Governing Artificial Intelligence in the Evolving Academic Publishing Ecosystem
Abstract
As scholarly publishing navigates the rapid integration of artificial intelligence (AI), encompassing generative AI, natural language processing (NLP), large language models (LLMs), and autonomous agents, the editorial ecosystem must adapt to maintain its core integrity. This editorial evaluates the operational advantages of AI, such as enhanced efficiency and automated integrity checks, against systemic risks, including hallucination, bias, and confidentiality breaches. Drawing on recent empirical validations and policy frameworks from the Committee on Publication Ethics (COPE), the International Committee of Medical Journal Editors (ICMJE), and major publishers, we outline a comprehensive editorial policy for the Journal of Metaverse. This policy emphasizes human oversight, transparency, and domain-specific rigor. We also introduce practical implementation mechanisms, including an AI-use disclosure template, auditing protocols, and specific reporting requirements for metaverse research. Furthermore, this editorial explores the strategic use of generative AI for post-publication science communication, demonstrating how automated infographics and multimedia summaries can significantly enhance research visibility and broad accessibility. This document serves as both a functional policy framework and a contribution to the discourse on responsible AI integration in academic publishing.
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.