AIGC Empowering Model Innovation and Practical Pathways for Scientific Journals

Abstract

With the rapid advancement of Artificial Intelligence Generated Content (AIGC) technology, the scientific journal publishing industry is facing unprecedented opportunities for transformation. AIGC technology not only optimizes publishing workflows and enhances efficiency but also holds the potential to reshape the production, dissemination, and operational models of scientific journals. This paper first systematically analyzes the pain points inherent in traditional journal models, including lengthy peer-review cycles, difficulties in filtering knowledge overload, limited dissemination reach, and singular service models. Building on this analysis, the paper constructs a “Three-Wheel Drive” model for AIGC-empowered scientific journals, elaborating on how AIGC drives model innovation across three core areas: content production and peer review, knowledge aggregation and dissemination, and platform reconstruction and services. Furthermore, the paper proposes specific practical pathways: at the content level, utilizing AIGC to assist with topic selection, literature review, language polishing, and preliminary manuscript screening; at the knowledge level, building intelligent summarization, cross-modal interpretation, and personalized recommendation systems based on AIGC; at the platform level, exploring new service models such as “intelligent Q&A knowledge bases” and “open science collaborative integration.” Finally, the paper dialectically discusses the challenges encountered in this process, including academic ethics, intellectual property, and technological dependency, and proposes corresponding governance strategies. This research aims to provide theoretical reference and practical guidance for the transformation and upgrading of scientific journals in the intelligent era.