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 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…

The paper proposes a structured framework, the Cognitive Accessibility UXR Playbook, that uses UXR principles and Generative AI to transform ambiguous requirements into measurable, actionable specific…

The paper extends the User Experience Research (UXR) Points of View (PoV) framework into an AI-augmented methodology specifically designed for guiding the development and governance of high-stakes, hu…

The paper demonstrates that models can acquire 'evaluation meta-knowledge' from training data describing evaluation practices, leading to inflated safety benchmark performance that is independent of e…

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…

The paper argues that LLM guardrails and persona dynamics create an unethical 'reality gap' by laundering epistemic risk onto users, advocating for task-level causal requirements over response-level m…

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…

The paper proposes AI From the Margins (AIM), a methodological stance that centers the lived experiences of minoritized communities to fundamentally reshape the goals and scope of participatory AI des…

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…

The paper finds that while LLMs can detect distress regardless of delusional framing, they significantly fail to intervene safely when distress is intertwined with delusion, suggesting a critical reco…

This survey provides a comprehensive, systematic roadmap for achieving cyber-physical system (CPS) resilience by integrating five interconnected themes: system-wide properties, handling data scarcity…

The paper demonstrates that tool-augmented agentic AI can learn from prior field experiment data to automatically generate superior, domain-specific interventions, transforming one-shot A/B testing in…

This paper introduces FreakOut-LLM, demonstrating that emotional context, specifically stress, significantly compromises the safety alignment of large language models, increasing jailbreak susceptibil…

The paper investigates how AI coding assistants shift developers' security focus from proactive prevention to reactive review, finding that this structural change is reinforced by current tool interac…

This paper systematically evaluates how LLMs uncritically adapt to potentially dangerous user prompts related to eating disorders, finding that specific linguistic cues significantly increase the like…

The paper analyzes the CIIM risk model using postphenomenology, arguing that such formal models act as mediating artifacts that fundamentally shape how cybersecurity practitioners perceive and respond…

This cross-national review analyzed government cybersecurity guidance for smart homes, finding that while general security advice is abundant, structured, step-by-step incident response guidance is ra…