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This research investigates the dynamics of concurrent human-agent collaboration in shared co-creative workspaces, revealing how professional designers dynamically shift between observation and intervention based on agent reliability. The study introduces a decision model and design implications for systems that can track user actions to selectively update their execution plans without disrupting creative flow. These findings are critical for advancing Human-AI interaction frameworks where seamless, adaptive partnership is essential for complex generative tasks.
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"When to Hand Off, When to Work Together": Understanding Concurrent Human-Agent Interaction in Shared Co-Creative Workspaces

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2603.02050v5 Announce Type: replace-cross Abstract: Shared co-creative workspaces allow users to contribute while agents execute. Yet how users adjust their participation as their work and the agent's execution shape one another remains less understood. We conducted two design probe studies with professional designers. In Study 1 (N=10), participants identified opportunities to contribute by observing the agent's progress, but avoided intervention when the agent misinterpreted their edits. Therefore, we developed CLEO, a second probe that tracks user actions and selectively updates its plan. In Study 2 (N=12), we analyzed 255 interaction turns and stimulated recall interviews, identifying five action categories, ten codes, six triggers, and four enabling factors. Participants engaged in concurrent interaction during 33.73% of turns, expressing newly recognized design ideas or preferences through edits and changing the division of work by completing pending subtasks or independently developing copies of unfinished outputs. We present a decision model and five design implications for concurrent human-agent collaboration.

Abstract:Shared co-creative workspaces allow users to contribute while agents execute. Yet how users adjust their participation as their work and the agent's execution shape one another remains less understood. We conducted two design probe studies with professional designers. In Study 1 (N=10), participants identified opportunities to contribute by observing the agent's progress, but avoided intervention when the agent misinterpreted their edits. Therefore, we developed CLEO, a second probe that tracks user actions and selectively updates its plan. In Study 2 (N=12), we analyzed 255 interaction turns and stimulated recall interviews, identifying five action categories, ten codes, six triggers, and four enabling factors. Participants engaged in concurrent interaction during 33.73% of turns, expressing newly recognized design ideas or preferences through edits and changing the division of work by completing pending subtasks or independently developing copies of unfinished outputs. We present a decision model and five design implications for concurrent human-agent collaboration.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2603.02050 [cs.HC]

(or arXiv:2603.02050v5 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2603.02050 Focus to learn more arXiv-issued DOI via DataCite

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