This paper investigates how two forms of friction, data-driven questions and what-if analysis, are perceived by medical experts when using decision-support systems.

This study examines public perceptions of automated decision-making in healthcare using data from an ongoing longitudinal survey panel. Factors influencing perceived helpfulness, riskiness, and fairne…

This paper analyzes 2,053 real patient-chatbot conversations and develops a patient simulator to evaluate the performance of LLMs in symptom assessment. Communication style was found to significantly…

This paper demonstrates the development of a culturally grounded, AI-augmented User Experience Research Point of View (POV) for a telemedicine dementia care framework in Nigeria, providing a replicabl…

This paper introduces a Generative AI-augmented UXR methodology, grounded in the UXR Point of View (PoV) Playbook, to design Neuroinclusive digital interventions for emotional regulation in adults wit…

This paper proposes PsyBridge, a hybrid intelligent decision-support framework for multi-dimensional mental health assessment using clinically validated screening tools, cognitive evaluation, and pers…

This study analyzes ClinicalTrials.gov records to track the rising trend of AI in clinical trials and demonstrates that a hybrid human-AI screening approach is viable but requires clearer reporting of…

CAREAgent is a novel agent designed for fine-grained clinical order generation, achieving significant performance improvements on unseen benchmarks by integrating structured reasoning and tool usage.

The paper introduces EHRBench, a large-scale, automated, and reliable benchmark derived from real Electronic Health Records (EHRs) to rigorously evaluate the clinical decision-making capabilities of L…

Two student cohorts designed cognitively accessible GenAI interfaces, leading to structural and experiential scaffolding concepts.

This study surveyed higher education practitioners to map their beliefs and behaviors regarding AI integration, finding that while they view AI favorably, institutional barriers and gaps in design-ori…

A self-evolving annotation framework for Major Depressive Disorder using large language models and expert verification is proposed to improve annotation consistency and explainability.

This study investigated the stability and prompt-responsiveness of AI tools in classifying the cognitive demand of math tasks, finding that few-shot prompting was a more reliable performance booster t…

The paper introduces the Causal Sensitivity Score (CSS), an interventional metric that reveals that standard coverage-based evaluations fail to detect critical responsiveness deficits in clinical LLMs…

The study found that while AI collaboration is promising, highly competent and proactive AI systems can negatively impact human perceptions of ownership and job meaningfulness, suggesting that design…

This paper critically reviews the intersection of philosophy of science and explainable AI (XAI) in medicine, identifying necessary conditions for a philosophically grounded approach to explanation.

The paper introduces a Generative AI-augmented User Experience Research (UXR) methodology, operationalized through a four-stage process, to create actionable, stigma-aware design guidance for digital…

This paper presents a large-scale analysis of AI-based learning assistant (Syntea) usage in higher education using log data from 77,543 students.

The paper critiques current AI benchmarking practices for low-resource settings, arguing that evaluation must shift focus from isolated model performance to the holistic performance of the deployed sy…