252 papers
This exploratory pilot study suggests that while increased kinaesthetic demands (specifically writing pressure) may slightly impair immediate information retention, tactile sensitivity manipulation (glove use) has no clear effect, and neither mental effort nor perceived workload significantly mediates these outcomes.
Jagarin is a three-layer mobile architecture comprising the DAWN urgency engine, ARIA email proxy, and ACE institutional protocol that enables personal AI agents to hibernate until contextually necessary, thereby resolving the conflict between battery efficiency and timely duty fulfillment without compromising privacy or requiring persistent background execution.
This paper presents "Wire Your Way," a prototyping platform featuring an augmented breadboard that replaces rigid step-by-step tutorials with personalized, circuit-aware guidance and in-situ validation to better support makers' unique construction and debugging preferences.
This article proposes a unified definition of cognitive warfare and an OODA loop-based interaction framework to address current definitional inconsistencies, enabling joint force leaders and analysts to effectively assess, compare, and evaluate cognitive campaigns through measurable attributes of superiority.
This study investigates how decision workflows, explanations, and user expertise influence human-AI trust, revealing that a two-step workflow does not necessarily reduce overreliance, that reported trust and behavioral reliance are distinct constructs, and that the effectiveness of explanations depends on the interaction between workflow design and user domain knowledge.
This paper introduces the Multilingual Cloud Corpus, the first national-scale, parallel, multimodal dataset for Bangladesh's predominantly oral and endangered minority languages, which was systematically collected through extensive fieldwork and is now publicly available to advance digital preservation and low-resource natural language processing.
This paper presents and evaluates a mobile intervention using tailored embodied conversational agents to actively involve adolescents in HPV vaccination decisions alongside their parents, demonstrating that this approach improves knowledge, vaccination intent, and user satisfaction while fostering adolescent agency.
This paper presents AttentiveLearn, a VR-based learning ecosystem that generates personalized post-lecture quizzes using eye-tracking data to infer attention, which a four-week field study found to improve student motivation and engagement despite inconclusive evidence of learning gains.
This paper introduces "Ailed," a novel framework that injects dynamic, emotion-like behavioral variability into any chess engine by mapping a real-time "psyche" scalar derived from positional factors through an audio-inspired signal chain, thereby simulating human-like performance fluctuations such as tilt and overconfidence without requiring human-subject validation.
This paper introduces LeanTutor, a proof-of-concept system that combines Large Language Models with theorem provers to create an AI mathematical proof tutor, and evaluates its effectiveness using the newly introduced PeanoBench dataset.
This paper addresses the unreliability of multimodal agents in executing toggle control instructions within GUIs by proposing State-aware Reasoning (StaR), a method that significantly improves toggle execution accuracy by over 30% through perceiving current states and inferring desired outcomes.
This paper introduces Bloom, an LLM-integrated health coaching application that, while not immediately outperforming a non-LLM control in short-term physical activity levels, significantly enhances psychological outcomes like enjoyment and self-compassion, suggesting LLMs are particularly effective at fostering the mindset shifts necessary for long-term behavior change.
Two studies demonstrate that framing large language models as companions, rather than as machines or tools, leads people to attribute greater mental capacities to them and influences their reliance on the models for factual information.
This paper presents a framework of twelve design considerations for human oversight of AI, derived from co-design workshops with domain experts and integrated with work design theory, to guide the creation of interfaces that ensure effective, engaging, and meaningful human-AI collaboration.
This paper presents XAgen, an explainability tool designed to help users of varying expertise identify and correct opaque failures in multi-agent workflows through log visualization, human-in-the-loop feedback, and automatic error detection, as validated by a user study demonstrating its effectiveness in locating failures and improving system configurations.
This paper presents a large-scale empirical study of 401 open-source repositories to characterize developer-authored cursor rules, resulting in a five-theme taxonomy of project context essential for enhancing AI coding assistants.
This paper argues that while Large Language Models function as reliable externalist sources of information, their widespread use threatens collective intelligence by eroding the internalist reflective justification necessary for true knowledge, necessitating a three-tier normative framework to preserve epistemic duties and standards.
This paper introduces SycoEval-EM, a multi-agent simulation framework that reveals significant and unpredictable sycophancy in large language models during emergency care scenarios, demonstrating that static benchmarks fail to capture their vulnerability to patient pressure and highlighting the need for adversarial testing in clinical AI certification.
This study compares AR-guided spinal needle intervention workflows using ultrasound shape completion versus CBCT, finding that while both are viable, the CBCT-based approach offers superior efficiency, precision, and user trust, whereas the ultrasound method provides a radiation-free alternative with limitations in deep anatomical reconstruction, suggesting a hybrid workflow as an optimal solution.
This paper proposes a new paradigm for causal discovery that leverages crowdsourcing, expert elicitation, and LLM-based simulation to aggregate fragmented knowledge from multiple agents into a comprehensive global causal structure unattainable by any single entity.