Download Full PDF

孙志超

赵小亮

西南林业大学，中国

 

马晓柯

北京语言大学，中国

 

摘要

生成式人工智能（ GenAI）正在引发语言教育领域的深刻变革，已有研究多聚焦于教师的技术接受意愿，而对情感体验对身份重构的深层调节机制关注较少。本研究以维果茨基“过往情感” （ Perezhivanie）理论为基础，采用 Q 方法对来自 15 个国家的 30 名海外新手汉语教师进行调查，经因子分析发现，存在三类典型情感调节模式 ：（ 1）经验叙事型 ：这类教师将自己过往的专业积累转化为叙事资源，并在人机协作过程中确认自身的专业价值，进而实现相对稳定的身份重构； （ 2）情感规避型 ：教师会受防御性情感主导，倾向通过规则规避掉技术带来的不确定性，呈现出一种过渡性的身份协商状态 ；（ 3）情境依附型 ：教师表面拥抱 GenAI 变革，然而内心却有所保留，其认知认同和实际行动间存在明显脱节，身份重构呈现出碎片化特征。研究发现，过往情感经验的叙事化程度是 Perezhivanie 调节效能的核心变量。尽管三类教师应对路径不同，但他们均未因 GenAI 冲击而产生根本性的身份崩塌，专业身份意识构成了他们共同的情感底线。本研究不仅打破了传统技术接受模型的分析框架，也为国际中文师资培养从“技能本位”转向“情感逻辑识别”提供了实证依据。

 

关键词

生成式人工智能（ GenAI），海外新手汉语教师，数字身份认同，过往情感（ Perezhivanie）， Q方法

 

Perezhivanie-Mediated Regulation of Digital Identity among Novice Overseas Chinese Language Teachers in the GenAI Era: A Q-Methodological Study
 

Zhichao Sun

Xiaoliang Zhao

Southwest Forestry University, China

 

Xiaoke Ma

Beijing Language and Culture University, China

 

Abstract

Generative artificial intelligence (GenAI) is rapidly transforming how language education works. Most studies so far have looked at whether teachers plan to accept new technology, but these studies miss something deeper; they overlook how teachers’ emotionsshape the way they rebuild their professional identity. This study takes a different path. It uses Vygotsky’s concept of Perezhivanie as its framework — the idea of how a person emotionally lives through and makes sense of an experience. The study applies Q methodology to examine 30 novice overseas Chinese language teachers from 15 countries, using 36 Q statements. Factor analysis produced three emotional regulation patterns: (1) the Narrative-Experiential Type: these teachers turn their past professional experience into stories and use them as resources; within a human-AI partnership, they confirm their own worth and rebuild their identity in a strong and steady way; (2) the Emotional-Avoidant Type: these teachers act from a defensive mindset and set up clear rules to keep the technology at a distance, so their identity stays in a state of ongoing negotiation; (3) the Context-Dependent Type: these teachers appear to welcome the changes GenAI brings, but a gap runs beneath the surface. They accept the technology in their minds, yet their actions stay limited, and this split leaves their identity rebuilt only in fragments. The findings point to one key variable: how much a teacher turns emotional experience into a story decides how well Perezhivanie helps them adjust. One more point stands out — none of the three types lost their sense of identity when GenAI disrupted their work; instead, all of them shared one emotional baseline, a sense of professional agency. This study not only moves beyond the analytical framework of traditional technology acceptance models but also provides empirical evidence for shifting international Chinese language teacher education from a “skills-oriented” approach toward one that recognizes and addresses the underlying emotional dynamics of technology use.

 

Keywords

Generative artificial intelligence (GenAI), novice overseas Chinese language teachers, digital identity, Perezhivanie, Q methodology