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<p>This study develops a teacher AI literacy scale comprising six dimensions: perception of AI, AI knowledge, AI skills, applications of AI, innovation of AI, and AI ethics. The research involved three sequential phases. In the first phase, a Random Forest Model (RFM) was employed to select 30 representative items from an initial pool of 60 questions. The second phase focused on psychometric validation using the Rasch model. Results demonstrated that the overall scale met psychometric standards, with all item fit indices falling within acceptable ranges. In the third phase, the validated scale was applied to a sample of 658 teachers using Latent Class Analysis (LCA). The analysis revealed five distinct latent classes of AI literacy among teachers: AI Pioneers, Practical Teachers, Developing Teachers, Emerging AI Teachers, and Traditional Teachers. In sum, the scale is promising but still requires cross-cultural validation and predictive-validity checks.</p>

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From Metrics to Practice: Rethinking and Refining the AI Literacy Scale for Teachers

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Yimin Ning,
  • E. Anni,
  • Dengming Yao,
  • Binyan Xu,
  • Tommy Tanu Wijaya

摘要

This study develops a teacher AI literacy scale comprising six dimensions: perception of AI, AI knowledge, AI skills, applications of AI, innovation of AI, and AI ethics. The research involved three sequential phases. In the first phase, a Random Forest Model (RFM) was employed to select 30 representative items from an initial pool of 60 questions. The second phase focused on psychometric validation using the Rasch model. Results demonstrated that the overall scale met psychometric standards, with all item fit indices falling within acceptable ranges. In the third phase, the validated scale was applied to a sample of 658 teachers using Latent Class Analysis (LCA). The analysis revealed five distinct latent classes of AI literacy among teachers: AI Pioneers, Practical Teachers, Developing Teachers, Emerging AI Teachers, and Traditional Teachers. In sum, the scale is promising but still requires cross-cultural validation and predictive-validity checks.