While generative AI tools are increasingly adopted for creative and analytical tasks, their role in interpretive practices, where meaning is subjective, plural, and non-causal, remains poorly understood. This paper examines AI-assisted tarot reading, a divinatory practice in which users pose a query, draw cards through a randomized process, and ask AI systems to interpret the resulting symbols. Drawing on interviews with tarot practitioners and Hartmut Rosa’s Theory of Resonance, we investigate how users seek, negotiate, and evaluate resonant interpretations in a context where no causal relationship exists between the query and the data being interpreted. We identify distinct ways practitioners incorporate AI into their interpretive workflows, including using AI to navigate uncertainty and self-doubt, explore alternative perspectives, and streamline or extend existing divinatory practices. Based on these findings, we offer design recommendations for AI systems that support interpretive meaning-making without collapsing ambiguity or foreclosing user agency.

Large language models (LLMs) are increasingly used to make sense of unstructured data, but their use in contexts like conversation analysis, where sensemaking is often human-driven, iterative, and subjective, is underexplored. We introduce Forage, a retrieval-augmented generation (RAG) tool for making sense of conversation data through exploratory search. We report on user studies with 27 participants across four user groups—including NPR journalists and municipal staff—observing how Forage is used to explore and analyze conversation data. We find Forage supports insight confirmation and generation, providing structure and novel insight about search results compared to a non-LLM-enabled search tool. Driven by the goal of generating multiple perspectives in Forage, we present Wild Forage, a design provocation that generates and presents multiple interpretations of the same data along a specified axis of potential interpretive difference, like political orientation. Expert user studies show that exposure to other interpretations through Wild Forage can help users meaningfully consider their assumptions in sensemaking, but findings also highlight the importance of future work to leverage this opportunity while avoiding potential harms, like stereotyping.

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.

CHI ’25, Yokohama, Japan

Facilitated dialogue is increasingly popular as a method of civic engagement and as a method for gathering social insight, but resources for its study are scant.We present Fora, a unique collection of annotated facilitated dialogues.We compile 262 facilitated conversations that were hosted with partner organizations seeking to engage their members and surface insights regarding issues like education, elections, and public health, primarily through the sharing of personal experience.Alongside this corpus of 39,911 speaker turns, we present a framework for the analysis of facilitated dialogue.We taxonomize key personal sharing behaviors and facilitation strategies in the corpus, annotate a 25% sample (10,000+ speaker turns) of the data accordingly, and evaluate and establish baselines on a number of tasks essential to the identification of these phenomena in dialogue.We describe the data, and relate facilitator behavior to turn-taking and participant sharing.We outline how this research can inform future work in understanding and improving facilitated dialogue, parsing spoken conversation, and improving the behavior of dialogue agents.