Examining Transparency Needs and Display Features: An Empirical Study of Dynamic Dual-Task HAT Simulation with AI Integration

Abstract

This study investigates how various task allocation strategies and communication modalities influence Human-AI Team performance in dynamic dual-task environments. Utilizing a modified Ballas Task simulator, the research involved 32 participants with military or similar backgrounds to evaluate four task allocation strategies – Operator Monitoring, Action Split, Target Split, and Take Over. These strategies were assessed under different levels of transparency and communication modes while participants engaged in concurrent tracking and tactical assessment tasks. The results indicate that the effectiveness of task allocation is contingent upon both transparency and the communication modality employed. Operator monitoring consistently reduced workload and assessment time, while enhancing tracking performance, target accuracy, and situational awareness – particularly when paired with verbal communication under low transparency. In contrast, non-verbal communication improved situational awareness and reduced tracking errors in the action split and target split strategies. Although take over yielded fewer advantages compared to the other strategies, it still surpassed the performance of an all-human baseline. These findings provide actionable insights for enhancing human-AI collaboration in high-stakes, time-sensitive contexts, with relevance extending beyond military operations to a variety of dynamic dual-task environments.