人工智能预期违反和学习者参与EFL环境:一种认知情感恢复模型
Jiawen Yu1,2, Xianyan Dai3,4, Baoyu Qiu5
1School of Foreign Languages, Nanfang College, Guangzhou, Guangdong, China.
Frontiers in psychology
|February 23, 2026
概括
当人工智能 (AI) 在英语外语 (EFL) 教育中失败时,学习者期望,参与度下降. 然而,人工智能适应性,认知重新评估和信任恢复可以帮助学习者坚持下去.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 获得第二语言的学习.
背景情况:
- 人工智能 (AI) 在英语外语 (EFL) 教育中被广泛使用,用于自适应反和个性化学习.
- 研究往往忽视了学习者对AI失败和恢复过程的反应,使认知情感机制未得到充分探索.
研究的目的:
- 提出和测试一种认知情感恢复模型,用于学习者参与人工智能支持的EFL环境.
- 调查期望违反如何影响参与和认知重新评估和信任恢复的调解作用.
- 为了检查数字坚对学习者在挫折后的坚持的缓解效应.
主要方法:
- 对298名中国EFL学习者 (大学和私立学院的学生) 进行了两波调查.
- 部分最小平方结构方程建模 (PLS-SEM) 用于数据分析.
- 这项研究整合了期望违规理论,认知评估理论和数字隔/弹性理论.
主要成果:
- 违反预期会显著降低学习者参与度.
- 感知到的人工智能适应性减轻了违反预期的负面影响,并启动了恢复.
- 认知重新评估和信任恢复起到了关键的调解作用,而数字坚则调节了参与途径.
结论:
- 在EFL中,学习者对人工智能的参与受到预期违反和恢复过程的影响.
- 设计适应性,培养信任和促进数字坚的AI系统对于持续的参与至关重要.
- 这项研究强调了理解AI学习者互动中的失败和恢复动态对于公平的语言教育的重要性.
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