237 papers
This paper demonstrates that large language models exhibit robust decision biases in two-arm bandit tasks, characterized by stubborn exploitation and low learning rates that persist across decoding parameters, thereby posing significant challenges for optimal human-AI collaboration.
This paper presents YAQIN, a co-designed AI application that integrates Islamic frameworks and user-centered design to provide culturally sensitive mental health support for Muslim women in the UK, addressing gaps in trust and engagement through a faith-aware chatbot and guided journaling tool.
This paper proposes an automated method for generating task breakpoints in Virtual Reality recordings using an Origin-Centric Graph to analyze spatio-temporal scene changes, enabling adaptive playback and demonstrating high accuracy against user-perceived ground truth.
This paper argues that relying solely on fixed embedding similarity for analyzing creative processes in AI design tools is insufficient because it fails to capture meaningful creative pivots, and it outlines three key challenges—aligning metrics with creative significance, handling multimodal traces, and evaluating agentic systems—while proposing context-aware LLM interventions to better capture session-specific dynamics.
This paper proposes a method to reconstruct high-level creative workflows from low-level system logs by parsing raw data into structured behavioral graphs, thereby enabling future process-aware AI agents to better understand user intent and assist in creative tasks.
The paper introduces AiRWeb, a phone-based augmented reality system that allows users to seamlessly offload and organize arbitrary web content into their surrounding physical space to overcome mobile screen limitations, demonstrating its learnability and usability while highlighting design challenges in activation modes.
MIRO is a privacy-preserving framework that combines distributed particulate matter sensors with a multi-radar mmWave re-identification system, utilizing GAN-based view adaptation to track workers and estimate their specific exposure to airborne pollutants in industrial environments without relying on visual data.
This paper reveals a critical expectation gap in the freelance economy where workers often withhold AI use due to a mistaken belief that clients can detect it, while clients prefer proactive disclosure and lack clear policies, ultimately highlighting the urgent need for standardized guidelines to rebuild trust and accountability in AI-mediated work.
The paper introduces GeoVisA11y, an open-source, LLM-based system that enables screen-reader users to interact with geovisualizations through natural language queries, validated by user studies demonstrating its effectiveness in bridging accessibility gaps and revealing distinct interaction patterns.
This paper presents the first physical testbed evaluation of the Internet of Mirrors (IoM), demonstrating that while offloading computation to higher-tier nodes reduces local latency and resource load, the optimal task placement strategy depends on a dynamic trade-off between network conditions, payload size, and concurrent user load.
This study argues that while federally mandated shared data systems like HMIS facilitate coordination and accountability in U.S. homelessness services, they simultaneously reinforce power asymmetries by shifting resource-constrained providers toward compliance roles and concentrating decision-making authority.
This paper introduces VRxBioRelax, a real-time virtual reality biofeedback system that utilizes sEMG data to drive an immersive relaxation environment, demonstrating through extensive pre-clinical testing that its average end-to-end latency of 25.34 ms significantly meets both VR comfort and clinical benchmarks for effective muscle relaxation training.
The paper introduces Agora, an AI-powered platform that leverages LLMs to simulate diverse human perspectives on policy issues, enabling users to practice consensus-building and demonstrating through a preliminary study that access to authentic voice explanations significantly enhances problem-solving skills and the quality of collective decisions compared to viewing aggregate data alone.
This study reveals that in factual verification tasks, users' trust and decision-making are primarily driven by the correctness and certainty framing of LLM rationales rather than their presentation format, highlighting the dual potential of well-designed rationales to either support decision-making or miscalibrate trust.
This paper presents a small-scale laboratory study demonstrating that embedding lightweight interactive components into Large Language Model-generated scaffolding can shift learners from passive consumption to active engagement, thereby improving perceived attention and short-term learning outcomes.
This study utilizes PLS-SEM analysis of data from Ghanaian universities to demonstrate that subjective norms, deterrence, and involvement mechanisms—particularly knowledge sharing—significantly shape contingent employees' attitudes toward information security policies, thereby driving their compliance intentions.
This mixed-methods study of 37 undergraduate students at a Ghanaian university reveals a strong preference for familiar instant messaging platforms like WhatsApp and Telegram over institutional learning management systems, driven by factors such as convenience, accessibility, and real-time interaction, thereby highlighting the need for educational strategies to align with students' existing digital habits.
This paper addresses the limitation of existing creative activity tracing methods that capture state changes without preserving intent or higher-level structure by proposing three complementary domain-specific approaches: a node-based interface for GenAI, a vocabulary of visual cues for visualization authoring, and a semantic history-embedded programming model.
The paper introduces NarrativeLoom, a multi-persona collaborative storytelling system grounded in Campbell's theory of blind variation and selective retention, which a controlled study of 50 participants shows significantly enhances the novelty, diversity, and overall creativity of co-authored stories compared to existing tools, particularly benefiting novice writers through structured scaffolding.
This paper argues that human-AI alignment in early developmental communities should be treated as a community-governed process involving layered collaboration between families and professionals, rather than an individual optimization problem, by establishing expert-grounded structures, professional guardrails, and family-level adaptations for multimodal LLM outputs.