Human-Computer Interaction
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- [1] arXiv:2512.19707 [pdf, other]
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Title: Bidirectional human-AI collaboration in brain tumour assessments improves both expert human and AI agent performanceJames K Ruffle, Samia Mohinta, Guilherme Pombo, Asthik Biswas, Alan Campbell, Indran Davagnanam, David Doig, Ahmed Hamman, Harpreet Hyare, Farrah Jabeen, Emma Lim, Dermot Mallon, Stephanie Owen, Sophie Wilkinson, Sebastian Brandner, Parashkev NachevComments: 38 pages, 6 figures, 7 supplementary figuresSubjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
The benefits of artificial intelligence (AI) human partnerships-evaluating how AI agents enhance expert human performance-are increasingly studied. Though rarely evaluated in healthcare, an inverse approach is possible: AI benefiting from the support of an expert human agent. Here, we investigate both human-AI clinical partnership paradigms in the magnetic resonance imaging-guided characterisation of patients with brain tumours. We reveal that human-AI partnerships improve accuracy and metacognitive ability not only for radiologists supported by AI, but also for AI agents supported by radiologists. Moreover, the greatest patient benefit was evident with an AI agent supported by a human one. Synergistic improvements in agent accuracy, metacognitive performance, and inter-rater agreement suggest that AI can create more capable, confident, and consistent clinical agents, whether human or model-based. Our work suggests that the maximal value of AI in healthcare could emerge not from replacing human intelligence, but from AI agents that routinely leverage and amplify it.
- [2] arXiv:2512.19810 [pdf, html, other]
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Title: Predicting Student Actions in a Procedural Training EnvironmentComments: 12 pages. Author Accepted Manuscript (AAM). \c{opyright} 2017 IEEE. Final published version: this https URLJournal-ref: IEEE Transactions on Learning Technologies, vol. 10, no. 4, pp. 463-474, 2017Subjects: Human-Computer Interaction (cs.HC)
Data mining is known to have a potential for predicting user performance. However, there are few studies that explore its potential for predicting student behavior in a procedural training environment. This paper presents a collective student model, which is built from past student logs. These logs are firstly grouped into clusters. Then an extended automaton is created for each cluster based on the sequences of events found in the cluster logs. The main objective of this model is to predict the actions of new students for improving the tutoring feedback provided by an intelligent tutoring system. The proposed model has been validated using student logs collected in a 3D virtual laboratory for teaching biotechnology. As a result of this validation, we concluded that the model can provide reasonably good predictions and can support tutoring feedback that is better adapted to each student type.
- [3] arXiv:2512.19832 [pdf, html, other]
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Title: How Tech Workers Contend with Hazards of Humanlikeness in Generative AISubjects: Human-Computer Interaction (cs.HC)
Generative AI's humanlike qualities are driving its rapid adoption in professional domains. However, this anthropomorphic appeal raises concerns from HCI and responsible AI scholars about potential hazards and harms, such as overtrust in system outputs. To investigate how technology workers navigate these humanlike qualities and anticipate emergent harms, we conducted focus groups with 30 professionals across six job functions (ML engineering, product policy, UX research and design, product management, technology writing, and communications). Our findings reveal an unsettled knowledge environment surrounding humanlike generative AI, where workers' varying perspectives illuminate a range of potential risks for individuals, knowledge work fields, and society. We argue that workers require comprehensive support, including clearer conceptions of ``humanlikeness'' to effectively mitigate these risks. To aid in mitigation strategies, we provide a conceptual map articulating the identified hazards and their connection to conflated notions of ``humanlikeness.''
- [4] arXiv:2512.19885 [pdf, html, other]
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Title: Visualizing a Collective Student Model for Procedural Training EnvironmentsComments: Preprint (not peer-reviewed). Version of Record: this https URLJournal-ref: Multimedia Tools and Applications, vol. 78, pp. 10983-11010, 2019Subjects: Human-Computer Interaction (cs.HC)
Visualization plays a relevant role for discovering patterns in big sets of data. In fact, the most common way to help a human with a pattern interpretation is through a graphic. In 2D/3D virtual environments for procedural training the student interaction is more varied and complex than in traditional e-learning environments. Therefore, the visualization and interpretation of students' behaviors becomes a challenge. This motivated us to design the visualization of a collective student model built from student logs taken from 2D/3D virtual environments for procedural training. This paper presents the design decisions that enable a suitable visualization of this model to instructors as well as a web tool that implements this visualization and is intended: to help instructors to improve their own teaching; and to enhance the tutoring strategy of an Intelligent Tutoring System. Then, this paper illustrates, with three detailed examples, how this tool can be used to those educational purposes. Next, the paper presents an experiment for validating the utility of the tool. In this experiment we show how the tool can help to modify the tutoring strategy of a 3D virtual laboratory. In this way, it is shown that the proposed visualization of the model can serve to improve the performance of students in 2D/3D virtual environments for procedural training.
- [5] arXiv:2512.19898 [pdf, html, other]
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Title: Free-Will vs Free-Wheel: Understanding Community Accessibility Requirements of Wheelchair Users through Interviews, Participatory Action, and ModelingSubjects: Human-Computer Interaction (cs.HC)
Community participation is an important aspect of an individuals physical and mental well-being. This participation is often limited for persons with disabilities, especially those with ambulatory impairments due to the inability to optimally navigate the community. Accessibility is a multi-faceted problem and varies from person to person. Moreover, it depends on various personal and environmental factors. Despite significant research conducted to understand challenges faced by wheelchair users, developing an accessibility model for wheelchair users by identifying various characteristic features has not been thoroughly studied. In this research, we propose a three-dimensional model of accessibility and validate it through in-depth qualitative analysis involving semi-structured interviews and participatory action research. The outcomes of our studies validated many of our hypotheses about community access for wheelchair users and identified a need for more accessible path planning tools and resources. Overall, this research strengthened our three-dimensional User-Wheelchair-Environment model of accessibility.
- [6] arXiv:2512.19899 [pdf, html, other]
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Title: Detecting cyberbullying in Spanish texts through deep learning techniquesComments: Preprint (Author's Original Manuscript, AOM). Published version: this https URLJournal-ref: International Journal of Data Mining, Modelling and Management, vol. 14, no. 3, pp. 234-247, 2022Subjects: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
Recent recollected data suggests that it is possible to automatically detect events that may negatively affect the most vulnerable parts of our society, by using any communication technology like social networks or messaging applications. This research consolidates and prepares a corpus with Spanish bullying expressions taken from Twitter in order to use them as an input to train a convolutional neuronal network through deep learning techniques. As a result of this training, a predictive model was created, which can identify Spanish cyberbullying expressions such as insults, racism, homophobic attacks, and so on.
- [7] arXiv:2512.19926 [pdf, other]
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Title: Developers' Experience with Generative AI -- First Insights from an Empirical Mixed-Methods Field StudySubjects: Human-Computer Interaction (cs.HC)
With the rise of AI-powered coding assistants, firms and programmers are exploring how to optimize their interaction with them. Research has so far mainly focused on evaluating output quality and productivity gains, leaving aside the developers' experience during the interaction. In this study, we take a multimodal, developer-centered approach to gain insights into how professional developers experience the interaction with Generative AI (GenAI) in their natural work environment in a firm. The aim of this paper is (1) to demonstrate a feasible mixed-method study design with controlled and uncontrolled study periods within a firm setting, (2) to give first insights from complementary behavioral and subjective experience data on developers' interaction with GitHub Copilot and (3) to compare the impact of interaction types (no Copilot use, in-code suggestions, chat prompts or both in-code suggestions and chat prompts) on efficiency, accuracy and perceived workload whilst working on different task categories. Results of the controlled sessions in this study indicate that moderate use of either in-code suggestions or chat prompts improves efficiency (task duration) and reduces perceived workload compared to not using Copilot, while excessive or combined use lessens these benefits. Accuracy (task completion) profits from chat interaction. In general, subjective perception of workload aligns with objective behavioral data in this study. During the uncontrolled period of the study, both higher cognitive load and productivity were perceived when interacting with AI during everyday working tasks. This study motivates the use of comparable study designs, in e.g. workshop or hackathon settings, to evaluate GenAI tools holistically and realistically with a focus on the developers' experience.
- [8] arXiv:2512.19999 [pdf, html, other]
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Title: Stories That Teach: Eastern Wisdom for Human-AI Creative PartnershipsComments: 4 pages, 1 figureSubjects: Human-Computer Interaction (cs.HC)
This workshop explores innovative human-AI collaboration methodologies in HCI visual storytelling education through our established "gap-and-fill" approach. Drawing on Eastern aesthetic philosophies of intentional emptiness, including Chinese negative-space traditions, Japanese "ma" concepts, and contemporary design minimalism, we demonstrate how educators can teach students to maintain creative agency while strategically leveraging AI assistance. During this workshop, participants will experience a structured three-phase methodology: creating a human-led narrative foundation, identifying strategic gaps, and collaborating on AI enhancements. The workshop combines theoretical foundations with intensive hands-on practice, enabling participants to create compelling HCI visual narratives that demonstrate effective human-AI partnership. Through sequential art techniques, storyboarding exercises, and guided AI integration, attendees learn to communicate complex interactive concepts, accessibility solutions, and user experience flows while preserving narrative coherence and creative vision. Building on our successful workshops at ACM C&C 2025, this session specifically addresses the needs of the Chinese HCI community for culturally informed and pedagogically sound approaches to AI integration in creative education.
- [9] arXiv:2512.20116 [pdf, html, other]
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Title: /UnmuteAll: Modeling Verbal Communication Patterns of Collaborative Contexts in MOBA GamesSubjects: Human-Computer Interaction (cs.HC)
Team communication plays a vital role in supporting collaboration in multiplayer online games. Therefore, numerous studies were conducted to examine communication patterns in esports teams. While non-verbal communication has been extensively investigated, research on assessing voice-based verbal communication patterns remains relatively understudied. In this study, we propose a framework that automatically assesses verbal communication patterns by constructing networks with utterances transcribed from voice recordings. Through a data collection study, we obtained 84 game sessions from five League of Legends teams and subsequently investigated how verbal communication patterns varied across different conditions. As a result, we revealed that esports players exhibited broader and more balanced participation in collaborative situations, increased utterances over time with the largest rise in decision making, and team-level differences that were contingent on effective professional training. Building upon these findings, this study provides a generalizable tool for analyzing effective team communication.
- [10] arXiv:2512.20129 [pdf, html, other]
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Title: Dreamcrafter: Immersive Editing of 3D Radiance Fields Through Flexible, Generative Inputs and OutputsComments: CHI 2025, Project page: this https URLSubjects: Human-Computer Interaction (cs.HC); Computer Vision and Pattern Recognition (cs.CV)
Authoring 3D scenes is a central task for spatial computing applications. Competing visions for lowering existing barriers are (1) focus on immersive, direct manipulation of 3D content or (2) leverage AI techniques that capture real scenes (3D Radiance Fields such as, NeRFs, 3D Gaussian Splatting) and modify them at a higher level of abstraction, at the cost of high latency. We unify the complementary strengths of these approaches and investigate how to integrate generative AI advances into real-time, immersive 3D Radiance Field editing. We introduce Dreamcrafter, a VR-based 3D scene editing system that: (1) provides a modular architecture to integrate generative AI algorithms; (2) combines different levels of control for creating objects, including natural language and direct manipulation; and (3) introduces proxy representations that support interaction during high-latency operations. We contribute empirical findings on control preferences and discuss how generative AI interfaces beyond text input enhance creativity in scene editing and world building.
- [11] arXiv:2512.20179 [pdf, html, other]
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Title: RESPOND: Risk-Enhanced Structured Pattern for LLM-driven Online Node-level Decision-makingComments: 28 pages, 8 figuresSubjects: Human-Computer Interaction (cs.HC)
Current LLM-based driving agents that rely on unstructured plain-text memory suffer from low-precision scene retrieval and inefficient reflection. To address this limitation, we present RESPOND, a structured decision-making framework for LLM-driven agents grounded in explicit risk patterns. RESPOND represents each ego-centric scene using a unified 5 by 3 matrix that encodes spatial topology and road constraints, enabling consistent and reliable retrieval of spatial risk configurations. Based on this representation, a hybrid rule and LLM decision pipeline is developed with a two-tier memory mechanism. In high-risk contexts, exact pattern matching enables rapid and safe reuse of verified actions, while in low-risk contexts, sub-pattern matching supports personalized driving style adaptation. In addition, a pattern-aware reflection mechanism abstracts tactical corrections from crash and near-miss frames to update structured memory, achieving one-crash-to-generalize learning. Extensive experiments demonstrate the effectiveness of RESPOND. In highway-env, RESPOND outperforms state-of-the-art LLM-based and reinforcement learning based driving agents while producing substantially fewer collisions. With step-wise human feedback, the agent acquires a Sporty driving style within approximately 20 decision steps through sub-pattern abstraction. For real-world validation, RESPOND is evaluated on 53 high-risk cut-in scenarios extracted from the HighD dataset. For each event, intervention is applied immediately before the cut-in and RESPOND re-decides the driving action. Compared to recorded human behavior, RESPOND reduces subsequent risk in 84.9 percent of scenarios, demonstrating its practical feasibility under real-world driving conditions. These results highlight RESPONDs potential for autonomous driving, personalized driving assistance, and proactive hazard mitigation.
- [12] arXiv:2512.20181 [pdf, html, other]
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Title: Competing or Collaborating? The Role of Hackathon Formats in Shaping Team Dynamics and Project ChoicesSubjects: Human-Computer Interaction (cs.HC)
Hackathons have emerged as dynamic platforms for fostering innovation, collaboration, and skill development in the technology sector. Structural differences across hackathon formats raise important questions about how event design can shape student learning experiences and engagement. This study examines two distinct hackathon formats: a gender-specific hackathon (GS) and a regular institutional hackathon (RI). Using a mixed-methods approach, we analyze variations in team dynamics, project themes, role assignments, and environmental settings. Our findings indicate that GS hackathon foster a collaborative and supportive atmosphere, emphasizing personal growth and community learning, with projects often centered on health and well-being. In contrast, RI hackathon tend to promote a competitive, outcome-driven environment, with projects frequently addressing entertainment and environmental sustainability. Based on these insights, we propose a hybrid hackathon model that combines the strengths of both formats to balance competition with inclusivity. This work contributes to the design of more engaging, equitable, and pedagogically effective hackathon experiences.
- [13] arXiv:2512.20221 [pdf, other]
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Title: The Effect of Empathic Expression Levels in Virtual Human Interaction: A Controlled ExperimentSubjects: Human-Computer Interaction (cs.HC)
As artificial intelligence (AI) systems become increasingly embedded in everyday life, the ability of interactive agents to express empathy has become critical for effective human-AI interaction, particularly in emotionally sensitive contexts. Rather than treating empathy as a binary capability, this study examines how different levels of empathic expression in virtual human interaction influence user experience. We conducted a between-subject experiment (n = 70) in a counseling-style interaction context, comparing three virtual human conditions: a neutral dialogue-based agent, a dialogue-based empathic agent, and a video-based empathic agent that incorporates users' facial cues. Participants engaged in a 15-minute interaction and subsequently evaluated their experience using subjective measures of empathy and interaction quality. Results from analysis of variance (ANOVA) revealed significant differences across conditions in affective empathy, perceived naturalness of facial movement, and appropriateness of facial expression. The video-based empathic expression condition elicited significantly higher affective empathy than the neutral baseline (p < .001) and marginally higher levels than the dialogue-based condition (p < .10). In contrast, cognitive empathy did not differ significantly across conditions. These findings indicate that empathic expression in virtual humans should be conceptualized as a graded design variable, rather than a binary capability, with visually grounded cues playing a decisive role in shaping affective user experience.
- [14] arXiv:2512.20306 [pdf, html, other]
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Title: Structured Visualization Design Knowledge for Grounding Generative Reasoning and Situated FeedbackSubjects: Human-Computer Interaction (cs.HC)
Automated visualization design navigates a tension between symbolic systems and generative models. Constraint solvers enforce structural and perceptual validity, but the rules they require are difficult to author and too rigid to capture situated design knowledge. Large language models require no formal rules and can reason about contextual nuance, but they prioritize popular conventions over empirically grounded best practices. We address this tension by proposing a cataloging scheme that structures visualization design knowledge as natural-language guidelines with semantically typed metadata. This allows experts to author knowledge that machines can query. An expert study ($N=18$) indicates that practitioners routinely adapt heuristics to situational factors such as audience and communicative intent. To capture this reasoning, guideline sections specify not only advice but also the contexts where it applies, exceptions that invalidate it, and the sources from which it derives. We demonstrate the scheme's expressiveness by cataloging 744 guidelines drawn from cognitive science, accessibility standards, data journalism, and research on rhetorical aspects of visual communication. We embed guideline sections in a vector space, opening the knowledge itself to structural analysis. This reveals conflicting advice across sources and transferable principles between domains. Rather than replacing constraint-based tools, our scheme provides what they lack: situated guidance that generative systems can retrieve to ground their reasoning, users can verify against cited sources, and experts can author as knowledge evolves.
- [15] arXiv:2512.20584 [pdf, other]
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Title: A human-centered approach to reframing job satisfaction in the BIM-enabled construction industrySubjects: Human-Computer Interaction (cs.HC)
As the construction industry undergoes rapid digital transformation, ensuring that new technologies enhance rather than hinder human experience has become essential. The inclusion of Building Information Modeling (BIM) plays a central role in this shift, yet its influence on job satisfaction remains underexplored. In response, this study developed a human-centered measurement model for evaluating job satisfaction in BIM work environments by adapting Hackman and Oldham's Job Characteristics Model for the architecture, engineering, and construction (AEC) industry to create a survey that captured industry perspectives on BIM use and job satisfaction. The model uses Partial Least Squares Structural Equation Modeling to analyze the survey results and identify what dimensions of BIM-related work affect job satisfaction. While it was hypothesized that BIM use increases job satisfaction, the results show that only some dimensions of BIM use positively impact BIM job satisfaction; the use of BIM does not guarantee an increase in overall job satisfaction. Additionally, more frequent BIM use was not associated with higher satisfaction levels. These findings suggest that in the AEC industry, sustainable job satisfaction depends less on technological autonomy and more on human-centric factors, particularly collaboration and meaningful engagement within digital workflows.
New submissions (showing 15 of 15 entries)
- [16] arXiv:2512.19713 (cross-list from cs.LG) [pdf, other]
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Title: Reducing Label Dependency in Human Activity Recognition with Wearables: From Supervised Learning to Novel Weakly Self-Supervised ApproachesJournal-ref: Sensors, 2025Subjects: Machine Learning (cs.LG); Human-Computer Interaction (cs.HC)
Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they demand extensive labeled datasets that are costly to obtain. Conversely, unsupervised methods eliminate labeling needs but often deliver suboptimal performance. This paper presents a comprehensive investigation across the supervision spectrum for wearable-based HAR, with particular focus on novel approaches that minimize labeling requirements while maintaining competitive accuracy. We develop and empirically compare: (1) traditional fully supervised learning, (2) basic unsupervised learning, (3) a weakly supervised learning approach with constraints, (4) a multi-task learning approach with knowledge sharing, (5) a self-supervised approach based on domain expertise, and (6) a novel weakly self-supervised learning framework that leverages domain knowledge and minimal labeled data. Experiments across benchmark datasets demonstrate that: (i) our weakly supervised methods achieve performance comparable to fully supervised approaches while significantly reducing supervision requirements; (ii) the proposed multi-task framework enhances performance through knowledge sharing between related tasks; (iii) our weakly self-supervised approach demonstrates remarkable efficiency with just 10\% of labeled data. These results not only highlight the complementary strengths of different learning paradigms, offering insights into tailoring HAR solutions based on the availability of labeled data, but also establish that our novel weakly self-supervised framework offers a promising solution for practical HAR applications where labeled data are limited.
- [17] arXiv:2512.19950 (cross-list from cs.CL) [pdf, html, other]
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Title: Bias Beneath the Tone: Empirical Characterisation of Tone Bias in LLM-Driven UX SystemsSubjects: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
Large Language Models are increasingly used in conversational systems such as digital personal assistants, shaping how people interact with technology through language. While their responses often sound fluent and natural, they can also carry subtle tone biases such as sounding overly polite, cheerful, or cautious even when neutrality is expected. These tendencies can influence how users perceive trust, empathy, and fairness in dialogue. In this study, we explore tone bias as a hidden behavioral trait of large language models. The novelty of this research lies in the integration of controllable large language model based dialogue synthesis with tone classification models, enabling robust and ethical emotion recognition in personal assistant interactions. We created two synthetic dialogue datasets, one generated from neutral prompts and another explicitly guided to produce positive or negative tones. Surprisingly, even the neutral set showed consistent tonal skew, suggesting that bias may stem from the model's underlying conversational style. Using weak supervision through a pretrained DistilBERT model, we labeled tones and trained several classifiers to detect these patterns. Ensemble models achieved macro F1 scores up to 0.92, showing that tone bias is systematic, measurable, and relevant to designing fair and trustworthy conversational AI.
- [18] arXiv:2512.20298 (cross-list from cs.CL) [pdf, html, other]
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Title: Patterns vs. Patients: Evaluating LLMs against Mental Health Professionals on Personality Disorder Diagnosis through First-Person NarrativesSubjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
Growing reliance on LLMs for psychiatric self-assessment raises questions about their ability to interpret qualitative patient narratives. We present the first direct comparison between state-of-the-art LLMs and mental health professionals in diagnosing Borderline (BPD) and Narcissistic (NPD) Personality Disorders utilizing Polish-language first-person autobiographical accounts. We show that the top-performing Gemini Pro models surpassed human professionals in overall diagnostic accuracy by 21.91 percentage points (65.48% vs. 43.57%). While both models and human experts excelled at identifying BPD (F1 = 83.4 & F1 = 80.0, respectively), models severely underdiagnosed NPD (F1 = 6.7 vs. 50.0), showing a reluctance toward the value-laden term "narcissism." Qualitatively, models provided confident, elaborate justifications focused on patterns and formal categories, while human experts remained concise and cautious, emphasizing the patient's sense of self and temporal experience. Our findings demonstrate that while LLMs are highly competent at interpreting complex first-person clinical data, they remain subject to critical reliability and bias issues.
- [19] arXiv:2512.20586 (cross-list from cs.AI) [pdf, html, other]
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Title: Automated stereotactic radiosurgery planning using a human-in-the-loop reasoning large language model agentHumza Nusrat, Luke Francisco, Bing Luo, Hassan Bagher-Ebadian, Joshua Kim, Karen Chin-Snyder, Salim Siddiqui, Mira Shah, Eric Mellon, Mohammad Ghassemi, Anthony Doemer, Benjamin Movsas, Kundan ThindSubjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
Stereotactic radiosurgery (SRS) demands precise dose shaping around critical structures, yet black-box AI systems have limited clinical adoption due to opacity concerns. We tested whether chain-of-thought reasoning improves agentic planning in a retrospective cohort of 41 patients with brain metastases treated with 18 Gy single-fraction SRS. We developed SAGE (Secure Agent for Generative Dose Expertise), an LLM-based planning agent for automated SRS treatment planning. Two variants generated plans for each case: one using a non-reasoning model, one using a reasoning model. The reasoning variant showed comparable plan dosimetry relative to human planners on primary endpoints (PTV coverage, maximum dose, conformity index, gradient index; all p > 0.21) while reducing cochlear dose below human baselines (p = 0.022). When prompted to improve conformity, the reasoning model demonstrated systematic planning behaviors including prospective constraint verification (457 instances) and trade-off deliberation (609 instances), while the standard model exhibited none of these deliberative processes (0 and 7 instances, respectively). Content analysis revealed that constraint verification and causal explanation concentrated in the reasoning agent. The optimization traces serve as auditable logs, offering a path toward transparent automated planning.
Cross submissions (showing 4 of 4 entries)
- [20] arXiv:2407.15779 (replaced) [pdf, html, other]
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Title: Analyzing the Impact of the Automatic Ball Strike System in Professional Baseball through a Case Study on KBO League DataComments: 13 pages, 16 figuresSubjects: Human-Computer Interaction (cs.HC)
Recent advancements in professional baseball have led to the introduction of the Automated Ball-Strike (ABS) system, or ``robot umpires,'' which utilize machine learning, computer vision, and precise tracking technologies to automate ball-strike calls. The Korean Baseball Organization (KBO) league became the first professional baseball league to implement ABS during the 2024 season. Leveraging pitch data from 2,515 KBO games across multiple seasons and employing mathematical modeling, we examine the aggregate decision tendencies of human umpires versus those of the ABS within the ``gray zone'' of the strike zone. We propose and answer four research questions to examine the differences between human and robot umpires, player adaptation to ABS, assess the ABS system's fairness and consistency, and analyze its strategic implications for the game. Our findings offer valuable insights into the impact of technological integration in sports officiating, providing lessons relevant to future implementations in professional baseball and beyond.
- [21] arXiv:2410.23639 (replaced) [pdf, html, other]
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Title: Integrating Brain-Computer Interface and Neuromorphic Computing for Human Digital TwinsComments: 7 pages, 3 figures,Subjects: Human-Computer Interaction (cs.HC); Networking and Internet Architecture (cs.NI)
The integration of immersive communication into a human-centric ecosystem has intensified the demand for sophisticated Human Digital Twins (HDTs) driven by multifaceted human data. However, the effective construction of HDTs faces significant challenges due to the heterogeneity of data collection devices, the high energy demands associated with processing intricate data, and concerns over the privacy of sensitive information. This work introduces a novel biologically-inspired (bio-inspired) HDT framework that leverages Brain-Computer Interface (BCI) sensor technology to capture brain signals as the data source for constructing HDT. By collecting and analyzing these signals, the framework not only minimizes device heterogeneity and enhances data collection efficiency, but also provides richer and more nuanced physiological and psychological data for constructing personalized HDTs. To this end, we further propose a bio-inspired neuromorphic computing learning model based on the Spiking Neural Network (SNN). This model utilizes discrete neural spikes to emulate the way of human brain processes information, thereby enhancing the system's ability to process data effectively while reducing energy consumption. Additionally, we integrate a Federated Learning (FL) strategy within the model to strengthen data privacy. We then conduct a case study to demonstrate the performance of our proposed twofold bio-inspired scheme. Finally, we present several challenges and promising directions for future research of HDTs driven by bio-inspired technologies.
- [22] arXiv:2508.16580 (replaced) [pdf, html, other]
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Title: Adaptive Command: Real-Time Policy Adjustment via Language Models in StarCraft IISubjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
We present Adaptive Command, a novel framework integrating large language models (LLMs) with behavior trees for real-time strategic decision-making in StarCraft II. Our system focuses on enhancing human-AI collaboration in complex, dynamic environments through natural language interactions. The framework comprises: (1) an LLM-based strategic advisor, (2) a behavior tree for action execution, and (3) a natural language interface with speech capabilities. User studies demonstrate significant improvements in player decision-making and strategic adaptability, particularly benefiting novice players and those with disabilities. This work contributes to the field of real-time human-AI collaborative decision-making, offering insights applicable beyond RTS games to various complex decision-making scenarios.
- [23] arXiv:2510.15905 (replaced) [pdf, html, other]
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Title: "She's Like a Person but Better": Characterizing Companion-Assistant Dynamics in Human-AI RelationshipsAikaterina Manoli, Janet V. T. Pauketat, Ali Ladak, Hayoun Noh, Angel Hsing-Chi Hwang, Jacy Reese AnthisComments: Improved visualizations, and corrected analysis error that had swapped reports of "Respect" and "Shame."Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
Large language models are increasingly used for both task-based assistance and social companionship, yet research has typically focused on one or the other. Drawing on a survey (N = 202) and 30 interviews with high-engagement ChatGPT and Replika users, we characterize digital companionship as an emerging form of human-AI relationship. With both systems, users were drawn to humanlike qualities, such as emotional resonance and personalized responses, and non-humanlike qualities, such as constant availability and inexhaustible tolerance. This led to fluid chatbot uses, such as Replika as a writing assistant and ChatGPT as an emotional confidant, despite their distinct branding. However, we observed challenging tensions in digital companionship dynamics: participants grappled with bounded personhood, forming deep attachments while denying chatbots "real" human qualities, and struggled to reconcile chatbot relationships with social norms. These dynamics raise questions for the design of digital companions and the rise of hybrid, general-purpose AI systems.
- [24] arXiv:2511.01106 (replaced) [pdf, html, other]
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Title: Defining a Role-Centered Terminology for Physical Representations and ControlsComments: 17 pages. Approximately 7000 words. 4 figures and 7 tablesSubjects: Human-Computer Interaction (cs.HC)
Previous classifications advanced research through a better understanding of the field and the variety of tangible user interfaces and related physical user interfaces, especially by discretizing a degree of tangibility based on the specimens produced by the community over the years, since the conceptualization of Tangible User Interface initiated a research effort to deepen the exploration of the concept. However, no taxonomy enables the classification of tangible user interfaces at the application level. This article proposes to refine the description of tangible user interfaces' interactional components through a terminological approach. The resulting terms are blended words, built from known words, that self-contain what digital role is represented or controlled and how it becomes physical. This holistic terminology then enables the definition of applications' hallmarks and four classes of tangibility for applications, which surpass the description of physical user interface specimens' morphology by abstracting and discriminating specimens at the applicative level. The descriptiveness and holisticness of the new terminology, as well as the clustering and discriminative power of the limited number of four classes, are showed on a corpus of applicative tangible user interfaces' specimens from the literature. Promising future work will benefit from the holistic terminology, the applications' hallmarks, and the tangibility classes, to describe applicative tangible user interfaces and related physical user interfaces to better understand the dozens of specimens that were produced by the field over three decades. Indeed, describing and classifying this whole set would deepen our understanding to provide tools for future developers and designers.
- [25] arXiv:2511.06914 (replaced) [pdf, html, other]
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Title: A Low-Cost ATmega32-Based Embedded System for Automated Patient Queue and Health Data Management in Private Medical ChambersComments: v2: revised writing and presentationSubjects: Human-Computer Interaction (cs.HC)
This paper presents a low-cost, stand-alone embedded system that automates patient queue handling and basic health data acquisition for small private medical chambers. The proposed design separates interaction into two physically distinct modules: a patient's self-service corner for entering basic details and measuring vital signs, and a doctor's corner for reviewing the current patient's information and advancing the queue. A single ATmega32 microcontroller coordinates both modules, interfacing with an LM35 temperature sensor, an XD-58C pulse sensor, matrix keypads for data entry, and dual 16$\times$2 LCDs for guided interaction and clinician-side display. Unlike IoT-first approaches that require continuous connectivity and higher deployment overhead, the system operates offline and provides deterministic local operation suitable for resource-constrained settings. Experimental validation shows temperature readings within $\pm 1^{\circ}$C (LM35 range tested), resting pulse readings within $\pm 3$~BPM, and button-to-display latency below 1.2~s, demonstrating reliable real-time performance under limited hardware resources.
- [26] arXiv:2501.12289 (replaced) [pdf, html, other]
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Title: Regressor-Guided Generative Image Editing Balances User Emotions to Reduce Time Spent OnlineChristoph Gebhardt, Robin Willardt, Seyedmorteza Sadat, Chih-Wei Ning, Andreas Brombach, Jie Song, Otmar Hilliges, Christian HolzComments: 44 pages, 22 figuresSubjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Internet overuse is a widespread phenomenon in today's digital society. Existing interventions, such as time limits or grayscaling, often rely on restrictive controls that provoke psychological reactance and are frequently circumvented. Building on prior work showing that emotional responses mediate the relationship between content consumption and online engagement, we investigate whether regulating the emotional impact of images can reduce online use in a non-coercive manner. We introduce and systematically analyze three regressor-guided image-editing approaches: (i) global optimization of emotion-related image attributes, (ii) optimization in a style latent space, and (iii) a diffusion-based method using classifier and classifier-free guidance. While the first two approaches modify low-level visual features (e.g., contrast, color), the diffusion-based method enables higher-level changes (e.g., adjusting clothing, facial features). Results from a controlled image-rating study and a social media experiment show that diffusion-based edits balance emotional responses and are associated with lower usage duration while preserving visual quality.
- [27] arXiv:2506.09160 (replaced) [pdf, other]
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Title: Understanding Human-AI Trust in EducationComments: Final version, published to Telematics and Informatics ReportsJournal-ref: Telematics and Informatics Reports 20 (2025) 100270Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
As AI chatbots become integrated in education, students are turning to these systems for guidance, feedback, and information. However, the anthropomorphic characteristics of these chatbots create ambiguity over whether students develop trust in them in ways similar to trusting a human peer or instructor (human-like trust, often linked to interpersonal trust models) or in ways similar to trusting a conventional technology (system-like trust, often linked to technology trust models). This ambiguity presents theoretical challenges, as interpersonal trust models may inappropriately ascribe human intentionality and morality to AI, while technology trust models were developed for non-social systems, leaving their applicability to conversational, human-like agents unclear. To address this gap, we examine how these two forms of trust, human-like and system-like, comparatively influence students' perceptions of an AI chatbot, specifically perceived enjoyment, trusting intention, behavioral intention to use, and perceived usefulness. Using partial least squares structural equation modeling, we found that both forms of trust significantly influenced student perceptions, though with varied effects. Human-like trust was the stronger predictor of trusting intention, whereas system-like trust more strongly influenced behavioral intention and perceived usefulness; both had similar effects on perceived enjoyment. The results suggest that interactions with AI chatbots give rise to a distinct form of trust, human-AI trust, that differs from human-human and human-technology models, highlighting the need for new theoretical frameworks in this domain. In addition, the study offers practical insights for fostering appropriately calibrated trust, which is critical for the effective adoption and pedagogical impact of AI in education.
- [28] arXiv:2512.18261 (replaced) [pdf, html, other]
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Title: Software Vulnerability Management in the Era of Artificial Intelligence: An Industry PerspectiveComments: Accepted at the 48th IEEE/ACM International Conference on Software Engineering (ICSE 2026) - Research TrackSubjects: Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Artificial Intelligence (AI) has revolutionized software development, particularly by automating repetitive tasks and improving developer productivity. While these advancements are well-documented, the use of AI-powered tools for Software Vulnerability Management (SVM), such as vulnerability detection and repair, remains underexplored in industry settings. To bridge this gap, our study aims to determine the extent of the adoption of AI-powered tools for SVM, identify barriers and facilitators to the use, and gather insights to help improve the tools to meet industry needs better. We conducted a survey study involving 60 practitioners from diverse industry sectors across 27 countries. The survey incorporates both quantitative and qualitative questions to analyze the adoption trends, assess tool strengths, identify practical challenges, and uncover opportunities for improvement. Our findings indicate that AI-powered tools are used throughout the SVM life cycle, with 69% of users reporting satisfaction with their current use. Practitioners value these tools for their speed, coverage, and accessibility. However, concerns about false positives, missing context, and trust issues remain prevalent. We observe a socio-technical adoption pattern in which AI outputs are filtered through human oversight and organizational governance. To support safe and effective use of AI for SVM, we recommend improvements in explainability, contextual awareness, integration workflows, and validation practices. We assert that these findings can offer practical guidance for practitioners, tool developers, and researchers seeking to enhance secure software development through the use of AI.
- [29] arXiv:2512.18871 (replaced) [pdf, other]
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Title: Psychometric Validation of the Sophotechnic Mediation Scale and a New Understanding of the Development of GenAI Mastery: Lessons from 3,932 Adult Brazilian WorkersComments: 35 pages, 28 Manuscript, Portuguese and English Versions of the Instrument in AnnexSubjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
The rapid diffusion of generative artificial intelligence (GenAI) systems has introduced new forms of human-technology interaction, raising the question of whether sustained engagement gives rise to stable, internalized modes of cognition rather than merely transient efficiency gains. Grounded in the Cognitive Mediation Networks Theory, this study investigates Sophotechnic Mediation, a mode of thinking and acting associated with prolonged interaction with GenAI, and presents a comprehensive psychometric validation of the Sophotechnic Mediation Scale. Data were collected between 2023 and 2025 from independent cross-sectional samples totaling 3,932 adult workers from public and private organizations in the Metropolitan Region of Pernambuco, Brazil. Results indicate excellent internal consistency, a robust unidimensional structure, and measurement invariance across cohorts. Ordinal-robust confirmatory factor analyses and residual diagnostics show that elevated absolute fit indices reflect minor local dependencies rather than incorrect dimensionality. Distributional analyses reveal a time-evolving pattern characterized by a declining mass of non-adopters and convergence toward approximate Gaussianity among adopters, with model comparisons favoring a two-process hurdle model over a censored Gaussian specification. Sophotechnic Mediation is empirically distinct from Hypercultural mediation and is primarily driven by cumulative GenAI experience, with age moderating the rate of initial acquisition and the depth of later integration. Together, the findings support Sophotechnia as a coherent, measurable, and emergent mode of cognitive mediation associated with the ongoing GenAI revolution.