[论文解读] Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task

Nataliya Kosmyna, Eugene Hauptmann+6 more|ArXiv.org|Jun 10, 2025

Artificial Intelligence in Healthcare and Education被引用 43

一句话总结

该研究比较 LLM 辅助的论文写作与搜索引擎及纯脑条件在神经与行为上的影响，揭示长期使用 LLM 所相关的不同脑连接模式与认知成本。

ABSTRACT

This study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools). Each completed three sessions under the same condition. In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM). A total of 54 participants took part in Sessions 1-3, with 18 completing session 4. We used electroencephalography (EEG) to assess cognitive load during essay writing, and analyzed essays using NLP, as well as scoring essays with the help from human teachers and an AI judge. Across groups, NERs, n-gram patterns, and topic ontology showed within-group homogeneity. EEG revealed significant differences in brain connectivity: Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity. Cognitive activity scaled down in relation to external tool use. In session 4, LLM-to-Brain participants showed reduced alpha and beta connectivity, indicating under-engagement. Brain-to-LLM users exhibited higher memory recall and activation of occipito-parietal and prefrontal areas, similar to Search Engine users. Self-reported ownership of essays was the lowest in the LLM group and the highest in the Brain-only group. LLM users also struggled to accurately quote their own work. While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.

研究动机与目标

• 调查 LLM 辅助作文写作的神经与行为后果。
• 在作文任务中比较 LLM、搜索引擎与纯脑条件。
• 评估参与者在不同会话中切换工具条件时的变化。
• 考察 AI 辅助写作的长期认知与教育影响。

提出的方法

• 将 54 名参与者在三个会话中随机分配到 LLM、Search Engine 和 Brain-only 组。
• EEG 测量写作任务中的认知负荷与连接性。
• 使用自然语言处理（NLP）来分析作文。
• 由人类教师与 AI 评审对作文评分。
• 第4次会话再分配：LLM 转 Brain 与 Brain 转 LLM 以测试迁移效应。

实验结果

研究问题

• RQ1 在 LLM、搜索引擎与纯脑写作条件下，神经连接性和认知负荷有何差异？
• RQ2 使用 AI 助手进行作文写作在行为与语言方面的影响是什么？
• RQ3 工具重新分配（LLM-to-Brain、Brain-to-LLM）如何随着时间影响认知参与度与表现？
• RQ4 长期使用和依赖 LLM 是否在神经、语言和行为指标上显示可测量的认知成本？

主要发现

• 纯脑参与者显示最强且最分布广泛的连接网络。
• 搜索引擎用户表现出中等程度的脑参与，而 LLM 用户的连接性最弱。
• 随着对外部工具依赖的增加，认知活动呈下降趋势。
• 在第4次会话中，LLM-to-Brain 参与者表现出 α 与 β 连接性下降，表明参与不足。
• Brain-to-LLM 用户在枕顶叶及前额叶区域显示更高的记忆回忆与激活，类似于搜索引擎组。
• 自我报告的作文归属感在 LLM 组最低，在 Brain-only 组最高；LLM 用户难以准确引用自己的作品。