The Impact of Generative Artificial Intelligence Tools on Knowledge Worker Productivity: A Study of AI-Assisted Writing, Coding, and Research Tasks

Abstract

Background: Generative Artificial Intelligence (AI) tools such as large language models have rapidly transformed knowledge-intensive work by assisting in tasks including writing, coding, and information synthesis. Despite their increasing adoption, empirical understanding of how these tools influence productivity, efficiency, and task quality among knowledge workers remains limited.

Objective: This study aims to investigate the impact of generative AI tools on the productivity and performance of knowledge workers engaged in writing, programming, and research-related tasks.

Methods: A mixed-method research design will be employed, combining quantitative productivity measurements with qualitative insights from surveys and semi-structured interviews. Participants will complete selected tasks both with and without AI assistance. Task completion time, output quality, and user satisfaction will be analyzed using statistical methods to determine productivity differences.

Results: The study is expected to demonstrate that generative AI tools significantly reduce task completion time while improving the quality and coherence of outputs in writing and research tasks. Coding productivity is anticipated to increase through automated code suggestions and debugging assistance.

Conclusion: Generative AI technologies have the potential to substantially augment human capabilities in knowledge work. However, their integration requires careful consideration of issues related to skill dependency, ethical use, and accuracy of generated content.