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生成式人工智能对英语专业学生学习投入影响——以新疆五所高校为例
The Impact of Generative Artificial Intelligence on the Learning Engagement of English Major Students—Taking Five Universities in Xinjiang as an Examples

DOI: 10.12677/ass.2025.146475, PP. 48-55

Keywords: 生成式人工智能,英语专业,学习投入,自我调节学习
Generative Artificial Intelligence
, English Major, Learning Engagement, Self-Regulated Learning

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Abstract:

生成式人工智能是赋能学习方式创新、探索教育高质量发展新路径。采用混合研究方法,对新疆五所高校173名英语专业学生进行问卷调查,选取8名学生进行半结构化访谈,基于行为投入、情感投入和认知投入三维度框架,结合自我调节理论,探究生成式人工智能对英语专业学生学习投入的影响及其作用机制。研究发现,GenAI的使用显著提高了学生的行为投入、情感投入和认知投入,其中认知投入增幅最高,情感与行为投入次之。自我调节学习在GenAI使用和学习投入之间起到部分中介作用,GenAI促进学生学习策略优化。访谈显示学生对生成内容的判别能力与指令精准度直接影响使用效果,GenAI素养是关键调节因素。最后提出注重培养学生的人机协同学习能力等建议。
Generative artificial intelligence (GenAI) facilitates the innovation of learning methodologies and uncovers novel pathways for the high-quality development of education. A mixed-methods research approach was employed, comprising a questionnaire survey administered to 173 English major students from five universities in Xinjiang, along with semi-structured interviews conducted with eight selected participants. Grounded in the three-dimensional framework of behavioral engagement, affective engagement, and cognitive engagement, and integrated with self-regulation theory, this study explored the impact of GenAI on the learning engagement of English major students and its underlying mechanism of action. The findings revealed that the utilization of GenAI substantially enhanced students’ behavioral, affective, and cognitive engagement, with the most pronounced increase observed in cognitive engagement, followed by affective and behavioral engagement. Self-regulated learning partially mediated the relationship between GenAI usage and learning engagement, while GenAI contributed to the refinement of students’ learning strategies. Interview data indicated that students’ discernment of generated content and the precision of their instructions significantly influenced the effectiveness of GenAI use. Furthermore, GenAI literacy emerged as a critical moderating factor. Finally, recommendations were proposed to emphasize the cultivation of students’ competencies in human-machine collaborative learning.

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