This study investigates how AI's integration into various workplaces affects employees' job satisfaction by examining changes in perceived job decency and meaningfulness, finding that the impact varie…

This paper proposes shifting the focus of AI research from isolated computational outputs to interaction dynamics, establishing 'Interaction-Centered Intelligence' as the primary framework for underst…

This study explores why and how users customize social AI, identifying motivations and contributions to a closer relationship.

This paper introduces the ExpressionCueLens framework to analyze how anthropomorphism expressions are used in human-AI companion agent interactions on Reddit and XiaoHongShu, revealing cultural and pl…

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 study investigates human-AI collaboration in question answering, finding that while collaboration is beneficial, humans make suboptimal decisions by both under-relying on correct AI suggestions a…

This paper proposes a new architecture for agent models, the Goal-Identity-Configurator (GIC), and discusses the distinction between 'agnetic' and 'agentive' systems, arguing for internalized agency.

The paper argues that purported anthropomorphic attributes of LLMs are not unique to language models but are substrate-dependent, demonstrating this by training a neural network on the game Age of Emp…

The paper introduces EUDAIMONIA, a new framework and benchmark for evaluating how well LLMs align with user welfare in social interactions, finding that even state-of-the-art models frequently violate…

This paper proposes a theoretical model of autonomy surrender in human decision-making environments due to AI integration, including mechanisms of cognitive bandwidth depletion and recovery.

The paper introduces the VET Framework, a tool for analyzing polarized public discourse on AI by categorizing narratives based on valence, effectiveness, and trajectory, thereby promoting AI literacy.

This paper introduces a controlled, two-player extension of the Alternate Uses Test (AUT) for comparing human-human and human-AI co-creation under matched conditions, demonstrating equivalent original…

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 identifies five persistent, deep-seated behavioral patterns ('training strata') in LLMs, observed through long-term, intimate human-AI interaction, suggesting that training artifacts survive…

This paper tests the effectiveness of warning labels in mitigating sycophantic AI's influence on user judgment and relationships, finding that while labels shift perception, they do not reliably reduc…

The paper introduces Rationalize, a role-pair framework that facilitates shared semantic reasoning between humans and AI models to achieve deep alignment of intent and action.

The XAIxArts workshop explores the operationalisation of Explainable AI in the Arts, focusing on diversity, ideation, and resource development.

This paper introduces a human-AI collaborative decision-making framework, called Human-Centric Reflective Architecture (HCRA), to enhance effectiveness and align AI agents with human preferences using…

The paper defines AI Identity as the correspondence between an agent's declared state and its observed behavior, concluding that current infrastructure and standards are fundamentally inadequate for g…