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在学生学习的大型语言模型与精心策划的文本相比,对学生的皮肤活动进行现场分析

Kenneth Y T Lim¹, Yue Heng Wong², Duc Nam Tran²

¹National Institute of Education, 1 Nanyang Walk, Singapore 637616, Singapore.

|February 27, 2026

概括

用大型语言模型 (LLM) 学习显示了在青少年中增强情感参与和降低认知负载的潜力. 这项研究提供了生理学证据,表明与传统文本相比,LLM可能会提高学习效率.

科学领域:

教育心理学教育心理学
认知科学认知科学
人与计算机的交互

背景情况:

通过大型语言模型 (LLM) 研究青少年在学习过程中的认知和情绪状态.
为政策制定者和教育工作者解决了对生成人工智能在教育中的有效性缺乏经验证据的问题.
有助于讨论AI在教学和学习中的作用.

研究的目的:

实证分析青少年通过LLM学习的认知和情绪反应与精选的文本.
通过使用生理和绩效数据,比较基于LLM的学习与传统方法的有效性.
提供数据驱动的洞察力,以便在教育环境中整合人工智能方面做出明智决策.

主要方法:

在与LLMs进行8分钟的学习会和与精选文本进行8分钟的会议期间,从参与者身上记录了皮肤电活动 (EDA).
在学习后进行了测试,以评估知识的获取和保留.
分析包括23个EDA样本和42个测试结果,以比较学习方法.

主要成果:

使用LLM学习显示了皮肤行为反应 (SCR) 的统计学显著增加,表明更积极的情绪价值 (p=0.09404).
使用LLM与皮肤行为水平 (SCL) 的显著下降有关,这表明认知负载较低 (p=0.09473).
参与者在使用LLMs学习时表现出更高的测试成绩 (p=0.02053),尽管自我报告的有效性显示出最小的差异.

结论:

这项初步研究提供了对青少年LLM学习效率的现场生理洞察.
研究结果表明,与精选文本相比,LLM可能会增强情感参与,减少认知负载.
与之前的研究相反,这些研究依赖于后期或长期评估,提供即时的经验数据.

关键词:

生成型的人工智能大型语言模型电皮活动电皮活动.神经机能学是指神经机能学.学习的科学学习的科学.可穿戴设备可以穿戴.

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