人机对话行为预测无辅助任务表现

中文摘要 AI 辅助

AI工具的辅助已在多个领域支持并提升了人类表现。然而，近期研究表明，这些即时益处可能带来未来代价，包括在AI辅助不再可用时表现下降。我们通过两项基于游戏的问题解决用户研究（n=139和n=111），采用从导师-学生对话分类学中借鉴的“对话行为”框架，研究人机交互行为如何与即时及未来的无辅助任务表现相关联。在我们的研究中，言语化思考过程与更高的无辅助结果相关，而直接请求解决方案则呈负相关。与参与者侧模式类似，助手对当前问题状态的解释与后续更好的无辅助表现相关，而直接提供下一步行动则与较差表现相关。定性和子类型分析进一步表明，表面相似的推理轮次可能引发不同的辅助。我们的发现表明，在问题解决中保留用户的认知参与可能支持超越AI辅助交互的表现。

英文摘要

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.

发表机构

• Columbia University（哥伦比亚大学）
• Together AI（Together AI公司）
• Stanford University（斯坦福大学）
• Cornell University（康奈尔大学）

机构由 AI 辅助整理，请以论文原文为准。