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| International Experiences and China’s Pathways to Research Integrity Governance in STM Journals in AI Era |
| ZHANG Qin |
| School of Foreign Languages, Hubei University, 430062, Wuhan, China |
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Abstract Scientific, technical, and medical (STM) journals in China are currently experiencing a significant transition toward high-quality development while confronting increasingly complex challenges in the artificial intelligence (AI) era. AI Advances have transformed research methodologies, evaluation processes, and dissemination practices, presenting both opportunities and potential risks for research integrity. The research integrity governance approaches implemented by leading international STM journals provide valuable insights in this context. These journals have accumulated substantial experiences in addressing AI-era challenges, providing valuable lessons for establishing a research integrity governance framework tailored to China’s specific academic and institutional context. This paper systematically reviews the research integrity governance practices across 265 mainstream STM journals in the AI era, analyzing their regulatory mechanisms, technological strategies, and ethical frameworks. The analysis reveals that international journals and publishing institutions implement a comprehensive approach to research integrity governance. This includes establishing detailed and precise regulatory rules, deploying advanced technological detection and verification tools, and developing shared ethical norms across the academic community. These measures are designed to mitigate risks while simultaneously fostering academic innovation. The resulting governance framework operates on multiple levels, incorporating hierarchical management structures that range from full-process oversight to targeted supervision at critical stages of the research and publication process. Additionally, governance measures are adjusted according to disciplinary sensitivity and specific risk profiles, ensuring intensive monitoring of high-risk areas. A notable innovation in many international practices is the integration of human–machine collaborative defense mechanisms, combining automated detection technologies with expert assessment to establish a robust, adaptive, and scalable model for maintaining research integrity. Based on these insights, this paper proposes three strategic priorities for developing China’s research integrity governance system for STM journals. First, it emphasizes establishing comprehensivefoundational governance frameworks that clearly define responsibilities, standards, and workflows across all stages of the research and publication lifecycle. Second, it highlights the importance of proactive ethical cultivation, embedding research integrity and professional norms into the core academic culture of journals. Third, it underlines the necessity of aligning policies and standards with both national regulations and international best practices to enhance coherence, comparability, and credibility. The development of an agile governance system requires strategic planning across three dimensions: promoting multi-stakeholder collaboration among publishers, researchers, and institutions to integrate diverse expertise; strengthening institutional and technical capacities to address emerging risks; and fostering technological innovation to improve the efficiency, accuracy, and reliability of research integrity monitoring. By learning from the structured, technology-enabled, and ethically grounded approaches of leading international journals, China can establish a research integrity governance system that addresses AI-era challenges while reflecting its national characteristics. This system would serve to safeguard scientific credibility and integrity while strengthening the global competitiveness and impact of STM publications in China.
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Published: 15 October 2025
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| 学科类型 | 代表期刊 | AI使用边界 | 核心管控目标 | | 自然科学 | Cell、Nature | 允许文献检索辅助,需声明训练数据集 | 维护研究方法透明度 | | 医学健康 | The BMJ | 允许语言润色,禁止结果解释 | 保障临床决策可靠性 | | 工程技术 | IEEE TPAMI | 允许算法辅助数据处理,要求代码开源 | 确保实验的可复现性 |
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