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Betty Tärning¹, Trond A Tjøstheim¹, Annika Wallin¹

¹Department of Philosophy and Cognitive Science, Lund University, Lund, Sweden.

Frontiers in artificial intelligence

|December 17, 2025

概括

大型语言模型 (LLM) 对学术成果的影响较弱. 然而,使用LLM进行修订改善了最终成绩,并且同行评审更喜欢LLM生成的文本以提高写作质量.

科学领域:

高等教育教学的教学.
教育中的人工智能
学术成绩分析学术成绩分析

背景情况:

大型语言模型 (LLM) 融入高等教育引发了关于它们对学生学习的影响的重大辩论.
现有的研究往往依赖于自我报告的数据,需要补充方法来评估LLM效应.
本研究通过分析课程成绩和同行评估来解决经验数据的需求.

研究的目的:

调查不同大型语言模型 (LLM) 使用策略对学生学术成果的影响.
为了比较使用LLMs进行文本生成/修订的学生和不使用LLMs的学生之间的学习成果.
评估同行对文本质量的看法以及在不同使用条件下检测LLM生成内容的能力.

主要方法:

一个涉及三组学生的准实验性设计:不使用LLM,LLM用于修订,LLM用于生成/修订.
学术成绩是通过中期考试成绩和最终课程成绩来衡量的.
进行了对文本质量的同行评估和LLM使用的分类准确性.

主要成果:

总体而言,LLM使用对学生学习成果的影响很弱.
与没有LLM的基线相比,使用LLMs进行修订的学生的最终成绩有所改善.
使用LLMs生成并随后由学生修改的文本在写作质量方面获得了更高的同行评分.

结论:

虽然LLM整合的整体影响有限,但特定的策略,如使用LLM进行修订,可以有利于学术表现.
同行评审者对LLM辅助文本的评价很高,特别是写作质量,但很难准确识别LLM的使用情况.
需要进一步的研究,以了解在学术环境中最佳的LLM整合策略.

关键词:

在法学士 (LLM) 课程中.学术写作学术写作生成型的人工智能高等教育高等教育进行同行评价.学生的学习成果学生的学习成果文本质量文本质量

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