人間-GenAIのインタラクティブな学習コンテキストにおける非公式推論の改善のための認知および神経機構: fNIRSの研究
Liangliang Xia1, Yan Dong1, Wei-Peng Teo2
1School of Educational Technology, Beijing Normal University, No.19 Xinjiekouwai St., Haidian District, Beijing, People's Republic of China.
NeuroImage
|February 13, 2026
まとめ
生成型人工知能 (GenAI) と伝統的な検索エンジンは,非公式推論を同様に改善します. しかし,GenAIの学習は,目標指向の意味づくりに従事しますが,従来の方法は,推論の利益のために情報検索に依存しています.
科学分野:
- 神経科学は神経科学である.
- コグニティブ・サイエンス コグニティブ・サイエンス
- 人工知能 (AI) とは,人工知能 (AI) のことです.
背景:
- 生成型人工知能 (GenAI) はパーソナライズされた学習を提供するが,非公式な推論と基礎となる神経機構への影響は不明である.
- インタラクティブな学習モードが認知プロセスにどのように影響するかを理解することは,教育技術の開発に不可欠です.
研究 の 目的:
- GenAIがサポートするインタラクティブラーニングと,従来の検索エンジンがサポートするインタラクティブラーニングにおける非公式推論の改善の認知および神経学的メカニズムを調査する.
- ダイナミック・ファンクショナル・コネクティビティ (dFC) 状態と,学習モードの間の推論改善との相関を比較する.
主な方法:
- スライドウィンドウ相関とk-meansクラスタリングで,fNIRSデータから動的機能接続性 (dFC) を分析します.
- GenAIグループと伝統的な検索エンジン (TSE) グループ (n=78) の間の非公式推論の改善を比較する.
- 推論の改善のニューラル相関を,中部/背側前頭前頭部および側頭前頭部-頭頂部領域で調べる.
主要な成果:
- GenAIとTSEのグループ間の非公式推論の改善において有意な差異はありません.
- 両グループは,学習中に類似の動的機能的接続性 (dFC) 状態を示した.
- GenAIグループ:ステート1 (目標指向の意味づくり) は,推論の改善と正に相関していました.
- TSEグループ:ステート3 (情報検索) は,推論の改善と正に相関していました.
結論:
- GenAIと伝統的な検索エンジンは,非公式な推論において同様の改善をもたらします.
- 明確な神経メカニズムが推論の改善を支える:GenAIは意味づくりを利用し,TSEは情報検索に依存する.
- 発見は,異なるインタラクティブな学習環境によって影響を受ける共有され,ユニークな神経経路を明らかにします.
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