人工智能和不确定的动机:隐藏的盟友影响EFL使用图尔明模型的论证论文
1Department of English Language, College of Arts, King Faisal University, Al Ahsa, Saudi Arabia.
Acta psychologica
|January 4, 2025
概括
人工智能 (AI) 工具和不确定的动机 (UM) 策略显著改善了沙特EFL学生的论证性写作. 该研究发现,提升了论文质量,清晰度和深度,克服了最初的学习曲线挑战.
科学领域:
- 教育技术的教育技术
- 应用语言学 应用语言学
背景情况:
- 论证性散文写作给英语作为外语 (EFL) 学习者带来了重大的语言和认知障碍.
- 需要有效的策略来提高EFL学习者的写作能力和参与度.
研究的目的:
- 研究人工智能 (AI) 工具和不确定的动机 (UM) 策略对沙特EFL学习者的论证性写作表现的综合影响.
- 评估人工智能驱动的反和UM对文章质量,清晰度,结构和深度的影响,使用图尔明模型.
主要方法:
- 60名沙特EFL学生完成了四个论证性写作任务.
- 利用人工智能工具进行实时反,并实施各种奖励的UM策略.
- 分析了基于图尔明模型的组件 (索赔,数据,支持,反驳) 的论文质量.
主要成果:
- 在论文质量,特别是清晰度,结构和深度方面,观察到显著的改进.
- 人工智能工具促进了论证组件的完善;UM持续的动机和参与.
- 由于与人工智能和图尔明模型相关的学习曲线,人们注意到最初的临时性绩效下降.
结论:
- 人工智能工具和UM策略的整合显示出强大的潜力,以提高EFL的论证性写作技能.
- 解决学习曲线的挑战对于最大限度地发挥AI和UM在教育中的好处至关重要.
- 对AI和UM的深思熟虑的整合可以改善EFL写作中的教育实践和学生成果.
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