cs.HC

arXiv:2503.10264v1 Announce Type: new Abstract: While peer review enhances writing and research quality, harsh feedback can frustrate and demotivate authors. Hence, it is essential to explore how critiques should be delivered to motivate authors and enable them to keep iterating their work. In this study, we explored the impact of appending an automatically generated positive summary to the peer reviews of a writing task, alongside varying levels of overall evaluations (high vs. low), on authors' feedback reception, revision outcomes, and motivation to revise. Through a 2x2 online experiment with 137 participants, we found that adding an AI-reframed positive summary to otherwise harsh feedback increased authors' critique acceptance, whereas low overall evaluations of their work led to increased revision efforts. We discuss the implications of using AI in peer feedback, focusing on how AI-driven critiques can influence critique acceptance and support research communities in fostering productive and friendly peer feedback practices.

arXiv:2503.09885v1 Announce Type: cross Abstract: Analyzing CT scans, MRIs and X-rays is pivotal in diagnosing and treating diseases. However, detecting and identifying abnormalities from such medical images is a time-intensive process that requires expert analysis and is prone to interobserver variability. To mitigate such issues, machine learning-based models have been introduced to automate and significantly reduce the cost of image segmentation. Despite significant advances in medical image analysis in recent years, many of the latest models are never applied in clinical settings because state-of-the-art models do not easily interface with existing medical image viewers. To address these limitations, we propose QuickDraw, an open-source framework for medical image visualization and analysis that allows users to upload DICOM images and run off-the-shelf models to generate 3D segmentation masks. In addition, our tool allows users to edit, export, and evaluate segmentation masks to iteratively improve state-of-the-art models through active learning. In this paper, we detail the design of our tool and present survey results that highlight the usability of our software. Notably, we find that QuickDraw reduces the time to manually segment a CT scan from four hours to six minutes and reduces machine learning-assisted segmentation time by 10\% compared to prior work. Our code and documentation are available at https://github.com/qd-seg/quickdraw

arXiv:2503.10254v1 Announce Type: new Abstract: In the rapidly evolving world of software development, the surge in developers' reliance on AI-driven tools has transformed Integrated Development Environments into powerhouses of advanced features. This transformation, while boosting developers' productivity to unprecedented levels, comes with a catch: increased hardware demands for software development. Moreover, the significant economic and environmental toll of using these sophisticated models necessitates mechanisms that reduce unnecessary computational burdens. We propose HyperSeq - Hyper-Adaptive Representation for Predictive Sequencing of States - a novel, resource-efficient approach designed to model developers' cognitive states. HyperSeq facilitates precise action sequencing and enables real-time learning of user behavior. Our preliminary results show how HyperSeq excels in forecasting action sequences and achieves remarkable prediction accuracies that go beyond 70%. Notably, the model's online-learning capability allows it to substantially enhance its predictive accuracy in a majority of cases and increases its capability in forecasting next user actions with sufficient iterations for adaptation. Ultimately, our objective is to harness these predictions to refine and elevate the user experience dynamically within the IDE.

arXiv:2503.09874v1 Announce Type: new Abstract: Studying collaborative behavior in Mixed Reality (MR) often requires extensive, challenging data collection. This paper introduces MoCoMR, a novel simulator designed to address this by generating synthetic yet realistic collaborative MR data. MoCoMR captures individual behavioral modalities such as speaking, gaze, and locomotion during a collaborative image-sorting task with 48 participants to identify distinct behavioral patterns. MoCoMR simulates individual actions and interactions within a virtual space, enabling researchers to investigate the impact of individual behaviors on group dynamics and task performance. This simulator facilitates the development of more effective and human-centered MR applications by providing insights into user behavior and interaction patterns. The simulator's API allows for flexible configuration and data analysis, enabling researchers to explore various scenarios and generate valuable insights for optimizing collaborative MR experiences.

arXiv:2503.10488v1 Announce Type: new Abstract: Generating co-speech gestures in real time requires both temporal coherence and efficient sampling. We introduce Accelerated Rolling Diffusion, a novel framework for streaming gesture generation that extends rolling diffusion models with structured progressive noise scheduling, enabling seamless long-sequence motion synthesis while preserving realism and diversity. We further propose Rolling Diffusion Ladder Acceleration (RDLA), a new approach that restructures the noise schedule into a stepwise ladder, allowing multiple frames to be denoised simultaneously. This significantly improves sampling efficiency while maintaining motion consistency, achieving up to a 2x speedup with high visual fidelity and temporal coherence. We evaluate our approach on ZEGGS and BEAT, strong benchmarks for real-world applicability. Our framework is universally applicable to any diffusion-based gesture generation model, transforming it into a streaming approach. Applied to three state-of-the-art methods, it consistently outperforms them, demonstrating its effectiveness as a generalizable and efficient solution for real-time, high-fidelity co-speech gesture synthesis.

arXiv:2404.16487v2 Announce Type: replace Abstract: This study investigates the feasibility of remote virtual reality (VR) studies conducted at home using VR headsets and video conferencing by deploying an experiment on emotion ratings. 20 participants used head-mounted displays to immerse themselves in 360{\deg} videos selected to evoke emotional responses. The research compares continuous ratings using a graphical interface to retrospective questionnaires on a digitized Likert Scale for measuring arousal and valence, both based on the self-assessment manikin (SAM). It was hypothesized that the two different rating methods would lead to significantly different values for both valence and arousal. The goal was to investigate whether continuous ratings during the experience would better reflect users' emotions compared to the post-questionnaire by mitigating biases such as the peak-end rule. The results show significant differences with moderate to strong effect sizes for valence and no significant differences for arousal with low to moderate effect sizes. This indicates the need for further investigation of the methods used to assess emotion ratings in VR studies. Overall, this study is an example of a remotely conducted VR experiment, offering insights into methods for emotion elicitation in VR by varying the timing and interface of the rating.

arXiv:2503.09824v1 Announce Type: new Abstract: As we progress toward Society 5.0's vision of a human-centered digital society, ensuring digital accessibility becomes increasingly critical, particularly for citizens with visual impairments and other disabilities. This paper examines the implementation challenges of accessible digital public services within Swiss public administration. Through Design Science Research, we investigate the gap between accessibility legislation and practical implementation, analyzing how current standards translate into real-world usability. Our research reveals significant barriers including resource constraints, fragmented policy enforcement, and limited technical expertise. To address these challenges, we present the Inclusive Public Administration Framework, which integrates Web Content Accessibility Guidelines with the HERMES project management methodology. This framework provides a structured approach to embedding accessibility considerations throughout digital service development. Our findings contribute to the discourse on digital inclusion in Society 5.0 by providing actionable strategies for implementing accessible public services. As we move towards a more integrated human-machine society, ensuring digital accessibility for visually impaired citizens is crucial for building an equitable and inclusive digital future.

arXiv:2503.10445v1 Announce Type: new Abstract: Reducing the spread of misinformation is challenging. AI-based fact verification systems offer a promising solution by addressing the high costs and slow pace of traditional fact-checking. However, the problem of how to effectively communicate the results to users remains unsolved. Warning labels may seem an easy solution, but they fail to account for fuzzy misinformation that is not entirely fake. Additionally, users' limited attention spans and social media information should be taken into account while designing the presentation. The online experiment (n = 537) investigates the impact of sources and granularity on users' perception of information veracity and the system's usefulness and trustworthiness. Findings show that fine-grained indicators enhance nuanced opinions, information awareness, and the intention to use fact-checking systems. Source differences had minimal impact on opinions and perceptions, except for informativeness. Qualitative findings suggest the proposed indicators promote critical thinking. We discuss implications for designing concise, user-friendly AI fact-checking feedback.

arXiv:2503.09838v1 Announce Type: new Abstract: We present BioSpark, a system for analogical innovation designed to act as a creativity partner in reducing the cognitive effort in finding, mapping, and creatively adapting diverse inspirations. While prior approaches have focused on initial stages of finding inspirations, BioSpark uses LLMs embedded in a familiar, visual, Pinterest-like interface to go beyond inspiration to supporting users in identifying the key solution mechanisms, transferring them to the problem domain, considering tradeoffs, and elaborating on details and characteristics. To accomplish this BioSpark introduces several novel contributions, including a tree-of-life enabled approach for generating relevant and diverse inspirations, as well as AI-powered cards including 'Sparks' for analogical transfer; 'Trade-offs' for considering pros and cons; and 'Q&A' for deeper elaboration. We evaluated BioSpark through workshops with professional designers and a controlled user study, finding that using BioSpark led to a greater number of generated ideas; those ideas being rated higher in creative quality; and more diversity in terms of biological inspirations used than a control condition. Our results suggest new avenues for creativity support tools embedding AI in familiar interaction paradigms for designer workflows.

arXiv:2503.09849v1 Announce Type: new Abstract: After-action reviews (AARs) are professional discussions that help operators and teams enhance their task performance by analyzing completed missions with peers and professionals. Previous studies that compared different formats of AARs have mainly focused on human teams. However, the inclusion of robotic teammates brings along new challenges in understanding teammate intent and communication. Traditional AAR between human teammates may not be satisfactory for human-robot teams. To address this limitation, we propose a new training review (TR) tool, called the Virtual Spectator Interface (VSI), to enhance human-robot team performance and situational awareness (SA) in a simulated search mission. The proposed VSI primarily utilizes visual feedback to review subjects' behavior. To examine the effectiveness of VSI, we took elements from AAR to conduct our own TR, designed a 1 x 3 between-subjects experiment with experimental conditions: TR with (1) VSI, (2) screen recording, and (3) non-technology (only verbal descriptions). The results of our experiments demonstrated that the VSI did not result in significantly better team performance than other conditions. However, the TR with VSI led to more improvement in the subjects SA over the other conditions.

arXiv:2503.09987v1 Announce Type: new Abstract: As human augmentation technologies evolve, the convergence of AI, robotics, and extended reality (XR) is redefining human potential -- enhancing cognition, perception, and physical abilities. However, these advancements also introduce ethical dilemmas, security risks, and concerns over loss of control. This workshop explores both the transformative potential and the unintended consequences of augmentation technologies. Bringing together experts from HCI, neuroscience, robotics, and ethics, we will examine real-world applications, emerging risks, and governance strategies for responsible augmentation. The session will feature keynote talks and interactive discussions, addressing topics such as AI-enhanced cognition, wearable robotics, neural interfaces, and XR-driven augmentation. By fostering multidisciplinary dialogue, this workshop aims to generate actionable insights for responsible innovation, proposing ethical frameworks to balance human empowerment with risk mitigation. We invite researchers, practitioners, and industry leaders to contribute their perspectives and help shape the future of human augmentation.

arXiv:2503.10241v1 Announce Type: new Abstract: Multimodal information-gathering settings, where users collaborate with AI in dynamic environments, are increasingly common. These involve complex processes with textual and multimodal interactions, often requiring additional structural information via cost-incurring requests. AI helpers lack access to users' true goals, beliefs, and preferences and struggle to integrate diverse information effectively. We propose a social continual learning framework for causal knowledge acquisition and collaborative decision-making. It focuses on autonomous agents learning through dialogues, question-asking, and interaction in open, partially observable environments. A key component is a natural language oracle that answers the agent's queries about environmental mechanisms and states, refining causal understanding while balancing exploration or learning, and exploitation or knowledge use. Evaluation tasks inspired by developmental psychology emphasize causal reasoning and question-asking skills. They complement benchmarks by assessing the agent's ability to identify knowledge gaps, generate meaningful queries, and incrementally update reasoning. The framework also evaluates how knowledge acquisition costs are amortized across tasks within the same environment. We propose two architectures: 1) a system combining Large Language Models (LLMs) with the ReAct framework and question-generation, and 2) an advanced system with a causal world model, symbolic, graph-based, or subsymbolic, for reasoning and decision-making. The latter builds a causal knowledge graph for efficient inference and adaptability under constraints. Challenges include integrating causal reasoning into ReAct and optimizing exploration and question-asking in error-prone scenarios. Beyond applications, this framework models developmental processes combining causal reasoning, question generation, and social learning.

arXiv:2503.10029v1 Announce Type: new Abstract: Hand interactions are increasingly used as the primary input modality in immersive environments, but they are not always feasible due to situational impairments, motor limitations, and environmental constraints. Speech interfaces have been explored as an alternative to hand input in research and commercial solutions, but are limited to initiating basic hand gestures and system controls. We introduce HandProxy, a system that expands the affordances of speech interfaces to support expressive hand interactions. Instead of relying on predefined speech commands directly mapped to possible interactions, HandProxy enables users to control the movement of a virtual hand as an interaction proxy, allowing them to describe the intended interactions naturally while the system translates speech into a sequence of hand controls for real-time execution. A user study with 20 participants demonstrated that HandProxy effectively enabled diverse hand interactions in virtual environments, achieving a 100% task completion rate with an average of 1.09 attempts per speech command and 91.8% command execution accuracy, while supporting flexible, natural speech input with varying levels of control and granularity.

arXiv:2503.10116v1 Announce Type: new Abstract: The knowledge transfer from 3D printing technology paved the way for unlocking the innovative potential of 3D Food Printing (3DFP) technology. However, this technology-oriented approach neglects userderived issues that could be addressed with advancements in 3DFP technology. To explore potential new features and application areas for 3DFP technology, we created the Mobile Food Printer (MFP) prototype. We collected insights from novice chefs for MFP in the restaurant context through four online focus group sessions (N=12). Our results revealed how MFP can be applied in the current kitchen routines (preparation, serving, and eating) and introduce novel dining experiences. We discuss our learnings under two themes: 1) dealing with the kitchen rush and 2) streamlining workflows in the kitchen. The opportunities we present in this study act as a starting point for HCI and HFI researchers and encourage them to implement mobility in 3DFP with a useroriented lens. We further provide a ground for future research to uncover potentials for advancing 3DFP technology.

arXiv:2503.09794v1 Announce Type: new Abstract: As Augmented Reality (AR) and Artificial Intelligence (AI) continue to converge, new opportunities emerge for AI agents to actively support human collaboration in immersive environments. While prior research has primarily focused on dyadic human-AI interactions, less attention has been given to Human-AI Teams (HATs) in AR, where AI acts as an adaptive teammate rather than a static tool. This position paper takes the perspective of team dynamics and work organization to propose that AI agents in AR should not only interact with individuals but also recognize and respond to team-level needs in real time. We argue that spatially aware AI agents should dynamically generate the resources necessary for effective collaboration, such as virtual blackboards for brainstorming, mental map models for shared understanding, and memory recall of spatial configurations to enhance knowledge retention and task coordination. This approach moves beyond predefined AI assistance toward context-driven AI interventions that optimize team performance and decision-making.

arXiv:2503.09639v1 Announce Type: new Abstract: Can we simulate a sandbox society with generative agents to model human behavior, thereby reducing the over-reliance on real human trials for assessing public policies? In this work, we investigate the feasibility of simulating health-related decision-making, using vaccine hesitancy, defined as the delay in acceptance or refusal of vaccines despite the availability of vaccination services (MacDonald, 2015), as a case study. To this end, we introduce the VacSim framework with 100 generative agents powered by Large Language Models (LLMs). VacSim simulates vaccine policy outcomes with the following steps: 1) instantiate a population of agents with demographics based on census data; 2) connect the agents via a social network and model vaccine attitudes as a function of social dynamics and disease-related information; 3) design and evaluate various public health interventions aimed at mitigating vaccine hesitancy. To align with real-world results, we also introduce simulation warmup and attitude modulation to adjust agents' attitudes. We propose a series of evaluations to assess the reliability of various LLM simulations. Experiments indicate that models like Llama and Qwen can simulate aspects of human behavior but also highlight real-world alignment challenges, such as inconsistent responses with demographic profiles. This early exploration of LLM-driven simulations is not meant to serve as definitive policy guidance; instead, it serves as a call for action to examine social simulation for policy development.

arXiv:2503.09805v1 Announce Type: new Abstract: Queer people are often discussed as targets of bias, harm, or discrimination in research on generative AI. However, the specific ways that queer people engage with generative AI, and thus possible uses that support queer people, have yet to be explored. We conducted a workshop study with 13 queer artists, during which we gave participants access to GPT-4 and DALL-E 3 and facilitated group sensemaking activities. We found our participants struggled to use these models due to various normative values embedded in their designs, such as hyper-positivity and anti-sexuality. We describe various strategies our participants developed to overcome these models' limitations and how, nevertheless, our participants found value in these highly-normative technologies. Drawing on queer feminist theory, we discuss implications for the conceptualization of "state-of-the-art" models and consider how FAccT researchers might support queer alternatives.

arXiv:2503.09901v1 Announce Type: new Abstract: Generative AI (GAI) technologies are disrupting professional writing, challenging traditional practices. Recent studies explore GAI adoption experiences of creative practitioners, but we know little about how these experiences evolve into established practices and how GAI resistance alters these practices. To address this gap, we conducted 25 semi-structured interviews with writing professionals who adopted and/or resisted GAI. Using the theoretical lens of Job Crafting, we identify four strategies professionals employ to reshape their roles. Writing professionals employed GAI resisting strategies to maximize human potential, reinforce professional identity, carve out a professional niche, and preserve credibility within their networks. In contrast, GAI-enabled strategies allowed writers who embraced GAI to enhance desirable workflows, minimize mundane tasks, and engage in new AI-managerial labor. These strategies amplified their collaborations with GAI while reducing their reliance on other people. We conclude by discussing implications of GAI practices on writers' identity and practices as well as crafting theory.

arXiv:2503.09832v1 Announce Type: new Abstract: Massively multiplayer online games (MMOGs) can foster social interaction and relationship formation, but they pose specific privacy and safety challenges, especially in the context of mediating intimate interpersonal connections. To explore the potential risks, we conducted a case study on Final Fantasy XIV (FFXIV) players intimate partner seeking posts on social media. We analyzed 1,288 posts from a public Weibo account using Latent Dirichlet Allocation (LDA) topic modeling and thematic analysis. Our findings reveal that players disclose sensitive personal information and share vulnerabilities to establish trust but face difficulties in managing identity and privacy across multiple platforms. We also found that players expectations regarding intimate partner are diversified, and mismatch of expectations may leads to issues like privacy leakage or emotional exploitation. Based on our findings, we propose design implications for reducing privacy and safety risks and fostering healthier social interactions in virtual worlds.

arXiv:2305.08982v2 Announce Type: replace Abstract: Millions of users come to online peer counseling platforms to seek support. However, studies show that online peer support groups are not always as effective as expected, largely due to users' negative experiences with unhelpful counselors. Peer counselors are key to the success of online peer counseling platforms, but most often do not receive appropriate training.Hence, we introduce CARE: an AI-based tool to empower and train peer counselors through practice and feedback. Concretely, CARE helps diagnose which counseling strategies are needed in a given situation and suggests example responses to counselors during their practice sessions. Building upon the Motivational Interviewing framework, CARE utilizes large-scale counseling conversation data with text generation techniques to enable these functionalities. We demonstrate the efficacy of CARE by performing quantitative evaluations and qualitative user studies through simulated chats and semi-structured interviews, finding that CARE especially helps novice counselors in challenging situations. The code is available at https://github.com/SALT-NLP/CARE