BU You-Quan, CAO Yong-Fu, CHANG Zeng-Yi, CHEN Hong-Yu, CHEN Xiao-Wei, CHEN Yuan-Yuan, CHEN Zhu-Cheng, DENG Rui, DING Jie, FAN Zhong-Kai, GAO Guo-Quan, GAO Xu, HU Lan, HU Xiao-Qing, JIA Hong-Ti, KONG Ying, LI En-Min, LI Ling, LI Yu-Hua, LIU Jun-Rong, LIU Zhi-Qiang, LUO Ya-Ping, LV Xue-Mei, PEI Yan-Xi, PENG Xiao-Zhong, TANG Qi-Qun, WAN You, WANG Yong, WANG Ming-Xu, WANG Xian, XIE Guang-Kuan, XIE Jun, YAN Xiao-Hua, YIN Mei, YU Zhong-Shan, ZHOU Chun-Yan, ZHU Rui-Fang, Editorial Department of Acta Anatomica Sinica, Editorial Department of Chemistry of Life
With the rapid development of generative artificial intelligence (GAI) technologies, their widespread application in academic research and writing is continuously expanding the boundaries of scientific inquiry. However, this trend has also raised a series of ethical and regulatory challenges, including issues related to authorship, content authenticity, citation accuracy, and accountability. In light of the growing involvement of AI in generating academic content, establishing an open, controllable, and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community. This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing—including topic selection, data management, citation practices, and authorship attribution. It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing, ensuring that technological tools enhance efficiency without compromising integrity. The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.