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arXiv · 2602.08403

Intelligent support for Human Oversight: Integrating Reinforcement Learning with Gaze Simulation to Personalize Highlighting

Thorsten Klößner·João Belo·Zekun Wu·Jörg Hoffmann·Anna Maria Feit
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Abstract

Interfaces for human oversight must effectively support users' situation awareness under time-critical conditions. We explore reinforcement learning (RL)-based UI adaptation to personalize alerting strategies that balance the benefits of highlighting critical events against the cognitive costs of interruptions. To enable learning without real-world deployment, we integrate models of users' gaze behavior to simulate attentional dynamics during monitoring. Using a delivery-drone oversight scenario, we present initial results suggesting that RL-based highlighting can outperform static, rule-based approaches and discuss challenges of intelligent oversight support.

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Thorsten Klößner, João Belo, Zekun Wu, Jörg Hoffmann, Anna Maria Feit. 2026-02-09. Intelligent support for Human Oversight: Integrating Reinforcement Learning with Gaze Simulation to Personalize Highlighting. https://arxiv.org/abs/2602.08403

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