在人-GenAI互动学习环境中改善非正式推理的认知和神经机制:一项fNIRS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
概括
生成型人工智能 (GenAI) 和传统搜索引擎同样提高了非正式推理. 然而,GenAI学习涉及以目标为导向的理解,而传统方法则依赖于信息检索以获得推理收益.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 生成型人工智能 (GenAI) 提供个性化学习,但它对非正式推理和潜在的神经机制的影响尚不清楚.
- 了解交互式学习模式如何影响认知过程对于教育技术开发至关重要.
研究的目的:
- 研究GenAI支持的与传统搜索引擎支持的交互式学习中非正式推理改进的认知和神经机制.
- 为了比较动态功能连接 (dFC) 状态及其与学习模式之间推理改进的相关性.
主要方法:
- 移动窗口相关性和k-means集群用于从fNIRS数据中分析动态功能连接 (dFC).
- 将GenAI组与传统搜索引擎 (TSE) 组 (n=78) 之间的非正式推理改进进行比较.
- 检查在中部/后侧前额和部-部区域推理改善的神经相关性.
主要成果:
- 在GenAI和TSE组之间,非正式推理改进没有显著差异.
- 这两组在学习过程中都表现出类似的动态功能连接 (dFC) 状态.
- 基因AI组:状态1 (目标导向的理解) 与推理能力的提高有积极的相关性.
- TSE组:状态3 (信息检索) 与推理改进正相关.
结论:
- 基因智能和传统搜索引擎在非正式推理方面取得了相似的改进.
- 独特的神经机制支持推理改进:GenAI利用意义,而TSE依赖于信息检索.
- 研究结果揭示了不同交互式学习环境影响的共同和独特的神经通路.
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