Precision Proactivity: Measuring Cognitive Load in Real-World AI-Assisted Work
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915840607125504 |
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| author | Lepine, Brandon Kim, Juho Mishkin, Pamela Beane, Matthew |
| author_facet | Lepine, Brandon Kim, Juho Mishkin, Pamela Beane, Matthew |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10742 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Precision Proactivity: Measuring Cognitive Load in Real-World AI-Assisted Work Lepine, Brandon Kim, Juho Mishkin, Pamela Beane, Matthew Artificial Intelligence Human-Computer Interaction K.4.2; K.4.3 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. |
| title | Precision Proactivity: Measuring Cognitive Load in Real-World AI-Assisted Work |
| topic | Artificial Intelligence Human-Computer Interaction K.4.2; K.4.3 |
| url | https://arxiv.org/abs/2505.10742 |