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

Avinash Ajit Nargund; Arthur Caetano; Kevin Yang; Rose Yiwei Liu; Philip Tezaur; Kriteen Shrestha; Qisen Pan; Tobias Höllerer; Misha Sra

arXiv · 2509.20666

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

Avinash Ajit Nargund·Arthur Caetano·Kevin Yang·Rose Yiwei Liu·Philip Tezaur·Kriteen Shrestha·Qisen Pan·Tobias Höllerer·Misha Sra

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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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Avinash Ajit Nargund, Arthur Caetano, Kevin Yang, Rose Yiwei Liu, Philip Tezaur, Kriteen Shrestha, Qisen Pan, Tobias Höllerer, Misha Sra. 2025-09-25. Understanding Mode Switching in Human-AI Collaboration: Behavioral Insights and Predictive Modeling. https://arxiv.org/abs/2509.20666

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