How Users Perceive Mixed-Initiative AI: Attitudes Toward Assistance in Problem Solving
🤖 AI Summary
This study investigates how the modality of assistance provision in hybrid proactive AI systems influences users’ perceptions and attitudes toward AI, independently of task performance. Using the Rush Hour puzzle task, the research compares two assistance modalities—on-demand (button-triggered) versus pre-scheduled (timer-based)—within a realistic problem-solving context that incorporates time and monetary cost constraints. A controlled experiment combining behavioral economics incentives, performance metrics, and post-task surveys reveals that, despite equivalent task performance across conditions, the pre-scheduled assistance significantly enhances users’ positive evaluations of the AI, even when it results in a lower final budget. These findings underscore the critical role of interaction design in human-AI collaboration and offer a novel perspective for optimizing AI assistance strategies beyond mere performance outcomes.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
mixed-initiative AI
assistance delivery mode
user perception
human-AI collaboration
problem solving
Innovation
Methods, ideas, or system contributions that make the work stand out.
mixed-initiative AI
assistance delivery mode
ecological validity
user perception
human-AI collaboration
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