In mixed-initiative systems, the mode of AI assistance delivery can be as consequential as the assistance itself. We investigated two assistance delivery modes: on-demand help (users request via Button) and pre-scheduled help (assistance delivered at user-selected intervals, with user actions resetting the Timer). To evaluate these modes, we selected Rush Hour puzzles as the human-AI collaborative task because they capture elements of real-world problem solving such as analysis, resource management, and decision-making under constraints. To enhance ecological validity, we imposed monetary costs for both time and AI assistance, simulating scenarios where people must balance implicit or explicit trade-offs such as time pressure, financial limitations, or opportunity costs. Although task performance was comparable across modes, participants who used the pre-scheduled (Timer) mode reported more positive perceptions of the AI, even when their ending budget was low. This suggests that assistance delivery mode can shape user experience independent of task outcomes, indicating that human-AI systems may need to consider how AI assistance is delivered alongside improving task performance.

翻译：在混合主动式系统中，人工智能辅助的提供方式可能与其辅助本身同等重要。我们研究了两种辅助提供模式：按需帮助（用户通过按钮请求）和预定时帮助（在用户选择的时间间隔提供辅助，用户操作会重置计时器）。为评估这些模式，我们选择“Rush Hour”益智游戏作为人机协作任务，因为它捕捉了现实世界问题解决中的分析、资源管理和约束条件下决策等要素。为提高生态效度，我们对时间和人工智能辅助均设置了金钱成本，模拟人们必须平衡时间压力、财务限制或机会成本等隐性或显性权衡的场景。尽管各模式间的任务表现相当，但使用预定时（计时器）模式的参与者对人工智能的感知更为积极，即使他们的最终预算较低。这表明辅助提供方式能够独立于任务结果塑造用户体验，意味着人机系统在提升任务表现的同时，可能需要考虑人工智能辅助的提供方式。