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.
Chinese literati gatherings (Wenren Yaji), as a situated form of Chinese traditional culture, remain underexplored in depth. Although generative AI supports powerful multimodal generation, current cultural applications largely emphasize aesthetic reproduction and struggle to convey the deeper meanings of cultural rituals and social frameworks. Based on embodied cognition, we propose an AI-driven dual-path framework for cultural understanding, which we instantiate through GatheringSense, a literati-gathering experience. We conduct a mixed-methods study (N = 48) to compare how AI-generated multimodal content and embodied participation complement each other in supporting the understanding of literati gatherings and fostering cultural resonance. Our results show that AI-generated content effectively improves the readability of cultural symbols and initial emotional attraction, yet limitations in physical coherence and micro-level credibility may affect users’ satisfaction. In contrast, embodied experience significantly deepens participants’ understanding of ritual rules and social roles, and increases their psychological closeness and presence. Based on these findings, we offer empirical evidence and five transferable design implications for generative experience in cultural heritage.
While previous research has attempted to link features of individuals' drawings to their emotional states, it often overlooks the deeply personal and context-driven nature of visual expression. To bridge the gap, we conducted a two‑week diary study with 21 participants, who used a custom‑built app to track daily emotions through free drawings, followed by interviews reflecting on their artwork. Among the 252 drawings gathered, we found no strong correlations between reported emotions and measurable drawing behaviors; instead, participants expressed emotions through diverse approaches, from illustrations of emotion sources (e.g., events, objects) and metaphors, to emojis, literal text and spontaneous, random mark-making. Participants developed consistent personal styles and described drawing as an intuitive, playful, and safe outlet, though some faced challenges with the ambiguity of visual expressions and interpreting their creations afterwards. With the lessons learned, we discuss opportunities for designing expression-centered emotion tracking technologies that embrace individuality and creativity.
Teenagers regularly use social media to connect and share information with peers. While much existing research focuses on the adverse impacts of social media on teens' privacy and well-being, little research has examined how teens' privacy could be strengthened through participating in online peer communities. Through a qualitative analysis of conversations in two teen-oriented communities on Reddit and Discord, we explore how teens leverage storytelling and conversations with peers to unpack privacy dilemmas in their lives. Our findings highlight the potential of these online interactions to help teens cope with privacy violations, make sense of complex social matters, and nurture their sense of agency. We recommend platform design directions to explore the implications of collective sensemaking in peer-driven online contexts, and call for a broader conceptualization of youth privacy and research on privacy literacy.
Blockchain technologies are increasingly embedded in everyday digital experiences, yet their underlying mechanisms remain opaque to young learners. We present Blockchain@School, an unplugged board-game-based toolkit designed to make concepts such as distributed consensus, immutability, and cryptographic security tangible via collaborative play. Developed through an iterative design process with educators, Blockchain@School combines physical blocks and cards with a lightweight web application for real-time validation. We evaluated the toolkit in a series of 90-minute workshops with more than 300 primary and middle school learners (ages 9–13), using pre/post questionnaires, structured observations, and teacher feedback. Findings indicate significant improvements in learners’ understanding of blockchain principles, alongside high engagement and effective collaboration. Teachers reported that the activities can be easily integrated into existing curricula and replicated in autonomy. Our work demonstrates how unplugged, game-based learning can lower entry barriers to emerging technologies, foster digital citizenship, and support scalable approaches to teaching complex computational ideas in K–12 education.
In human conversation, empathic dialogue requires nuanced temporal cues indicating whether the conversational partner is paying attention. This type of "active listening" is overlooked in the design of Conversational Agents (CAs), which use the same pacing for one conversation. To model the temporal cues in human conversation, we need CAs that dynamically adjust response pacing according to user input. We qualitatively analyzed ten cases of active listening to distill five context-aware pacing strategies: Reflective Silence, Facilitative Silence, Empathic Silence, Holding Space, and Immediate Response. In a between-subjects study (N=50) with two conversational scenarios (relationship and career-support), the context-aware agent scored higher than static-pacing control on perceived human-likeness, smoothness, and interactivity, supporting deeper self-disclosure and higher engagement. In the career-support scenario, the CA yielded higher perceived listening quality and affective trust. This work shows how insights from human conversation like context-aware pacing can empower the design of more empathic human-AI communication.
Non-experts increasingly engage in user-driven algorithm auditing, interacting directly with AI systems to probe, document, and reflect on biased behavior. Yet, auditing remains challenging due to model opacity and limited support for navigating and interpreting outputs. This paper explores the design and evaluation of interfaces grounded in the sensemaking framework to support non-experts in auditing gender bias in image captioning. In a between-subjects study, 60 participants audited an image captioning model using one of three interface conditions: a Baseline interface, a Masking Tool for image manipulation, or a Filtering Tool for organizing captions. Our findings show that interface design shaped what participants noticed, how they interpreted model behavior, and supported their hypotheses. The Image Masking Tool enabled fine-grained testing of visual cues and context, while the Text Filtering Tool revealed broader asymmetries in gendered language. We argue that incorporating sensemaking into auditing practices can advance accountability and transparency in machine learning systems.