Precision Proactivity: Measuring Cognitive Load in Real-World AI-Assisted Work

Abstract

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.

Questions this source addresses

• How can intrinsic and extraneous cognitive load be estimated from transcripts of real-world AI-assisted knowledge work?
• How does AI-generated content usage compensate for cognitive load, and why is this compensation only partial?
• How does professional expertise moderate the relationship between cognitive load and performance benefits from AI assistance?
• Why does extraneous cognitive load have a much larger negative association with task performance than intrinsic load in AI-assisted work?