arXiv subjects

Joonhwan Lee

Publications and source records attributed to Joonhwan Lee.

8 recordsLinked to original sources

arXiv2026

Analyzing Human Heuristics and Strategies in Everyday Decision-Making Conversations for Conversational AI Design

Conversational AI increasingly supports everyday decision-making, yet most systems rely on data-centric reasoning rather than the heuristic and interactional strategies people use in natural conversation. To ground design in actual human practice, we analyze 955 real-world Korean conversations (15,476 utterances) involving food and travel decisions, applying a decision-making codebook through an LLM-assisted coding pipeline. Our findings reveal that people prioritize satisficing over optimization, relying heavily on internal knowledge and interactional strategies to manage cognitive load. Critically, we identify a frequency-efficiency mismatch: the most prevalent heuristics sustain conversational flow during exploration, whereas infrequent, rule-based strategies are highly effective at driving resolution during exploitation. By mapping how these patterns transfer across the spectrum of human-AI interaction, this work provides empirical grounding consistent with cognitive theories of decision-making and offers design implications that align AI systems with human heuristic processes.

cs.HC↗

arXiv2026

Designing Transparent AI-Mediated Language Support for Intergenerational Family Communication

Intergenerational linguistic differences pose challenges to effective and intimate family communication. This paper presents GenSync, a chat-based interface that supports intergenerational understanding through different forms of translation visibility. We conducted a controlled within-subjects study with 16 family dyads (32 participants), comparing three conditions: no translation, black-box translation, and transparent translation that displays both original and interpreted messages. The results show that translation visibility plays a critical role in shaping conversational experiences. Transparent translation supported conversational quality, intimacy, and usability, while black-box translation often disrupted conversational flow. These findings position intergenerational language support as a form of interpretive mediation and contribute design implications for AI-mediated communication in socially sensitive contexts.

cs.HC↗

arXiv2026

InnerPond: Fostering Inter-Self Dialogue with a Multi-Agent Approach for Introspection

Introspection is central to identity construction and future planning, yet most digital tools approach the self as a unified entity. In contrast, Dialogical Self Theory (DST) views the self as composed of multiple internal perspectives, such as values, concerns, and aspirations, that can come into tension or dialogue with one another. Building on this view, we designed InnerPond, a research probe in the form of a multi-agent system that represents these internal perspectives as distinct LLM-based agents for introspection. Its design was shaped through iterative explorations of spatial metaphors, interaction scaffolding, and conversational orchestration, culminating in a shared spatial environment for organizing and relating multiple inner perspectives. In a user study with 17 young adults navigating career choices, participants engaged with the probe by co-creating inner voices with AI, composing relational inner landscapes, and orchestrating dialogue as observers and mediators, offering insight into how such systems could support introspection. Overall, this work offers design implications for AI-supported introspection tools that enable exploration of the self's multiplicity.

cs.HC↗

arXiv2026

Actor's Note: Examining the Role of AI-Generated Questions in Character Journaling for Actor Training

Character journaling is a well-established exercise in actor training, but many actors struggle to sustain it due to cognitive burden, the blank page problem, and unclear short-term rewards. We reframe large language models not as co-authors but as maieutic partners-tools that guide reflection through context-aware questioning rather than producing text on behalf of the user. Based on this perspective, we designed Actor's Note, a journaling tool that tailors questions to the script, role, and rehearsal phase while preserving actor agency. We evaluated the system in a 14-day crossover study with 29 actors using surveys, logs, and interviews. Results indicate that the tool reduced entry barriers, supported sustained reflection, and enriched character exploration, with participants describing different benefits when AI was introduced at earlier versus later rehearsal stages. This work contributes empirical insights and design principles for creativity-support tools that sustain reflective practices while preserving artistic immersion in performance training.

cs.HC↗

arXiv2025

Theatrical Language Processing: Exploring AI-Augmented Improvisational Acting and Scriptwriting with LLMs

The increasing convergence of artificial intelligence has opened new avenues, including its emerging role in enhancing creativity. It is reshaping traditional creative practices such as actor improvisation, which often struggles with predictable patterns, limited interaction, and a lack of engaging stimuli. In this paper, we introduce a new concept, Theatrical Language Processing (TLP), and an AI-driven creativity support tool, Scribble$.$ai, designed to augment actors' creative expression and spontaneity through interactive practice. We conducted a user study involving tests and interviews with fourteen participants. Our findings indicate that: (1) Actors expanded their creativity when faced with AI-produced irregular scenarios; (2) The AI's unpredictability heightened their problem-solving skills, specifically in interpreting unfamiliar situations; (3) However, AI often generated excessively detailed scripts, which limited interpretive freedom and hindered subtext exploration. Based on these findings, we discuss the new potential in enhancing creative expressions in film and theater studies through an AI-driven tool.

cs.HC↗

arXiv2025

Designing a User Interface for Generative Design in Augmented Reality: A Step Towards More Visualization and Feed-Forwarding

Generative design, an AI-assisted technology for optimizing design through algorithmic processes, is propelling advancements across numerous fields. As the use of immersive environments such as Augmented Reality (AR) continues to rise, integrating generative design into such platforms presents a potent opportunity for innovation. However, a vital challenge that impedes this integration is the current absence of an efficient and user-friendly interface for designers to operate within these environments effectively. To bridge this gap, we introduce a novel UI system for generative design software in AR, which automates the process of generating the potential design constraints based on the users' inputs. This system allows users to construct a virtual environment, edit objects and constraints, and export the final data in CSV format. The interface enhances the user's design experience by enabling more intuitive interactions and providing immediate visual feedback. Deriving from participatory design principles, this research proposes a significant leap forward in the realms of generative design and immersive environments.

cs.HC↗

arXiv2022

Personalization Trade-offs in Designing a Dialogue-based Information System for Support-Seeking of Sexual Violence Survivors

The lack of reliable, personalized information often complicates sexual violence survivors' support-seeking. Recently, there is an emerging approach to conversational information systems for support-seeking of sexual violence survivors, featuring personalization with wide availability and anonymity. However, a single best solution might not exist as sexual violence survivors have different needs and purposes in seeking support channels. To better envision conversational support-seeking systems for sexual violence survivors, we explore personalization trade-offs in designing such information systems. We implement a high-fidelity prototype dialogue-based information system through four design workshop sessions with three professional caregivers and interviewed with four self-identified survivors using our prototype. We then identify two forms of personalization trade-offs for conversational support-seeking systems: (1) specificity and sensitivity in understanding users and (2) relevancy and inclusiveness in providing information. To handle these trade-offs, we propose a reversed approach that starts from designing information and inclusive tailoring that considers unspecified needs, respectively.

cs.HC↗

arXiv2021

Applying the Persona of User's Family Member and the Doctor to the Conversational Agents for Healthcare

Conversational agents have been showing lots of opportunities in healthcare by taking over a lot of tasks that used to be done by a human. One of the major functions of conversational healthcare agent is intervening users' daily behaviors. In this case, forming an intimate and trustful relationship with users is one of the major issues. Factors affecting human-agent relationship should be deeply explored to improve long-term acceptance of healthcare agent. Even though a bunch of ideas and researches have been suggested to increase the acceptance of conversational agents in healthcare, challenges still remain. From the preliminary work we conducted, we suggest an idea of applying the personas of users' family members and the doctor who are in the relationship with users in the real world as a solution for forming the rigid relationship between humans and the chatbot.

cs.HC↗