arXiv:2606. 00182v1 Announce Type: cross Abstract: Human-AI collaboration is considered the most promising way to incorporate AI in the workplace.
arXiv:2607. 27179v1 Announce Type: cross Abstract: Conversational AI is increasingly positioned as a teammate rather than a tool, yet we know little about how its presence reshapes communication among the humans on the team.
arXiv:2606. 09833v1 Announce Type: cross Abstract: AI agents are reshaping the workspace, leading to drastic change of how humans work.
arXiv:2607. 17826v1 Announce Type: cross Abstract: Despite the growing availability of customizable social artificial intelligence (AI), such as ChatGPT, Grok, and Character.
Conversational AI is increasingly positioned as a teammate rather than a tool, yet we know little about how its presence reshapes communication among the humans on the team. We examined sociocognitive communication dynamics in team decision-making using Group Communication Analysis (GCA), team surveys, and lexical analyses of team discourse.
The study investigates how users switch roles in a human‑AI chess collaboration, using multimodal behavioral signals such as gaze and task‑specific features. Participants mostly retained their roles, but when they switched they showed more exploratory gaze and poorer move quality. A classifier trained on these signals achieved a PR‑AUC of 0.56, indicating that behavioral cues can predict role switches.
arXiv:2608. 00818v2 Announce Type: replace Abstract: The discovery of scaling laws has highlighted the extraordinary potential of AI systems with a striking empirical pattern: as AI systems scale, their capabilities tend to improve predictably.
The study investigates when work done with AI feels like one's own, using a qualitative survey where participants described tasks that felt owned versus not owned. Findings show that ownership depends on the collaboration process: people feel ownership when they lead, iterate, or rewrite, but disown work when merely approving AI suggestions. Ownership also extends to tasks where people set the vision but rely on AI for execution, yet loss of personal voice and lack of comprehension erode ownership, and willingness to disclose AI use is driven more by community norms than by pride.
arXiv:2606. 09832v1 Announce Type: cross Abstract: As AI systems evolve from single conversational agents to complex multi-agent architectures, a critical design dimension has been overlooked: how the social identity of individual agents shapes human behavior within the collaboration.
Despite the growing availability of customizable social artificial intelligence (AI), such as ChatGPT, Grok, and Character. ai, we know little about how users actively shape social AI to reflect their personal preferences.
arXiv:2608. 11322v1 Announce Type: cross Abstract: Human-AI research often evaluates individual capabilities, combined performance, or final outputs, but these approaches do not preserve how one party's response becomes part of the conditions under which the other party's next contribution is formed.
arXiv:2512. 12413v2 Announce Type: replace Abstract: Generative AI tools are increasingly embedded in everyday work and learning, yet their fluency, opacity, and propensity to hallucinate mean that users must critically evaluate AI outputs rather than accept them at face value.
arXiv:2606. 03544v1 Announce Type: new Abstract: Self-improving language agents are typically evaluated in isolation: an agent attempts a task, receives feedback, and iteratively refines its own behavior.