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February 25, 2026Language Teaching Research0 citations

The development and validation of the artificial intelligence literacy scale for middle school English teachers

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YGYang GaoXWXiaochen Wang

Key Points

  • The aim is to create and validate an AI literacy scale tailored for middle school English teachers in China.
  • Developed a scale based on a framework of Awareness, Use, Evaluation, and Ethics.
  • Conducted item analysis and exploratory factor analysis on 700 teachers.
  • Performed confirmatory factor analysis with an independent sample of 823 teachers to finalize the scale's structure.
  • Ensured reliability, construct validity, and criterion-related validity of the scale.
  • The final instrument consists of 24 items across four dimensions: Awareness, Use, Evaluation, and Ethics.
  • Demonstrated strong psychometric properties including satisfactory reliability and validity.
  • Measurement invariance was established across gender, age, and educational level.

Abstract

As artificial intelligence (AI) becomes increasingly embedded in education, AI literacy has emerged as a critical competency for teachers across disciplines. However, existing research and assessment tools largely focus on STEM (science, technology, engineering and mathematics) fields, with limited attention given to AI literacy within language education. To address this gap, the present study aimed to develop and validate an AI literacy scale specifically designed for middle school English teachers in China. The scale is based on a four-dimensional framework comprising Awareness, Use, Evaluation, and Ethics. Using sequential design, item analysis and exploratory factor analysis were conducted with a sample of 700 teachers, followed by confirmatory factor analysis with an independent sample of 823 teachers, resulting in a final instrument comprising 24 items across four dimensions. The final instrument is structured around four dimensions: awareness, use, evaluation, and ethics. In addition, the scale demonstrated strong psychometric properties, including satisfactory reliability, construct validity, and criterion-related validity. Furthermore, measurement invariance was established across gender, age, and educational level, indicating the robustness of the scale across key demographic groups. The study offers a validated, context-specific tool to inform professional development and educational policy, enhancing teachers’ preparedness for AI-integrated instruction.

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Cite This Study

Gao et al. (2026) studied this question.

synapsesocial.com/papers/699e919cf5123be5ed04f4f4https://doi.org/10.1177/13621688261416208
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