研究生学术能力在生成式人工智能环境下的提升路径

The Path of Improving the Academic Ability of Graduate Students in the Generative Artificial Intelligence Environment

摘要: 生成式人工智能的快速发展为研究生学术能力培养带来了深刻变革，也引发了关于研究生学术能力的技术赋能与学术风险的广泛讨论。生成式人工智能在文献检索综述、研究方法设计与学术成果写作等环节具有显著的辅助价值，能够有效提升研究生的学术效率；但同时也存在工具依赖、学术诚信与学科差异等潜在风险，尤其当前端使用介入核心能力训练环节时危害更为突出。为此，应从个体使用策略规范、评价方案改革、技术工具优化三个层面构建协同治理路径，引导研究生在人工智能环境中实现学术能力的实质性提升。

Abstract: The rapid development of generative artificial intelligence has brought profound changes to graduate students’ academic ability training, and has also sparked extensive discussion on the technical empowerment and academic risk. Generative artificial intelligence has significant value in literature search, research method design and academic achievement writing, and can effectively improve the academic efficiency of graduate students. However, there are also potential risks such as tool dependence, academic integrity, and disciplinary differences, especially when the current use of core competence training is more harmful. Therefore, we should construct a collaborative governance path from individual use strategy, evaluation plan reform, and technical tool optimization to guide graduate students to achieve substantial improvement of academic ability in the artificial intelligence environment.