OpenRaiser/Intern-Atlas huggingface dataset:
From Interaction to Collaboration: How Hybrid Intelligence Enhances Chatbot Feedback
Venue: arXiv cs.HC 2025
Year: 2025

Generative AI (GenAI) chatbots are becoming increasingly integrated into virtual assistant technologies, yet their success hinges on the ability to gather meaningful user feedback to improve interaction quality, system outcomes, and overall user acceptance. Successful chatbot interactions can enable organizations to build long-term relationships with their customers and users, supporting customer loyalty and furthering the organization's goals. This study explores the impact of two distinct narratives and feedback collection mechanisms on user engagement and feedback behavior: a standard AI-focused interaction versus a hybrid intelligence (HI) framed interaction. Initial findings indicate that while small-scale survey measures allowed for no significant differences in user willingness to leave feedback, use the system, or trust the system, participants exposed to the HI narrative statistically significantly provided more detailed feedback. These initial findings offer insights into designing effective feedback systems for GenAI virtual assistants, balancing user effort with system improvement potential.
Resource Utilization Optimized Federated Learning
Venue: arXiv cs.DC 2025
Year: 2025

Federated learning (FL) systems facilitate distributed machine learning across a server and multiple devices. However, FL systems have low resource utilization limiting their practical use in the real world. This inefficiency primarily arises from two types of idle time: (i) task dependency between the server and devices, and (ii) stragglers among heterogeneous devices. This paper introduces FedOptima, a resource-optimized FL system designed to simultaneously minimize both types of idle time; existing systems do not eliminate or reduce both at the same time. FedOptima offloads the training of certain layers of a neural network from a device to server using three innovations. First, devices operate independently of each other using asynchronous aggregation to eliminate straggler effects, and independently of the server by utilizing auxiliary networks to minimize idle time caused by task dependency. Second, the server performs centralized training using a task scheduler that ensures balanced contributions from all devices, improving model accuracy. Third, an efficient memory management mechanism on the server increases scalability of the number of participating devices. Four state-of-the-art offloading-based and asynchronous FL methods are chosen as baselines. Experimental results show that compared to the best results of the baselines on convolutional neural networks and transformers on multiple lab-based testbeds, FedOptima (i) achieves higher or comparable accuracy, (ii) accelerates training by 1.9x to 21.8x, (iii) reduces server and device idle time by up to 93.9% and 81.8%, respectively, and (iv) increases throughput by 1.1x to 2.0x.
Impact of Environmental Colors on Human Aggressiveness: Insights from a Minecraft-Based Behavioral Study
Venue: arXiv cs.HC 2025
Year: 2025

This study explores the influence of environmental colors on human behavior, specifically focusing on aggressiveness and passiveness. Color is widely regarded as an influential environmental factor shaping human behavior, yet existing studies present conflicting evidence regarding its impact on aggressiveness and passiveness. This study employed Minecraft as a controlled digital platform to investigate whether exposure to different colors influences both the frequency and nature of participant interactions (aggressive versus non-aggressive), and whether prolonged exposure amplifies these effects. Anonymous online participants were exposed to various colors before interacting with non-player characters simulating human-like encounters. Three key outcomes were measured: (1) total interactions per color, (2) ratios of aggressive to non-aggressive interactions per color, and (3) the effect of varying exposure durations on aggressiveness. While no significant overall differences in interaction frequency were observed among the colors, post-hoc analyses revealed that Red and Black elicited significantly more interactions compared to Green. Additionally, Red, Yellow, and Black were associated with higher ratios of aggressive behavior relative to Green or White. Prolonged exposure to Red also appeared to intensify aggressive responses. These findings underscore the potential role of environmental color in shaping online social behaviors and highlight the importance of environmental settings in areas ranging from online communication platforms to digital marketing strategies.
Participatory Design of EHR Components: Crafting Novel Relational Spaces for IT Specialists and Hospital Staff to Cooperate
Venue: arXiv cs.HC 2025
Year: 2025

Introduced in the early 2010s, Electronic Health Records (EHRs) have become ubiquitous in hospitals. Despite clear benefits, they remain unpopular among healthcare professionals and present significant challenges. Positioned at the intersection of Health Information Systems studies, Computer Supported Collaborative Work (CSCW), Service Design, and Participatory Design (PD), our research investigates how involving users in the co-design of new EHR components within a dedicated hospital space can transform healthcare practices. Through participatory co-design methodologies, including ethnographic observation, collaborative workshops, and realistic simulations, we identify the material and interactional elements essential for rebalancing power dynamics between users and designers. This project contributes to rethinking traditional EHR design approaches, embedding design practice into systemic transformation to genuinely meet healthcare professionals' needs.
Mapping Executive Function Tasks for Children: A Scoping Review for Designing a Research-Oriented Platform
Venue: arXiv cs.HC 2025
Year: 2025

Background: Executive functions (EFs) are cognitive processes essential for controlling impulses, staying focused, thinking before acting, and managing information. Childhood is a critical period for EF development, but there is a lack of standardized tools that combine EF tasks with physical activity in a gamified approach. Objectives: This scoping review maps EF tasks for children, identifies common strategies, and explores methods for measuring outcomes, providing a foundation for a research-oriented platform to assess EF development. Design: A systematic search was conducted in SCOPUS, ScienceDirect, and ERIC databases with the query "executive function task" AND (children OR child OR childhood). Inclusion criteria were studies published between 2019 and 2024 in English, with participants aged 5 to 9 years. Data extracted included task details, scoring mechanisms, and stop conditions. Studies lacking clear methodological descriptions were excluded. Results: A total of 2044 articles were identified, with 113 duplicates removed. After selection, 23 studies met the inclusion criteria. The identified tasks are listed in Table 2. Key tasks, strategies, and measurement methodologies were highlighted. Conclusions: Integrating EF tasks into a structured platform offers a promising approach to standardize assessments, fill research gaps, and provide a reliable tool for studying EF development in children. Keywords: Executive Functions, Inhibition, Working Memory, Cognitive Flexibility, Task Design, Standardization
Translating Multimodal AI into Real-World Inspection: TEMAI Evaluation Framework and Pathways for Implementation
Venue: arXiv cs.HC 2025
Year: 2025

This paper introduces the Translational Evaluation of Multimodal AI for Inspection (TEMAI) framework, bridging multimodal AI capabilities with industrial inspection implementation. Adapting translational research principles from healthcare to industrial contexts, TEMAI establishes three core dimensions: Capability (technical feasibility), Adoption (organizational readiness), and Utility (value realization). The framework demonstrates that technical capability alone yields limited value without corresponding adoption mechanisms. TEMAI incorporates specialized metrics including the Value Density Coefficient and structured implementation pathways. Empirical validation through retail and photovoltaic inspection implementations revealed significant differences in value realization patterns despite similar capability reduction rates, confirming the framework's effectiveness across diverse industrial sectors while highlighting the importance of industry-specific adaptation strategies.
An AI-powered Public Health Automated Kiosk System for Personalized Care: An Experimental Pilot Study
Venue: arXiv cs.HC 2025
Year: 2025

Background: The HERMES Kiosk (Healthcare Enhanced Recommendations through Artificial Intelligence & Expertise System) is designed to provide personalized Over-the-Counter (OTC) medication recommendations, addressing the limitations of traditional health kiosks. It integrates an advanced GAMENet model enhanced with Graph Attention Networks (GAT) and Multi-Head Cross-Attention (MHCA) while ensuring user privacy through federated learning. This paper outlines the conceptual design and architecture of HERMES, with a focus on deployment in high-traffic public areas. Methods: HERMES analyzes self-reported symptoms and anonymized medical histories using AI algorithms to generate context-aware OTC medication recommendations. The system was initially trained using Electronic Health Records (EHR) from the MIMIC-III dataset (6,350 patients) and Drug-Drug Interaction (DDI) data from the TWOSIDES database, incorporating the top 90 severity DDI types. Real-time DDI checks and ATC-mapped drug codes further improve safety. The kiosk is designed for accessibility, offering multilingual support, large fonts, voice commands, and Braille compatibility. A built-in health education library promotes preventive care and health literacy. A survey was conducted among 10 medical professionals to evaluate its potential applications in medicine. Results: Preliminary results show that the enhanced GAMENet model achieved a Precision-Recall AUC (PRAUC) of 0.74, outperforming the original model. These findings suggest a strong potential for delivering accurate and secure healthcare recommendations in public settings. Conclusion: HERMES demonstrates how AI-driven, privacy-preserving kiosks can enhance public health access, empower users, and alleviate burdens on healthcare systems. Future work will focus on real-world deployment, usability testing, and scalability for broader adoption.
Hybrid Deep Learning Model to Estimate Cognitive Effort from fNIRS Signals
Venue: arXiv cs.HC 2025
Year: 2025

This study estimates cognitive effort based on functional near-infrared spectroscopy data and performance scores using a hybrid DeepNet model. The estimation of cognitive effort enables educators to modify material to enhance learning effectiveness and student engagement. In this study, we collected oxygenated hemoglobin using functional near-infrared spectroscopy during an educational quiz game. Participants (n=16) responded to 16 questions in a Unity-based educational game, each within a 30-second response time limit. We used DeepNet models to predict the performance score from the oxygenated hemoglobin, and compared traditional machine learning and DeepNet models to determine which approach provides better accuracy in predicting performance scores. The result shows that the proposed CNN-GRU gives better performance with 73% than other models. After the prediction, we used the predicted score and the oxygenated hemoglobin to observe cognitive effort by calculating relative neural efficiency and involvement in our test cases. Our result shows that even with moderate accuracy, the predicted cognitive effort closely follow the actual trends. This findings can be helpful in designing and improving learning environments and provide valuable insights into learning materials.
Maestoso: An Intelligent Educational Sketching Tool for Learning Music Theory
Venue: arXiv cs.HC 2025
Year: 2025

Learning music theory not only has practical benefits for musicians to write, perform, understand, and express music better, but also for both non-musicians to improve critical thinking, math analytical skills, and music appreciation. However, current external tools applicable for learning music theory through writing when human instruction is unavailable are either limited in feedback, lacking a written modality, or assuming already strong familiarity of music theory concepts. In this paper, we describe Maestoso, an educational tool for novice learners to learn music theory through sketching practice of quizzed music structures. Maestoso first automatically recognizes students' sketched input of quizzed concepts, then relies on existing sketch and gesture recognition techniques to automatically recognize the input, and finally generates instructor-emulated feedback. From our evaluations, we demonstrate that Maestoso performs reasonably well on recognizing music structure elements and that novice students can comfortably grasp introductory music theory in a single session.
Semantic Direct Modeling
Venue: arXiv cs.HC 2025
Year: 2025

Current direct modeling systems limit users to low-level interactions with vertices, edges, and faces, forcing designers to manage detailed geometric elements rather than focusing on high-level design intent. This paper introduces semantic direct modeling (SDM), a novel approach that lifts direct modeling from low-level geometric modifications to high-level semantic interactions. This is achieved by utilizing a large language model (LLM) fine-tuned with CAD-specific prompts, which can guide the LLM to reason through design intent and accurately interpret CAD commands, thereby allowing designers to express their intent using natural language. Additionally, SDM maps design intent to the corresponding geometric features in the CAD model through a new conditional, context-sensitive feature recognition method, which uses generative AI to dynamically assign feature labels based on design intent. Together, they enable a seamless flow from high-level design intent to low-level geometric modifications, bypassing tedious software interactions. The effectiveness of SDM has been validated through real mechanical design cases.
Show Me How: Benefits and Challenges of Agent-Augmented Counterfactual Explanations for Non-Expert Users
Venue: arXiv cs.HC 2025
Year: 2025

Counterfactual explanations offer actionable insights by illustrating how changes to inputs can lead to different outcomes. However, these explanations often suffer from ambiguity and impracticality, limiting their utility for non-expert users with limited AI knowledge. Augmenting counterfactual explanations with Large Language Models (LLMs) has been proposed as a solution, but little research has examined their benefits and challenges for non-experts. To address this gap, we developed a healthcare-focused system that leverages conversational AI agents to enhance counterfactual explanations, offering clear, actionable recommendations to help patients at high risk of cardiovascular disease (CVD) reduce their risk. Evaluated through a mixed-methods study with 34 participants, our findings highlight the effectiveness of agent-augmented counterfactuals in improving actionable recommendations. Results further indicate that users with prior experience using conversational AI demonstrated greater effectiveness in utilising these explanations compared to novices. Furthermore, this paper introduces a set of generic guidelines for creating augmented counterfactual explanations, incorporating safeguards to mitigate common LLM pitfalls, such as hallucinations, and ensuring the explanations are both actionable and contextually relevant for non-expert users.
Predicting Satisfaction of Counterfactual Explanations from Human Ratings of Explanatory Qualities
Venue: arXiv cs.HC 2025
Year: 2025

Counterfactual explanations are a widely used approach in Explainable AI, offering actionable insights into decision-making by illustrating how small changes to input data can lead to different outcomes. Despite their importance, evaluating the quality of counterfactual explanations remains an open problem. Traditional quantitative metrics, such as sparsity or proximity, fail to fully account for human preferences in explanations, while user studies are insightful but not scalable. Moreover, relying only on a single overall satisfaction rating does not lead to a nuanced understanding of why certain explanations are effective or not. To address this, we analyze a dataset of counterfactual explanations that were evaluated by 206 human participants, who rated not only overall satisfaction but also seven explanatory criteria: feasibility, coherence, complexity, understandability, completeness, fairness, and trust. Modeling overall satisfaction as a function of these criteria, we find that feasibility (the actionability of suggested changes) and trust (the belief that the changes would lead to the desired outcome) consistently stand out as the strongest predictors of user satisfaction, though completeness also emerges as a meaningful contributor. Crucially, even excluding feasibility and trust, other metrics explain 58% of the variance, highlighting the importance of additional explanatory qualities. Complexity appears independent, suggesting more detailed explanations do not necessarily reduce satisfaction. Strong metric correlations imply a latent structure in how users judge quality, and demographic background significantly shapes ranking patterns. These insights inform the design of counterfactual algorithms that adapt explanatory qualities to user expertise and domain context.
MaRDMO: Future Gateway to FAIR Mathematical Data
Venue: arXiv cs.HC 2025
Year: 2025

Mathematical research data plays a crucial role across scientific disciplines, yet its documentation and dissemination remain challenging due to the lack of standardized research data management practices. The MaRDMO Plugin addresses these challenges by integrating mathematical models, algorithms, and interdisciplinary workflows into the established framework of the Research Data Management Organiser (RDMO). Built on FAIR principles, MaRDMO enables structured documentation and retrieval of mathematical research data through guided questionnaires. It connects to multiple knowledge graphs, including MathModDB, MathAlgoDB, and the MaRDI Portal. Users can document and search for models, algorithms, and workflows via dynamic selection interfaces that also leverage other sources such as Wikidata. The plugin facilitates the export to the individual MaRDI services, ensuring data quality through automated validation. By embedding mathematical research data management into the widely adopted RDMO platform, MaRDMO represents a significant step toward making mathematical research data more findable, accessible, and reusable.
AI-Assisted Conversational Interviewing: Effects on Data Quality and Respondent Experience
Venue: arXiv cs.HC 2025
Year: 2025

Standardized surveys scale efficiently but sacrifice depth, while conversational interviews improve response quality at the cost of scalability and consistency. This study bridges the gap between these methods by introducing a framework for AI-assisted conversational interviewing. To evaluate this framework, we conducted a web survey experiment where 1,800 participants were randomly assigned to AI 'chatbots' which use large language models (LLMs) to dynamically probe respondents for elaboration and interactively code open-ended responses to fixed questions developed by human researchers. We assessed the AI chatbot's performance in terms of coding accuracy, response quality, and respondent experience. Our findings reveal that AI chatbots perform moderately well in live coding even without survey-specific fine-tuning, despite slightly inflated false positive errors due to respondent acquiescence bias. Open-ended responses were more detailed and informative, but this came at a slight cost to respondent experience. Our findings highlight the feasibility of using AI methods such as chatbots enhanced by LLMs to enhance open-ended data collection in web surveys.
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
Venue: arXiv cs.CL 2025
Year: 2025

We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 86.7 on AIME 2024, 55.0 on Codeforces and 77.3 on GPQA, demonstrating excellent reasoning abilities in STEM and coding. Beyond reasoning tasks, the method demonstrates notable generalization across diverse domains. For instance, it surpasses DeepSeek R1 by 8% in win rate on non-reasoning tasks, indicating its broader applicability. Compared to other state-of-the-art reasoning models, Seed1.5-Thinking is a Mixture-of-Experts (MoE) model with a relatively small size, featuring 20B activated and 200B total parameters. As part of our effort to assess generalized reasoning, we develop two internal benchmarks, BeyondAIME and Codeforces, both of which will be publicly released to support future research. Model trial link: https://www.volcengine.com/experience/ark.
Task Matters: Investigating Human Questioning Behavior in Different Household Service for Learning by Asking Robots
Venue: arXiv cs.HC 2025
Year: 2025

Learning by Asking (LBA) enables robots to identify knowledge gaps during task execution and acquire the missing information by asking targeted questions. However, different tasks often require different types of questions, and how to adapt questioning strategies accordingly remains underexplored. This paper investigates human questioning behavior in two representative household service tasks: a Goal-Oriented task (refrigerator organization) and a Process-Oriented task (cocktail mixing). Through a human-human study involving 28 participants, we analyze the questions asked using a structured framework that encodes each question along three dimensions: acquired knowledge, cognitive process, and question form. Our results reveal that participants adapt both question types and their temporal ordering based on task structure. Goal-Oriented tasks elicited early inquiries about user preferences, while Process-Oriented tasks led to ongoing, parallel questioning of procedural steps and preferences. These findings offer actionable insights for developing task-sensitive questioning strategies in LBA-enabled robots for more effective and personalized human-robot collaboration.
Wireless Silent Speech Interface Using Multi-Channel Textile EMG Sensors Integrated into Headphones
Venue: arXiv cs.HC 2025
Year: 2025

This paper presents a novel wireless silent speech interface (SSI) integrating multi-channel textile-based EMG electrodes into headphone earmuff for real-time, hands-free communication. Unlike conventional patch-based EMG systems, which require large-area electrodes on the face or neck, our approach ensures comfort, discretion, and wearability while maintaining robust silent speech decoding. The system utilizes four graphene/PEDOT:PSS-coated textile electrodes to capture speech-related neuromuscular activity, with signals processed via a compact ESP32-S3-based wireless readout module. To address the challenge of variable skin-electrode coupling, we propose a 1D SE-ResNet architecture incorporating squeeze-and-excitation (SE) blocks to dynamically adjust per-channel attention weights, enhancing robustness against motion-induced impedance variations. The proposed system achieves 96% accuracy on 10 commonly used voice-free control words, outperforming conventional single-channel and non-adaptive baselines. Experimental validation, including XAI-based attention analysis and t-SNE feature visualization, confirms the adaptive channel selection capability and effective feature extraction of the model. This work advances wearable EMG-based SSIs, demonstrating a scalable, low-power, and user-friendly platform for silent communication, assistive technologies, and human-computer interaction.