Scholarlink

<p>Generative artificial intelligence (GenAI) holds significant potential to enhance second language (L2) teaching, with its effectiveness largely determined by teachers’ competence to use it. As such, a reliable instrument is required to assess this competence. This study developed and validated the GenAI Competence Scale for L2 Teachers (GAICS-L2T) through a three-phase process involving 933 Chinese L2 teachers. In Phase 1, the scale’s initial factors and items were developed by adapting the Digital Competence Framework for Teachers (Dig-CFT) issued by China’s Ministry of Education (2022), which outlined five factors: Consciousness, Knowledge &amp; Skills, Application, Responsibility, and Teacher Development. In Phase 2 (<i>n</i> = 525), the “Professional Development” factor was removed due to low adjusted correlation coefficients (&lt; 0.3) with the total score. Additionally, items related to Skills were excluded due to cross-loadings. As a result, exploratory factor analysis (EFA) identified a refined 24-item scale with four distinct factors—Consciousness, Knowledge, Application, and Responsibility—explaining 78.78% of the total variance. In Phase 3 (<i>n</i> = 408), confirmatory factor analysis (CFA) confirmed the scale’s structure, demonstrating an excellent fit. The GAICS-L2T also showed strong validity, reliability, and cross-gender invariance, although it did not support invariance across school levels, suggesting varying perceptions of GenAI among teachers at different educational stages. Overall, the GAICS-L2T proves to be a psychometrically robust tool for evaluating L2 teachers’ competence in GenAI.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

GenAI competence is different from digital competence: developing and validating the GenAI competence scale for second language teachers

  • 期刊论文

  • 发表日期：

    2025年6月19日

查看全文
  • Hanwei Wu,
  • Yonghong Zeng,
  • Zhongping Chen,
  • Fen Liu

摘要

Generative artificial intelligence (GenAI) holds significant potential to enhance second language (L2) teaching, with its effectiveness largely determined by teachers’ competence to use it. As such, a reliable instrument is required to assess this competence. This study developed and validated the GenAI Competence Scale for L2 Teachers (GAICS-L2T) through a three-phase process involving 933 Chinese L2 teachers. In Phase 1, the scale’s initial factors and items were developed by adapting the Digital Competence Framework for Teachers (Dig-CFT) issued by China’s Ministry of Education (2022), which outlined five factors: Consciousness, Knowledge & Skills, Application, Responsibility, and Teacher Development. In Phase 2 (n = 525), the “Professional Development” factor was removed due to low adjusted correlation coefficients (< 0.3) with the total score. Additionally, items related to Skills were excluded due to cross-loadings. As a result, exploratory factor analysis (EFA) identified a refined 24-item scale with four distinct factors—Consciousness, Knowledge, Application, and Responsibility—explaining 78.78% of the total variance. In Phase 3 (n = 408), confirmatory factor analysis (CFA) confirmed the scale’s structure, demonstrating an excellent fit. The GAICS-L2T also showed strong validity, reliability, and cross-gender invariance, although it did not support invariance across school levels, suggesting varying perceptions of GenAI among teachers at different educational stages. Overall, the GAICS-L2T proves to be a psychometrically robust tool for evaluating L2 teachers’ competence in GenAI.