人工智能增强英语教学中的认知负载和教师的创新行为:对技术适应性的调解分析
Su Dongying1, Meijuan Gai2,3, Aikebaier Simayilijiang4
1School of Foreign Language, Ningxia Medical University, Yinchuan City, Ningxia Province, China.
PloS one
|March 5, 2026
概括
高认知负载会对教师在人工智能环境中的创新教学产生负面影响,但技术适应性可以调节这种影响. 这项研究提供了通过更好的教师支持来改善人工智能增强教育的见解.
科学领域:
- 教育技术的教育技术.
- 认知心理学 认知心理学
- 教师专业发展教师专业发展
背景情况:
- 人工智能在教育中的整合给教师带来了挑战,可能会增加认知负载并阻碍创新实践.
- 了解认知负载,技术适应性和创新教学之间的相互作用对于有效的AI实施至关重要.
- 现有的理论,如认知负载理论,技术接受模型和创新扩散理论,为这项调查提供了基础.
研究的目的:
- 研究教师的认知负载,技术适应能力和AI增强教育环境中的创新教学行为之间的关系.
- 开发和测试一个理论框架,解释认知需求如何通过适应机制影响教学创新.
- 引入和验证用于测量人工智能辅助教学认知负载 (ATCL) 的新尺度.
主要方法:
- 使用探索性结构方程建模 (ESEM) 和WLSMV估计.
- 600名教师使用经过验证的仪器收集了人工智能辅助教学认知负载 (ATCL),技术适应性和创新教学行为的数据.
- 在教师子组中建立了测量不变性,测量模型显示出强大的心理测量特性.
主要成果:
- 在认知负载和创新的教学行为之间发现了显著的负面关系 (β = -134,p < .001).
- 技术适应性显著调解了这种关系,间接影响为β = -171 (p < .001).
- 在ATCL尺度表现出卓越的可靠性 (α = .91.93) 和构造有效性 (AVE = .64.68).
结论:
- 教师在人工智能环境中的认知负载是创新教学的一个重要障碍,但可以通过增强技术适应性来减轻这一问题.
- 这项研究提供了经验证据和验证的尺度 (ATCL) 以了解人工智能增强教学中的认知负载.
- 这些发现对设计专业发展计划和机构政策具有实际意义,以促进技术丰富的教室中的教学创新.
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