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整合协同学习和能力分组与人工智能生成的多层次任务,以增强阅读态度和结果
Latifah Hamdan Alghamdi1, Talal Musaed Alghizzi2
1ELC, College of Languages and Translation, King Khalid University, Abha, 62529, Kingdom of Saudi Arabia.
Acta psychologica
|January 29, 2026
概括
整合人工智能生成的阅读任务和能力分组显著改善了大学生的英语阅读理解. 这种方法增强了参与度和学习者的自主性,为多样化的EFL课堂提供了可扩展的解决方案.
科学领域:
- 教育技术的教育技术
- 应用语言学 应用语言学
- 获得第二语言的学习.
背景情况:
- 作为外语英语 (EFL) 课堂的不同水平阻碍了个性化的阅读理解指导.
- 传统的适合所有人的方法不足以满足各种学习者的需求.
- 技术整合为差异化教学提供了潜在的解决方案.
研究的目的:
- 调查人工智能产生的多层次阅读任务,基于能力的分组和协作学习对EFL学生阅读理解和参与的影响.
- 评估在大学EFL环境中,一个差异化,技术增强的教学模式的有效性.
- 探索学习者自主性和情感参与的变化.
主要方法:
- 准实验性的混合方法设计在9周内与87名一年级女EFL学生进行.
- 学生被分为CEFR-J级别 (A2,B1.1,B1.2) 的学生,并获得AI定制的阅读材料.
- 协同协作的学习环境促进了能力组内和能力组之间的同行支架.
主要成果:
- 在所有能力水平中观察到阅读理解的显著增长,特别是在A2组中,使用与雅思一致的测试.
- 通过利克特尺度调查问卷报告的高学习者参与度和满意度.
- 定性数据表明,学习者的自主性,信心和情感投资增加了.
结论:
- 人工智能辅助差异化和协同能力分组的综合战略有效地提高了EFL阅读理解.
- 该模型促进了个性化,学习者自主性和同行辅助的发展.
- 这种方法为具有异质学生群体的EFL环境提供了一个可扩展的解决方案.
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