[論文レビュー] 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被引用数 43

ひとこと要約

この研究はLLM支援エッセイ作成と検索エンジンおよび脳オンリー条件の神経および行動への影響を比較し、長期的なLLM使用に関連する異なる脳結合パターンと認知コストを明らかにします。

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.

研究の動機と目的

• Investigate neural and behavioral consequences of LLM-assisted essay writing.
• Compare LLM, search engine, and brain-only conditions in essay tasks.
• Assess changes when participants shift between tool conditions across sessions.
• Examine long-term cognitive and educational implications of AI-assisted writing.

提案手法

• Random assignment of 54 participants into LLM, Search Engine, and Brain-only groups across three sessions.
• EEG to measure cognitive load and connectivity during writing tasks.
• Natural language processing (NLP) to analyze essays.
• Human teachers and an AI judge to score essays.
• Session 4 reassignments: LLM-to-Brain and Brain-to-LLM to test transfer effects.

実験結果

リサーチクエスチョン

• RQ1 How do neural connectivity and cognitive load differ across LLM, search engine, and brain-only essay writing conditions?
• RQ2 What are the behavioral and linguistic effects of using an AI assistant for essay writing?
• RQ3 How do reassignment of tools (LLM-to-Brain, Brain-to-LLM) affect cognitive engagement and performance over time?
• RQ4 Do long-term LLM use and dependence show measurable cognitive costs in neural, linguistic, and behavioral metrics?

主な発見

• Brain-only participants show the strongest and most distributed connectivity networks.
• Search Engine users show moderate brain engagement, while LLM users have the weakest connectivity.
• Cognitive activity scales down with greater reliance on external tools.
• In session 4, LLM-to-Brain participants exhibit reduced alpha and beta connectivity indicating under-engagement.
• Brain-to-LLM users show higher memory recall and activation in occipito-parietal and prefrontal areas, akin to the Search Engine group.
• Self-reported ownership of essays is lowest in the LLM group and highest in the Brain-only group; LLM users struggle to accurately quote their own work.