人工智能驱动脚手架在EFL在高等教育环境中的个人获取效率的有效性
1Nanjing University of Chinese Medicine Hanlin College, Taizhou, China.
Frontiers in psychology
|January 29, 2026
概括
由人工智能驱动的支架通过提高感知到的实用性和易用性来增强英语外语 (EFL) 学习,交互频率介导认知处理深度,以提高学习效率.
科学领域:
- 教育技术的教育技术
- 认知心理学 认知心理学
- 应用语言学 应用语言学
背景情况:
- 人工智能驱动的脚手架在高等教育中越来越多地用于英语作为外语 (EFL) 学习.
- 了解这些工具的认知机制和教育效果对于优化语言学习至关重要.
- 现有的框架,如技术接受模型 (TAM) 和认知负载理论 (CLT),为研究提供了基础.
研究的目的:
- 研究人工智能驱动的脚手架在EFL学习中的认知机制和教育有效性.
- 探索影响EFL获取效率的多维途径,专注于大学生.
- 检查认知处理深度 (CPD) 的调解作用和认知能力 (COA) 的调节作用.
主要方法:
- 使用结构方程建模 (SEM) 和必要条件分析 (NCA) 进行定量数据分析.
- 从大学生收集的数据使用人工智能辅助的对话工具进行脚手架.
- 在跨学科框架内整合TAM和CLT.
主要成果:
- 感知有用性 (PU) 和易用性 (PEoU) 直接预测个人获得EFL (LEF) 的学习效率.
- 人工智能辅助对话 (AIC) 的交互频率通过CPD间接影响LEF.
- 认知能力 (COA) 加强了人工智能工具使用-CPD关系,支持自我调节学习理论.
结论:
- 研究结果强调了认知适应策略和以学习者为中心的AI工具设计对于有效的EFL学习的重要性.
- 确定了AIC和PEOU的关键值,为实时教育干预提供了洞察力.
- 该研究倡导纵向方法和更广泛的社会认知调查,以推进人工智能增强的语言学习系统.
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