Systems like ChatGPT and Claude assist billions through proactive dialogue-offering unsolicited, task-relevant information. Drawing on Cognitive Load Theory, we study how cognitive load shapes performance in AI-assisted knowledge work. We recruited 34 financial professionals to complete a complex valuation task using GPT-4o and developed a transcript-based framework estimating intrinsic and extraneous load from computational indicators anchored in a task decomposition and knowledge graph. Across 1,178 participant-subtask observations, AI-generated content usage is positively associated with quality, while extraneous load shows the largest negative association-roughly three times that of intrinsic load. Mediation reveals a compensatory pathway partially offsetting but not eliminating load-related deficits. Extraneous load persists within speakers and spills asymmetrically to model responses. Model-initiated task switching is the strongest predictor of decline. Expertise moderates these dynamics: less experienced professionals face larger penalties and derive greater marginal gains from AI-generated content, yet are not those who most increase uptake under load.

翻译：诸如ChatGPT和Claude等系统通过主动对话——提供未经请求但任务相关的信息——为数十亿用户提供协助。基于认知负荷理论，我们研究了认知负荷如何影响AI辅助知识工作中的表现。我们招募了34名金融专业人士，要求他们使用GPT-4o完成一项复杂的估值任务，并开发了一个基于对话记录的评估框架。该框架通过以任务分解和知识图谱为基础的计算指标，估算内在认知负荷与外在认知负荷。通过对1,178项参与者-子任务观察数据的分析，我们发现AI生成内容的使用与任务质量呈正相关，而外在负荷则显示出最大的负向关联——其影响程度约为内在负荷的三倍。中介效应分析揭示了一种补偿性路径，该路径能够部分抵消但无法完全消除负荷相关的绩效损失。外在负荷不仅在同一位发言者内部持续存在，还会不对称地扩散至模型响应中。模型发起的任务切换是导致绩效下降的最强预测因子。专业经验调节了这些动态关系：经验较少的从业者面临更大的绩效惩罚，但从AI生成内容中获得的边际收益也更高；然而，在负荷状态下，他们并非最显著增加AI内容使用率的群体。