Problem
Research questions and friction points this paper is trying to address.
Human-AI interaction
unassisted task performance
dialogue acts
cognitive participation
problem-solving
Human-AI Conversational Behaviors Predict Unassisted Task Performance
🤖 AI Summary
This study addresses the concern that while AI assistance yields immediate benefits, it may degrade users' independent capabilities upon its removal. To investigate this, the authors introduce a teacher-student dialogue act framework and conduct two gamified experiments to quantify, for the first time, the predictive effects of human-AI dialogue acts on subsequent unassisted performance. The findings reveal that cognitive engagement is critical for sustaining long-term capability: actively articulating reasoning processes and explaining states significantly enhance independent performance, whereas directly requesting or providing answers exerts detrimental effects. By elucidating these dynamics, this work provides empirical foundations for designing AI systems that promote autonomous learning rather than fostering dependency.
📝 Abstract
Assistance from AI tools has supported and improved human performance across domains. However, recent research suggests that these immediate benefits may entail future costs, including diminished performance when AI assistance is no longer available. We study how human-AI interaction behaviors correlate with immediate and future unassisted task performance across two game-based problem-solving user studies ($n=139$ and $n=111$), using a \textit{dialogue act} framework adopted from a tutor-student dialogue taxonomy. In our studies, verbalizing thought processes correlates with higher unassisted outcomes, whereas directly requesting solutions correlates negatively. Similar to these participant-side patterns, assistant explanations of the current problem state are associated with better subsequent unassisted performance, whereas directly providing the next action is associated with worse performance. Qualitative and subtype analyses further show that ostensibly similar reasoning turns can elicit different assistance. Our findings suggest that preserving users' cognitive participation in problem-solving may support performance beyond AI-assisted interaction.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dialogue Act Framework
Human-AI Interaction
Unassisted Task Performance
Cognitive Participation
Problem-Solving
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