Understanding Mode Switching in Human-AI Collaboration: Behavioral Insights and Predictive Modeling

• URL: http://arxiv.org/abs/2509.20666v1
• Date: Thu, 25 Sep 2025 01:58:46 GMT
• Title: Understanding Mode Switching in Human-AI Collaboration: Behavioral Insights and Predictive Modeling
• Authors: Avinash Ajit Nargund, Arthur Caetano, Kevin Yang, Rose Yiwei Liu, Philip Tezaur, Kriteen Shrestha, Qisen Pan, Tobias Höllerer, Misha Sra,
• Abstract summary: We investigate how users dynamically switch between higher and lower levels of control during a sequential decision-making task.<n>We collect over 400 mode-switching decisions from eight participants, along with gaze, emotional state, and subtask difficulty data.<n>We train a lightweight model that predicted control level switches.
• Score: 16.20562194559668
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-AI collaboration is typically offered in one of two of user control levels: guidance, where the AI provides suggestions and the human makes the final decision, and delegation, where the AI acts autonomously within user-defined constraints. Systems that integrate both modes, common in robotic surgery or driving assistance, often overlook shifts in user preferences within a task in response to factors like evolving trust, decision complexity, and perceived control. In this work, we investigate how users dynamically switch between higher and lower levels of control during a sequential decision-making task. Using a hand-and-brain chess setup, participants either selected a piece and the AI decided how it moved (brain mode), or the AI selected a piece and the participant decided how it moved (hand mode). We collected over 400 mode-switching decisions from eight participants, along with gaze, emotional state, and subtask difficulty data. Statistical analysis revealed significant differences in gaze patterns and subtask complexity prior to a switch and in the quality of the subsequent move. Based on these results, we engineered behavioral and task-specific features to train a lightweight model that predicted control level switches ($F1 = 0.65$). The model performance suggests that real-time behavioral signals can serve as a complementary input alongside system-driven mode-switching mechanisms currently used. We complement our quantitative results with qualitative factors that influence switching including perceived AI ability, decision complexity, and level of control, identified from post-game interview analysis. The combined behavioral and modeling insights can help inform the design of shared autonomy systems that need dynamic, subtask-level control switches aligned with user intent and evolving task demands.

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