reveal variations in barrier perceptions across groups, while also identifying shared concerns. To further demonstrate the value of this data, we showcase its use in two custom prototypes: a visual analytics tool and a personalized routing tool. Our survey findings and open dataset advance work in accessibility-focused maps, routing algorithms, and urban planning.

Large language models (LLMs) have been positioned to revolutionize HCI, by reshaping not only the interfaces, design patterns, and sociotechnical systems that we study, but also the research practices we use.To-date, however, there has been little understanding of LLMs' uptake in HCI.We address this gap via a systematic literature review of 153 CHI papers from 2020-24 that engage with LLMs.We taxonomize: (1) domains where LLMs are applied; (2) roles of LLMs in HCI projects; (3) contribution types; and (4) acknowledged limitations and risks.We find LLM work in 10 diverse domains, primarily via empirical and artifact contributions.Authors use LLMs in five distinct roles, including as research tools or simulated users.Still, authors often raise validity and reproducibility concerns, and overwhelmingly study closed models.We outline opportunities to improve HCI research with and on LLMs, and provide guiding questions for researchers to consider the validity and appropriateness of LLM-related work.

Digital technologies have positively transformed society, but they have also led to undesirable consequences not anticipated at the time of design or development. We posit that insights into past undesirable consequences can help researchers and practitioners gain awareness and anticipate potential adverse effects. To test this assumption, we introduce Blip, a system that extracts real-world undesirable consequences of technology from online articles, summarizes and categorizes them, and presents them in an interactive, web-based interface. In two user studies with 15 researchers in various computer science disciplines, we found that Blip substantially increased the number and diversity of undesirable consequences they could list in comparison to relying on prior knowledge or searching online. Moreover, Blip helped them identify undesirable consequences relevant to their ongoing projects, made them aware of undesirable consequences they “had never considered,” and inspired them to reflect on their own experiences with technology.

Geovisualizations are powerful tools for exploratory spatial analysis, enabling sighted users to discern patterns, trends, and relationships within geographic data. However, these visual tools have remained largely inaccessible to screen-reader users. We introduce AltGeoViz, a new interactive geovisualization approach that dynamically generates alt-text descriptions based on the user’s current map view, providing voiceover summaries of spatial patterns and descriptive statistics. In a remote user study with five screen-reader users, we found that participants were able to interact with spatial data in previously infeasible ways, demonstrated a clear understanding of data summaries and their location context, and could synthesize spatial understandings of their explorations. Moreover, we identified key areas for improvement, such as the addition of spatial navigation controls and comparative analysis features.

Computer science research has led to many breakthrough innovations but has also been scrutinized for enabling technology that has negative, unintended consequences for society. Given the increasing discussions of ethics in the news and among researchers, we interviewed 20 researchers in various CS sub-disciplines to identify whether and how they consider potential unintended consequences of their research innovations. We show that considering unintended consequences is generally seen as important but rarely practiced. Principal barriers are a lack of formal process and strategy as well as the academic practice that prioritizes fast progress and publications. Drawing on these findings, we discuss approaches to support researchers in routinely considering unintended consequences, from bringing diverse perspectives through community participation to increasing incentives to investigate potential consequences. We intend for our work to pave the way for routine explorations of the societal implications of technological innovations before, during, and after the research process.

The HCI community has been advocating preregistration as a practice to improve the credibility of scientific research. However, it remains unclear how HCI researchers preregister studies and what preregistration users perceive as benefits and challenges. By systematically reviewing the past four CHI proceedings and surveying 11 researchers, we found that only 1.11% of papers presented preregistered studies, though both authors and reviewers of preregistered studies perceive it as beneficial. Our formative studies revealed key challenges ranging from a lack of detail about the study design, hindering comprehensibility, to inconsistencies between preregistrations and published papers. To explore ways for addressing these issues, we developed Apéritif, a research prototype that scaffolds the preregistration process and automatically generates analysis code and a methods description. In an evaluation with 17 HCI researchers, we found that Apéritif reduces the effort of preregistering a study, facilitates researchers’ workflows, and promotes consistency between research artifacts.