Human-AI Interaction
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Related Articles from SNS
Effects of Personality- and Opinion-Alignment in Human-AI Interaction
arXiv:2511.10544v3 Announce Type: replace Abstract: Interactions with AI assistants are increasingly personalized to individual users. As AI personalization is dynamic and machine-learning-driven, we have limited understanding of how personalization affects interaction outcomes and user perceptions. We conducted a large-scale controlled experiment in which 1,000 participants interacted with AI assistants prompted to take on specific personality traits and opinions.
Blessing from Human-AI Interaction: Super Reinforcement Learning in Confounded Environments
arXiv:2209.15448v3 Announce Type: replace Abstract: As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an important priority. In this paper, we introduce the paradigm of super policy learning that takes advantage of Human-AI interaction for data driven sequential decision making. This approach utilizes the observed action, either from AI or humans, as input for...
Clinical Reasoning in the Age of AI: Longitudinal Cognition and Human-AI Collaboration
arXiv:2606.08442v1 Announce Type: new Abstract: As physicians turn to AI-powered systems to help meet the dual demands of speed and care quality, they are met with hallucinations and sycophancy. Understanding how doctors reason through clinical problems in real-world settings is critical for design of effective AI reasoning systems. While recent advances in medical AI have emphasized performance benchmarks and diagnostic accuracy, comparatively little attention has been paid to the structure...
Tree-Based Formalization of Multi-Agent Complementarity in Human-AI Interactions
Announce Type: new Abstract: Complementarity is the case in which a human--AI interaction (HAI) outperforms the best prediction benchmark available among its members. Although this idea is central in HAI research, formal work on complementarity remains limited. Existing frameworks do not model how agents' predictions compose into workflow-sensitive multi-agent protocols.
Conceptualising Reflective Use: Toward A Process Perspective On Human-AI Interaction
Announce Type: new Abstract: The rapid diffusion of generative artificial intelligence (genAI) systems reshapes how individuals engage with information systems, requiring users to monitor, assess, and adapt their interaction with non-deterministic systems. Existing constructs capture elements of this engagement but do not account for the situated dynamics of the entire evaluative process in genAI use. This research-in-progress, situated in a larger endeavour towards a scale development,...
Boosting metacognition in entangled human-AI interaction to navigate cognitive-behavioral drift
arXiv:2602.01959v2 Announce Type: replace Abstract: People navigate complex environments using cues, heuristics, and other strategies, which are often adaptive in stable settings. However, as AI increasingly permeates society's information environments, those become more adaptive and evolving: LLM-based chatbots participate in extended interaction, maintain conversational histories, mirror social cues, and can hypercustomize responses, thereby shaping not only what information is accessed...
The Human-AI Delegation-Verification Dilemma: Individual Strategies, Collective Equilibria and Sociotechnical Lock-in
Announce Type: replace Abstract: This paper takes an ecological approach toward large-scale models of hybrid human-AI intelligence. Emerging models of human-AI interaction predominantly advance the complementarity thesis variously dubbed human-AI collaboration and human-AI hybrid intelligence. However, this constitutes an over-simplification of the modalities of human-AI interaction and possibility-space for both individual and collective action that human-AI interaction potentiates.
A Model of Integrated Information Processing in Human-AI Interaction
arXiv:2606.07283v1 Announce Type: new Abstract: For Human-AI Interaction (HAII) research to move forward, theoretical work linking psychological mechanisms to interface design is needed. Such work should extend rather than replace established HCI and automation research, adapting to the increasing autonomy and agency of AI systems. Building on prior frameworks focused on roles and levels in human interaction with automation, a gap remains from a psychological view: a task-centered,...
Where's the Structure? A Systematic Literature Review of Empirical Research on Human-AI Collaboration and Hybrid Intelligence for Learning
arXiv:2606.05222v1 Announce Type: new Abstract: Artificial intelligence (AI) has been applied across educational contexts to support learning. One approach to such support is "human-AI collaboration" (also termed "hybrid intelligence"), where human(s) and AI components interact to promote human learning. However, as in human-to-human computer-supported collaborative learning (CSCL), unstructured interaction does not necessarily produce an effective learning experience.
Rationalize: Shared Semantic Reasoning for Human-AI Alignment
new Abstract: We introduce Rationalize, a role-pair framework for shared semantic reasoning between humans and AI models in data-driven sensemaking. Building on ideas in human-machine teaming and critical thinking, we conceptualize human-AI interaction as a series of complementary role pairs (Explorer-Guide, Investigator-Informant, Teacher-Student, Judge-Advocate) operating in a shared reasoning space. In this space, human analysts and AI models (such as LLMs) make purposes, questions,...