应用大语言模型提升知识工作者绩效-以学术论文评审为例 Enhancing The Performance of Knowledge  Workers Through Large  Language Models: A  Case Study of Academic Paper Peer Review in  Scholarly Services
	ISBN: 9789083644615
	DOI: 10.55578/book.2605.002
	Published: May 2026

Book Introduction:

This book focuses on the application and effectiveness of LLM-driven AI tools in the peer review process for academic manuscripts. It systematically examines the impact of AI-assisted review on efficiency, accuracy, quality, cognitive load, and user experience, with the aim of providing both theoretical foundations and practical pathways for constructing a more efficient, scientific, and sustainable academic review ecosystem. Two empirical experiments were conducted to investigate both the independent effects of AI tools and the mechanisms of their collaboration with human reviewers. Based on large-sample quantitative and qualitative data, this study comprehensively reveals the advantages, limitations, and future directions of human-AI collaborative models.

Authors:  Guoxing Liu

书籍介绍

 本书聚焦于大语言模型（Large Language Models, LLMs）驱动的人工智能 AI 工具在学术论文同行评审流程中的应用效果，系统考察 AI 辅助对评审效率、准确性、质量、认知负荷以及用户体验的影响，为构建更高效、更科学、更可持续的学术评审生态提供理论依据和实践路径。研究采用了两项实证实验，分别探讨AI 工具的独立效应及其与人类评审员协作的机制，并基于大样本的定量与定性数据，全面揭示人机协同模式下的优势、局限与未来发展方向。

作者:  刘国兴