摘要

生成式人工智能在教学内容生成、学习支持、作业反馈与课堂互动等环节的快速普及，使大学教育在获得效率提升空间的同时面临新的课堂治理与质量保障挑战。本文从机遇与风险的双重视角出发指出：生成式AI有助于提升学习支持与反馈效率，促进差异化学习与自主学习，并优化课堂互动与学习体验；但其也可能引发学术诚信风险与学习成果真实性弱化、学习主体性削弱与认知投入不足，以及生成内容可靠性不足导致的核验成本上升等问题。基于高校教师课堂实践需求，本文提出生成式AI融入大学课堂的实施路径：构建允许—限制—禁止的情境化使用规则，推动任务设计由“直接生成”转向“可评价的学习活动”，以过程性证据建设重建学习证据链，并通过形成性评价与结果评价的组合引导学生投入真实学习过程，同时配套教师发展、资源配置与制度规范等支持保障，以实现生成式AI在大学教育中的有序应用与质量提升。

Abstract

The rapid proliferation of generative artificial intelligence in areas such as teaching content generation, learning support, assignment feedback, and classroom interaction has provided university education with opportunities for enhanced efficiency while simultaneously posing new challenges in classroom governance and quality assurance. From the dual perspective of opportunities and risks, this paper points out that generative AI can help improve the efficiency of learning support and feedback, promote differentiated and self-directed learning, and optimize classroom interaction and learning experience. However, it may also lead to risks such as academic integrity issues and the weakening of learning outcome authenticity, the diminishment of learner agency and insufficient cognitive engagement, as well as increased verification costs due to insufficient reliability of generated content. Based on the practical needs of university teachers in classroom instruction, this paper proposes implementation pathways for integrating generative AI into university classrooms: establishing contextualized usage rules encompassing "permitted," "restricted," and "prohibited" scenarios; shifting task design from "direct generation" towards "evaluable learning activities"; reconstructing the learning evidence chain through the collection of procedural evidence; and guiding students to engage in authentic learning processes through a combination of formative and summative assessments. Concurrently, supporting safeguards such as teacher development, resource allocation, and institutional norms should be implemented to achieve the orderly application and quality enhancement of generative AI in university education.

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