生成式人工智能视域下新闻编译课程学生人机协同能力培养路径研究

Research on the Cultivation Path of Human-Machine Collaboration Ability in News Translation Course under the Perspective of Generative Artificial Intelligence

摘要: 本文基于某高校《英汉新闻编译》课程实践，考察生成式人工智能介入下学生能力的变化。研究发现，在任务结构发生变化的条件下，学生逐步从以语言转换为主的操作转向包含多环节选择的协同实践，其变化主要体现在多源信息的取舍与新闻价值判断、提示表达与人机分工安排、对生成内容的核查与内容取向审视，以及围绕特定受众展开的再叙事与表达调整等方面。在技术参与的过程中，学生对使用边界与基本规范的理解也有所加深。本文据此构建新闻编译课堂的人机协作能力框架。结果表明，生成式人工智能并未削弱新闻训练，反而在适当教学设计下促发专业判断与职业责任意识，最后提出以能力生成为导向的人机协同培养路径。

Abstract: This paper examines the changes in students’ abilities under the intervention of generative artificial intelligence (AI) in a university’s “English-Chinese News Translation” course. Research has found that, under changing task structures, students gradually shift from primarily language-based operations to collaborative practices involving multiple choices. These changes are mainly reflected in the selection of multi-source information and judgment of news value, prompting and expression, human-machine division of labor, verification of generated content and examination of content orientation, and re-narrative and expression adjustments centered around specific audiences. During the process of technological participation, students also deepen their understanding of usage boundaries and basic norms. Based on this, this paper constructs a human-machine collaboration ability framework for news translation classes. The results show that generative artificial intelligence does not weaken news training; on the contrary, with appropriate instructional design, it fosters professional judgment and a sense of professional responsibility. Finally, a human-machine collaboration cultivation path oriented towards ability generation is proposed.