Artificial Intelligence (AI) is increasingly automating expert judgments across diverse domains. However, the practical dynamics of adaptation among diverse stakeholders remain underexplored. We investigated the Korea Baseball Organization’s adoption of the Automated Ball-Strike System (ABS), the first league-wide deployment of an AI adjudicator. Interviews with 38 stakeholders—umpires, players, coaches, and fans—revealed that adoption was driven by demands for fairness and frustration with human limitations, and was viewed as an inevitable trajectory. Acceptance depended less on accuracy than on verifiable consistency, which reduced interpersonal conflict by shifting judgment to technology. However, adaptive burdens were redistributed: players faced pressure to recalibrate strategies for survival, while umpires grappled with diminished authority. Systemic legitimacy hinged on procedural transparency and visible feedback mechanisms. Based on these findings, we propose governance principles emphasizing transparency and adaptive role reconfiguration for sustainable human-AI coexistence.

While it is known that North Korean defectors (NKDs) struggle with South Korea's healthcare system, the specific challenges of their patient journey remain underexplored. To investigate this, we conducted interviews with 10 NKDs about an 8-step patient journey and identified the clinical consultation step as a critical barrier for all participants, marked by three key challenges: expressing symptoms, managing social and cultural concerns, and overcoming language differences. In response, we developed Medibridge, a mobile prototype that allows users to rehearse with an AI doctor before a real hospital visit to generate a tangible "Helper Note"for their actual consultation. Our evaluation with 15 NKDs showed improvements in perceived communication capability, including greater expression clarity, reduced social and cultural concerns, and enhanced linguistic confidence. Our contributions include an empirical understanding of NKDs' healthcare challenges, a novel AI-powered rehearsal system that prepares users for real-world clinical communication, and design implications for inclusive technologies for displaced populations.

Online browsing often requires balancing open-ended exploration with focused comparison, yet most interfaces remain static regardless of user focus. Adaptive interfaces offer a way to better align interface presentation with browsing needs, but how such adaptations should be triggered, designed, and controlled in practice remains unclear. We investigate adaptive browsing interfaces that modify information hierarchy, information granularity, and session-based ordering in response to inferred browsing intent. Drawing from a conceptual framework of five adaptation dimensions, we implement ReLay, a browser-based probe that applies lightweight, in-situ adaptations automatically while allowing user overrides. In a two-phase study (n = 10), participants welcomed adaptive changes when they were transparent, consistent, and easily reversible. Users approached control as calibration rather than error correction, verifying system behavior before relying on it. These findings illustrate how intent-responsive, controllable adaptation can support browsing without diminishing user agency. Our work contributes (1) a conceptual framework for adaptive interface behaviors in online browsing, (2) an instantiation of the framework through three selected dimensions, and (3) empirical insights into user acceptance, control, and design implications for adaptive browsing systems.

Understanding unfamiliar codebases is essential yet challenging in software development. Visual aids such as call graphs and control flow graphs can help, but often lead to information overload and limited interactivity. Meanwhile, LLM-based code assistants provide accessible natural language explanations that reduce cognitive barriers, but lack spatial context for code navigation. We posit that integrating these two complementary approaches can address their respective limitations. To validate this integration, we introduce CodeVoyager, a tool that combines LLM with interactive visual aids to support more effective code comprehension. We first conducted an exploratory study (n=11) to assess the tool’s potential and identify areas for refinement. Following iterative refinement, we evaluated the enhanced tool against a widely used chat-based code assistant in a within-subjects study (n=16). Results showed that CodeVoyager improved code comprehension and increased user trust. These improvements were achieved by enabling seamless interaction between textual explanations and visual code exploration, mirroring how developers naturally discuss code. This work contributes to visual-LLM integrated developer tools through (1) a novel integration approach mirroring natural code discussion, (2) empirical evidence of improved comprehension and trust, and (3) design implications for multimodal code comprehension systems.

Conversational shopping agents powered by large language models are increasingly used for online product exploration, yet the role of interaction style in shaping shopping behavior and user experience remains underexplored in shopping IR. To address the gap, we conducted two studies in experience-goods domains. Study 1 involved 24 participants and compared a neutral conversational agent with a traditional product search interface, confirming functional adequacy and identifying two unmet needs, self-reflective preference structuring and socially grounded relational guidance. Study 2 involved 30 participants and evaluated two personas derived from these needs, Self-Mirroring and Relational Peer, against the same neutral agent in a within-subjects design with information availability held constant. Self-Mirroring increased critical thinking scores and sustained follow-up questioning of retrieved content, whereas Relational Peer increased social presence while reducing explicit verification behaviors such as comparing alternatives and checking conditions. The discussion outlines implications for AI shopping agents that adapt interaction style to decision context, balancing efficient exploration with user-led evaluation.

Ensuring visual content accessible on websites is essential for users with visual impairments. However, existing alt text authoring tools often provide alt text only in the primary language or use literal machine translations for other languages, often failing to capture cultural nuances and resulting in inaccurate or misleading descriptions. To overcome this limitation, we introduce AltCAT, the first Alt text authoring tool for web developers that incorporates Culturally-Aware Translation. AltCAT integrates culture-specific knowledge and region-appropriate image description conventions into alt text translations. Our tool consists of two main components: an Alt Text Processing Engine that detects images and alt text on a webpage, generates high-quality alt text, and provides culturally-aware translations using large multimodal models, and an Interactive Authoring Interface for refinement and HTML export. Our key contributions are introducing culturally-aware alt text translation and a practical authoring tool for multilingual web accessibility.

Conversational argument search systems influence how users access diverse perspectives but are prone to selective exposure. To address this, we propose two strategies: an interface-level multi-agent framework that structures perspective presentation and an interaction-level questioning strategy that encourages deeper engagement. We evaluate these strategies through a 2 x 2 factorial user study, examining their impact on selective exposure. Results show that the multi-agent setup facilitates broader perspective comparison, while agent-initiated questioning fosters deeper reflection; together, they promote more balanced argument access. Based on these findings, we discuss conversational search systems to mitigate selective exposure by implementing multi-agent interactions and questioning mechanisms.

The self-reference effect is a psychological phenomenon where information relating to oneself is processed more deeply and remembered more effectively than other information. We propose "self-referential reviews," crafted by merging personal information with existing reviews using the novel "Self-Referential ReviewMaker" prototype, which leverages Large Language Models (LLMs). The essence of the "self-referential review" lies in harnessing the self-reference effect, making the readers feel as if they are the protagonist of the review. To validate the efficacy of self-referential reviews, we conducted a user study focusing on online reviews with thirty-four participants. The contributions of our paper are centered around self-referential reviews, highlighting (1) the creation of these reviews using our new prototype, Self-Referential ReviewMaker, (2) their effectiveness in enhancing review helpfulness through the self-reference effect, and (3) the identification of additional factors influencing the self-reference effect with further discussion on enhancing user-focused review systems.