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人工智能与不确定动机:运用图尔敏模型影响英语议论文的潜在因素

AI and Uncertain Motivation: Hidden allies that impact EFL argumentative essays using the Toulmin Model.

作者信息

Al Fraidan Abdullah

机构信息

Department of English Language, College of Arts, King Faisal University, Al Ahsa, Saudi Arabia.

出版信息

Acta Psychol (Amst). 2025 Feb;252:104684. doi: 10.1016/j.actpsy.2024.104684. Epub 2025 Jan 3.

Abstract

This study investigates the combined impact of artificial intelligence (AI) tools and Uncertain Motivation (UM) strategies on the argumentative writing performance of Saudi EFL learners, using the Toulmin Model. Sixty Saudi EFL students participated in four writing tasks, with results demonstrating significant improvements in essay quality, particularly in clarity, structure, and depth. AI tools provided real-time feedback, enhancing students' ability to refine claims, data, backing, and counterarguments. UM strategies, employing varied and unpredictable rewards, effectively sustained student motivation and engagement. However, a temporary decline was noted early in the study, attributed to the learning curve associated with both AI and the Toulmin model. Writing argumentative essays poses significant linguistic and cognitive challenges for EFL learners. By addressing these barriers, the study highlights the potential of AI to enhance EFL writing proficiency and underscores the motivational role of UM in maintaining student engagement. The findings emphasize the importance of integrating AI and UM thoughtfully into educational practices to maximize their effectiveness. Future research is encouraged to explore AI's long-term effects, address ethical considerations, and refine the integration of AI and UM strategies across diverse learning contexts.

摘要

本研究使用图尔敏模型,调查人工智能(AI)工具和不确定动机(UM)策略对沙特英语外语学习者议论文写作表现的综合影响。60名沙特英语外语学生参与了四项写作任务,结果表明文章质量有显著提高,尤其是在清晰度、结构和深度方面。人工智能工具提供实时反馈,提高了学生完善主张、数据、论据和反驳观点的能力。UM策略采用多样且不可预测的奖励,有效维持了学生的动机和参与度。然而,研究初期发现成绩出现了暂时下降,这归因于与人工智能和图尔敏模型相关的学习曲线。撰写议论文对英语外语学习者构成了重大的语言和认知挑战。通过克服这些障碍,本研究凸显了人工智能在提高英语外语写作能力方面的潜力,并强调了UM在维持学生参与度方面的激励作用。研究结果强调了将人工智能和UM审慎地融入教育实践以使其效果最大化的重要性。鼓励未来的研究探索人工智能的长期影响,解决伦理问题,并在不同学习环境中优化人工智能和UM策略的整合。

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