Lu Chunlin
School of foreign languages, Baotou Teachers' College, Baotou, China.
PLoS One. 2025 Jul 11;20(7):e0321544. doi: 10.1371/journal.pone.0321544. eCollection 2025.
This research study examined the manner in which English as a Foreign Language (EFL) learners' willingness towards writing, grammatical construction, and lexical bundle acquisition were affected by language of AI-generated corpora. Eighty EFL students from China's Baotou Teachers' College participated in a repeated-measures quasi-experimental study and were split into controlled and trial groups. During the span of the 14-sessions, the experimental cohort underwent education that utilized AI-driven corpora, whereas the controlled cohort was instructed through traditional textbooks. Data were collected using pre- and post-treatment assessments, including tests on lexical bundles and grammar, as well as a willingness to write scale. The findings revealed that the trial group's performance was substantially improved than the controlled cohort regarding all outcomes. Specifically, trial group demonstrated higher mean scores for lexical bundles, grammar, and willingness to communicate. Statistical analysis, including mixed ANOVA, confirmed the significant effects of time and group membership on language proficiency and willingness to communicate. These findings suggest that the intervention positively influenced learners' language skills and attitudes toward writing. The interaction effects of time and group membership further highlighted the nuanced relationship between instructional interventions and learner outcomes. The study underscores the importance of integrating AI-driven language corpora into language teaching to enhance vocabulary, grammar, and communication skills. Pedagogical implications include the need for dynamic and engaging learning environments, while curriculum developers should consider incorporating data-driven learning approaches. Further research might identify optimal designs for learning and investigate its long-term impacts associated with such interventions. Policies should support equitable access to technology-enhanced language learning resources, promoting more effective language education practices overall.
本研究考察了作为外语的英语(EFL)学习者对写作、语法结构和词汇束习得的意愿受人工智能生成语料库语言影响的方式。来自中国包头师范学院的80名EFL学生参与了一项重复测量准实验研究,并被分为对照组和实验组。在为期14节的课程中,实验组接受了利用人工智能驱动语料库的教学,而对照组则通过传统教科书进行教学。通过治疗前和治疗后的评估收集数据,包括词汇束和语法测试,以及写作意愿量表。研究结果显示,在所有结果方面,实验组的表现均比对照组有显著提高。具体而言,实验组在词汇束、语法和交流意愿方面的平均得分更高。包括混合方差分析在内的统计分析证实了时间和组成员身份对语言能力和交流意愿的显著影响。这些发现表明,该干预对学习者的语言技能和写作态度产生了积极影响。时间和组成员身份的交互作用进一步突出了教学干预与学习者结果之间的细微关系。该研究强调了将人工智能驱动的语言语料库整合到语言教学中以提高词汇、语法和交流技能的重要性。教学启示包括需要动态且引人入胜的学习环境,而课程开发者应考虑纳入数据驱动的学习方法。进一步的研究可能会确定最佳的学习设计,并调查其与此类干预相关的长期影响。政策应支持公平获取技术增强的语言学习资源,总体上促进更有效的语言教育实践。