arXiv subjects

Hyerim Park

Publications and source records attributed to Hyerim Park.

4 recordsLinked to original sources

arXiv2026

What Are You Really Asking For? A Comparative 5W1H Analysis of Learner Questioning in CPR Training with IVAs in Screen-based and Augmented Reality Environments

Question-asking is one of the key indicators of cognitive engagement. However, understanding how the distinct psychological affordances of presentation media shape learners' spoken inquiries with embodied Intelligent Virtual Agents (IVAs) remains limited. To systematically examine this process, we propose a 5W1H-based framework for analyzing learner questions. Using this framework, we conducted a user study comparing an Augmented Reality-based IVA (AR-IVA) deployed in the physical environment with a screen-based IVA (Video-IVA) during cardiopulmonary resuscitation (CPR) instruction. Results showed that the AR-IVA elicited higher spatial and social presence and promoted more frequent and longer questions focused on clarification and understanding. In contrast, the Video-IVA encouraged questions regarding procedural refinement. Presence acted as a selective filter, shaping the timing and topic of questions rather than as a universal mediator. These effects were significantly moderated by learners' motivational and strategic characteristics toward learning. Based on these findings, we propose design implications for IVA-supported learning systems.

cs.HC↗

arXiv2026

Enhancing Generative AI Image Refinement with Scribbles and Annotations: A Comparative Study of Multimodal Prompts

Generative AI (GenAI) image tools are increasingly used in design practice, enabling rapid ideation but offering limited support for refinement tasks such as adjusting layout, scale, or visual attributes. While text prompts and inpainting allow localized edits, they often remain inefficient or ambiguous for precise, in-context, and iterative refinement -- motivating the exploration of alternative methods. This work examines how pen-based scribbles and annotations can enhance GenAI image refinement. A formative study with seven professional designers informed a prototype supporting three input modalities: text-only, visual-only, and combined prompting. A within-subjects study with 30 designers and design students compared these modalities across closed- and open-ended tasks, evaluating expressiveness, efficiency, workload, user experience, iteration, and multimodal strategies. Visual prompts improved clarity and speed for spatial edits while reducing workload, whereas text remained effective for semantic and global changes. The combined modality received the highest overall ratings, enabling complementary use, balancing spatial precision with semantic detail, and supporting smoother iteration. Task-specific preferences also emerged: adding new objects often required both modalities, while moving or modifying elements was typically handled through visual input. This work contributes (1) an empirical comparison of multimodal prompting for GenAI refinement, (2) a prototype integrating scribbles and annotations, and (3) insights into designers' multimodal strategies to inform future GenAI interfaces that better support refinement in GenAI-supported design workflows.

cs.HC↗

arXiv2026

Evaluating Generative AI in the Lab: Methodological Challenges and Guidelines

Generative AI (GenAI) systems are inherently non-deterministic, producing varied outputs even for identical inputs. While this variability is central to their appeal, it challenges established HCI evaluation practices that typically assume consistent and predictable system behavior. Designing controlled lab studies under such conditions therefore remains a key methodological challenge. We present a reflective multi-case analysis of four lab-based user studies with GenAI-integrated prototypes, spanning conversational in-car assistant systems and image generation tools for design workflows. Through cross-case reflection and thematic analysis across all study phases, we identify five methodological challenges and propose eighteen practice-oriented recommendations, organized into five guidelines. These challenges represent methodological constructs that are either amplified, redefined, or newly introduced by GenAI's stochastic nature: (C1) reliance on familiar interaction patterns, (C2) fidelity-control trade-offs, (C3) feedback and trust, (C4) gaps in usability evaluation, and (C5) interpretive ambiguity between interface and system issues. Our guidelines address these challenges through strategies such as reframing onboarding to help participants manage unpredictability, extending evaluation with constructs such as trust and intent alignment, and logging system events, including hallucinations and latency, to support transparent analysis. This work contributes (1) a methodological reflection on how GenAI's stochastic nature unsettles lab-based HCI evaluation and (2) eighteen recommendations that help researchers design more transparent, robust, and comparable studies of GenAI systems in controlled settings.

cs.HC↗

arXiv2025

Exploring Visual Prompts: Refining Images with Scribbles and Annotations in Generative AI Image Tools

Generative AI (GenAI) tools are increasingly integrated into design workflows. While text prompts remain the primary input method for GenAI image tools, designers often struggle to craft effective ones. Moreover, research has primarily focused on input methods for ideation, with limited attention to refinement tasks. This study explores designers' preferences for three input methods - text prompts, annotations, and scribbles - through a preliminary digital paper-based study with seven professional designers. Designers preferred annotations for spatial adjustments and referencing in-image elements, while scribbles were favored for specifying attributes such as shape, size, and position, often combined with other methods. Text prompts excelled at providing detailed descriptions or when designers sought greater GenAI creativity. However, designers expressed concerns about AI misinterpreting annotations and scribbles and the effort needed to create effective text prompts. These insights inform GenAI interface design to better support refinement tasks, align with workflows, and enhance communication with AI systems.

cs.HC↗