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AI evidence extraction

Language teachers’ AI literacy: A psychometric study based on the ED-AI framework

Salim Nabhan, Anita Habók · Computers and Education Artificial Intelligence · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

6/10
Relevance
1/4
Quality (LMQS)
E
Evidence
0
Citations
0.00
FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1016/j.caeai.2026.100583

Methodology & findings

Study design

Two-phase psychometric study using exploratory factor analysis (EFA) with 165 participants and confirmatory factor analysis (CFA) with a separate sample of 227 preservice English language teachers to develop and validate the Teachers' AI Literacy Scale (TAILS)..

Sample

N = 392, 4 groups

Primary method

Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), Cronbach's alpha for internal consistency, Chi-square/df ratio, Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), Tucker-Lewis Index (TLI), Comparative Fit Index (CFI)

Main result

The study developed and validated the Teachers' AI Literacy Scale (TAILS) with results confirming "a six-factor structure with high internal consistency (Cronbach's α values > 0.90) and acceptable model fit indices (Chi-square/df = 1.766, RMSEA = 0.058, SRMR = 0.054, TLI = 0.908, CFI = 0.919), demonstrating strong validity and reliability." Each dimension aligned clearly with the competencies required for AI-integrated language teaching.

Reports effect sizes.

Research paradigm

positivist/quantitative

Author conclusions

The authors conclude that "The TAILS is a psychometrically robust, context-specific instrument for assessing AI literacy in language teacher education. This study bridges the gap between theoretical frameworks and practical assessment, offering a foundation for curriculum development, professional training, and policymaking. Its application supports the preparation of AI-competent educators equipped to navigate the ethical, pedagogical, and technological demands of the digital classroom."

Risk of bias

Sample limited to preservice English language teachers (generalizability concern); Two-phase design with different samples may affect longitudinal validity; No mention of blinding or control for social desirability bias in self-report measures; Sample consists exclusively of preservice English language teachers, limiting generalizability to in-service teachers or other language education contexts; No information provided on gender, age, or other demographic characteristics that could introduce selection bias; Potential sampling bias if recruitment was not random across institutions; No discussion of potential common method variance given reliance on self-report measures; Sample limited to preservice English language teachers only, may not generalize to in-service teachers or teachers of other languages; Single context-specific validation (language teacher education), cross-cultural validity unknown; No information on gender, age, or other demographic characteristics that could introduce selection bias

Open questions raised

  • The study addresses the gap in measuring AI literacy within language teacher education, as "most existing assessments target students or general users, leaving a gap in measuring AI literacy within language teacher education."
  • The authors identified that "most existing assessments target students or general users, leaving a gap in measuring AI literacy within language teacher education." Future directions include application to curriculum development, professional training, and policymaking for language teacher preparation.
  • The study addresses the gap that "most existing assessments target students or general users, leaving a gap in measuring AI literacy within language teacher education." Future directions implied include curriculum development, professional training program implementation, and policymaking applications.
Data: not_statedCode: not_statedExtracted from: pdfAgreement 67%

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