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<p>Generative artificial intelligence is rapidly reshaping teachers’ professional practices, creating new demands for judgment, responsibility, and autonomy in educational decision-making. However, existing measurement tools largely focus on AI literacy, attitudes, or general readiness, leaving a critical gap in assessing how teachers engage with generative AI in professionally meaningful and ethically responsible ways. This study addresses this gap by developing and validating the Teachers’ Generative AI Professional Competence Scale, conceptualized as a multidimensional construct comprising pedagogical judgment, ethical leadership, and professional agency. A sequential exploratory mixed-method design was employed, beginning with construct specification, literature synthesis, item generation, expert validation, and cognitive pretesting. The scale was then administered to a sample of 648 teachers in Saudi Arabia. Data were randomly split for exploratory and confirmatory factor analyses. Results supported a stable three-factor structure with strong item loadings and satisfactory explained variance. Confirmatory factor analysis demonstrated excellent model fit and substantially better fit than the one-factor model over alternative factor structures. Reliability and validity evidence, including internal consistency, composite reliability, and average variance extracted, indicated strong psychometric properties. Multigroup CFA supported configural and loading invariance across the gender groups examined; scalar and strict invariance could not be evaluated conclusively from the retained output. In addition, exploratory graph analysis recovered a three-community solution consistent with the factor-analytic structure and demonstrated high structural stability. Overall, the findings provide promising initial evidence for the validity and reliability of the proposed scale for assessing teachers’ professional competence in the context of generative AI. The scale provides a valuable tool for future research, teacher education, and policy development aimed at supporting responsible and human-centered integration of AI in education.</p>

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Development and validation of the teachers generative AI professional competence scale

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Wafa Mohammed Aldighrir

摘要

Generative artificial intelligence is rapidly reshaping teachers’ professional practices, creating new demands for judgment, responsibility, and autonomy in educational decision-making. However, existing measurement tools largely focus on AI literacy, attitudes, or general readiness, leaving a critical gap in assessing how teachers engage with generative AI in professionally meaningful and ethically responsible ways. This study addresses this gap by developing and validating the Teachers’ Generative AI Professional Competence Scale, conceptualized as a multidimensional construct comprising pedagogical judgment, ethical leadership, and professional agency. A sequential exploratory mixed-method design was employed, beginning with construct specification, literature synthesis, item generation, expert validation, and cognitive pretesting. The scale was then administered to a sample of 648 teachers in Saudi Arabia. Data were randomly split for exploratory and confirmatory factor analyses. Results supported a stable three-factor structure with strong item loadings and satisfactory explained variance. Confirmatory factor analysis demonstrated excellent model fit and substantially better fit than the one-factor model over alternative factor structures. Reliability and validity evidence, including internal consistency, composite reliability, and average variance extracted, indicated strong psychometric properties. Multigroup CFA supported configural and loading invariance across the gender groups examined; scalar and strict invariance could not be evaluated conclusively from the retained output. In addition, exploratory graph analysis recovered a three-community solution consistent with the factor-analytic structure and demonstrated high structural stability. Overall, the findings provide promising initial evidence for the validity and reliability of the proposed scale for assessing teachers’ professional competence in the context of generative AI. The scale provides a valuable tool for future research, teacher education, and policy development aimed at supporting responsible and human-centered integration of AI in education.