【摘要】 生成式人工智能技术的快速发展正深刻改变着大学生的学习方式，由此如何有效评估并提升大学生应用生成式人工智能学习的能力成为关键问题，但当前鲜有研究对这种能力的组成要素和评价工具进行探析。为此，文章首先运用扎根理论，构建了大学生应用生成式人工智能学习模型。随后，文章通过德尔菲法与层次分析法，构建了大学生应用生成式人工智能学习能力评价指标体系，并确定了各指标的权重。最后，文章将该评价指标体系应用于实际学习情境中，发现该评价指标体系能够有效评价大学生应用生成式人工智能学习的能力，并为大学生能力的自我提高和教师有针对性地指导不同类别的大学生提供依据。更多还原

【Abstract】 The rapid development of generative artificial intelligence technology is profoundly changing the learning methods of college students. How to effectively evaluate and enhance college students’ learning ability in applying generative artificial intelligence has become a key issue. However, there are currently few studies exploring the components and evaluation tools of this ability. Therefore, this paper first applied grounded theory to construct a learning model for college students in applying generative artificial intelligence. Subsequently, using the Delphi method and analytic hierarchy process, an evaluation index system for college students’ learning ability in applying generative artificial intelligence was constructed, and the weights of each index were determined. Finally, the evaluation index system was applied in actual learning situations. The research results indicated that the evaluation index system can effectively evaluate college students’ learning ability in applying generative artificial intelligence, providing basis for the self-improvement of college students’ abilities and teachers’ ability to deliver differentiated guidance tailored to distinct college students.更多还原