生成式人工智能对学生学习成果的影响
魏传佳
(泉州轻工职业学院福建泉州362200)
[摘要]:自2022年末ChatGPT推出以来，生成式人工智能已对各行各业产生重大影响。当前高等教育领域的现有研究多聚焦于生成式人工智能对教师和院校的影响，相比之下，关于该技术对学生影响的研究则较为有限。本研究旨在探究生成式人工智能如何影响高等教育阶段学生的学习成果。研究采用准实验视角，对192份学生反思报告的定性数据进行分析,并运用定量内容分析(QCA)方法。结果表明:当学生使用生成式人工智能进行知识构建与扩充时(掌握式方法),其学习层次更高;反之，若仅以项目式方式使用生成式人工智能而不进行知识扩充(项目式方法),则学生的学习成果层次较低。从实践角度来看，课程大纲可融入生成式人工智能相关设计，为学生搭建从基础知识构建任务到复杂知识扩充的学习支架。评估设计可调整为强化掌握目标结构，鼓励学生对生成式人工智能的输出结果进行批判性思考，而非简单复述，从而实现最优学习成果。
[关键词]:学习成果;生成式人工智能;高等教育;目标结构;定量内容分析
The Impact of Generative Artificial Intelligence on
Students Learning Outcomes
Chuanjia Wei
Quanzhou College of Technology, Quanzhou, Fujian 362200, P. R. ChinaE-mail: 15080406979@163.com; weichuanjia1983@gmail.com
[Abstract]Since the launch of ChatGPT at the end of 2022, generative artificial intelligence (GenAl) hasexerted profound impacts across all industries, Extant research in higher education predominantly focuses on theimplications of generative AI for instructors and institutions, while studies examining its effects on studentsremain relatively scarce. This study explores how generative artificial intelligence shapes undergraduate learningoutcomes, Adopting a quasi-experimental framework, the research analyzes qualitative data from 192 studentrefective reports via Quantitative Content Analysis (QCA). The results reveal that students achieve higher-orderlearning outcomes when they employ generative AI for knowledge construction and expansion (mastery-orientedapproach). In contrast, students who utilize generative AI merely for task completion without subsequentknowledge elaboration (prject-oriented apprach) demonstrate low-level learning performance. Practically, syllabican integrate GenAI-oriented instructional design to scaffold students' learning progresion from foundationalknowledge-building tasks to advanced knowledge expansion activities. Assessment frameworks can berestructured to prioritize mastery goal structures, prompting students to engage in critical evaluation rather thanpassive replication of Al-generated outputs, thereby optimizing their learning achievements.
[Keywords]: learning outcomes; generative artificial intelligence; higher education; goalstructure;Quantitative Content Analysis (QCA)
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