Generative AI (GenAI) image tools are increasingly integrated into design workflows, prompting HCI research on their interaction methods and interfaces. We reviewed 37 such tools, including 28 HCI research systems and nine commercial systems (2022–July 2025), using three analytical frameworks: interaction methods, creative processes, and tool functionalities. We found that text prompts remain the dominant input method, while visual and attribute-based inputs—particularly in academic tools—are gaining traction and are often combined with text for refinement. Commercial systems emphasize parameter control, whereas academic tools focus on semantic attributes and visual organization. Most tools support ideation and exploration, but provide limited support for refinement and evaluation. Based on these findings, we identify nine design opportunities, including advanced visual interaction, simplified parameter control, precision editing, direct manipulation, workflow integration, default settings that support rapid exploration, and user guidance for later stages. We contribute a framework for analyzing GenAI interfaces and actionable directions for designing more usable, creativity-supportive GenAI image systems.

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.

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.

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.

We investigate how path context, encompassing both comfort and attractiveness, shapes user experiences in outdoor locative storytelling using Augmented Reality (AR). Addressing a research gap that predominantly concentrates on indoor settings or narrative backdrops, our user-focused research delves into the interplay between perceived path context and locative AR storytelling on routes with diverse walkability levels. We examine the correlation and causation between narrative engagement, spatial presence, perceived workload, and perceived path context. Our findings show that on paths with reasonable path walkability, attractive elements positively influence the narrative experience. However, even in environments with assured narrative walkability, inappropriate safety elements can divert user attention to mobility, hindering the integration of real-world features into the narrative. These results carry significant implications for path creation in outdoor locative AR storytelling, underscoring the importance of ensuring comfort and maintaining a balance between comfort and attractiveness to enrich the outdoor AR storytelling experience.

Augmented Reality (AR) is regarded as an innovative storytelling medium that presents novel experiences by layering a virtual narrative space over a real 3D space. However, understanding of how the virtual narrative space and the real space are connected with one another in the design of augmented narrative spaces has been limited. For this, we conducted a systematic literature review of 64 articles featuring AR storytelling applications and systems in HCI, AR, and MR research. We investigated how virtual narrative spaces have been paired, functionalized, placed, and registered in relation to the real spaces they target. Based on these connections, we identified eight dominant types of augmented narrative spaces that are primarily categorized by whether they virtually narrativize reality or realize the virtual narrative. We discuss our findings to propose design recommendations on how virtual-real connections can be incorporated into a more structured approach to AR storytelling.

We propose a novel crowd data-driven optimization approach for artwork placement in virtual exhibitions. With the emerging concept of Metaverse, a multitude of users can engage with content contemporaneously in virtual exhibitions. Yet, few studies have suggested a method to resolve crowd density concentration in multiuser Mixed Reality (MR) and Virtual Reality (VR) environments. In this study, our approach leverages crowd data engaged with artworks to predict optimal placement of artworks to distribute crowd density in virtual exhibitions, prior to exhibition planning. To investigate the requirement and validity of our approach, we conducted focus group interviews and an artwork relocation experiment as preliminary studies. In the generation of solution scenes for optimal placement, our optimizer adaptively scrutinizes placeable areas with considerations of crowd density distribution, scene rationality, and artwork similarity. Through a performance comparison analysis between optimization results, we confirmed that the optimizer successfully fulfilled the intended objectives with respect to the design considerations, resolving practical scenarios in exhibition planning.

Prior research explored ways to alert virtual reality users of bystanders entering the play area from afar. However, in confined social settings like sharing a couch with seatmates, bystanders' proxemic cues, such as distance, are limited during interruptions, posing challenges for proxemic-aware systems. To address this, we investigated three visualizations, using a 2D animoji, a fully-rendered avatar, and their combination, to gradually share bystanders' orientation and location during interruptions. In a user study (N=22), participants played virtual reality games while responding to questions from their seatmates. We found that the avatar preserved game experiences yet did not support the fast identification of seatmates as the animoji did. Instead, users preferred the mixed visualization, where they found the seatmate's orientation cues instantly in their view and were gradually guided to the person's actual location. We discuss implications for fine-grained proxemic-aware virtual reality systems to support interaction in constrained social spaces.

Most research on providing location-based content in 3D interactive virtual reality has been limited to social media content. Few studies have suggested how to represent the video clip of movies or TV shows in virtual reality. This paper investigates a video content representation method to provide a hyper-reality experience of the narrative world in virtual reality. We reflect the time and place settings of the video content in virtual reality and have participants watch the video in four different virtual reality environments. We reveal that reflecting the story's environment settings to the virtual reality environment significantly improves the spatial presence and narratives engagement. We also confirm a positive correlation between spatial presence and narrative engagement, including subscales such as emotional engagement and narrative presence. Based on the study results, we discuss how to provide the hyper-reality experience in content-adaptive virtual reality.