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.
Cite
@article{arxiv.2505.10742,
title = {Precision Proactivity: Measuring Cognitive Load in Real-World AI-Assisted Work},
author = {Brandon Lepine and Juho Kim and Pamela Mishkin and Matthew Beane},
journal= {arXiv preprint arXiv:2505.10742},
year = {2026}
}
Comments
Major Revision. Substantially rewritten manuscript with new analysis, updated framing, and new title