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Domain-Differentiated Generative AI Competency Self-Efficacy Among University EFL Teachers: Patterns and Predictors
1Department of English, College of Arts, Jouf University, Sakaka 72341, Saudi Arabia.
Behavioral Sciences (Basel, Switzerland)
|July 28, 2026
Summary
Teachers
Area of Science:
- Educational Technology
- Artificial Intelligence in Education
- Teacher Professional Development
Background:
- Self-efficacy influences teachers' adoption of new practices.
- Existing measures of generative AI (GenAI) readiness overlook domain-specific differences.
- Understanding teachers' GenAI self-efficacy across distinct applications is crucial.
Purpose of the Study:
- To investigate domain-differentiated generative AI competency self-efficacy among Saudi EFL university teachers.
- To identify specific areas of high and low confidence in GenAI use.
- To inform targeted professional development strategies.
Main Methods:
- Surveyed 232 EFL teachers in Saudi public universities using a GenAI competency self-efficacy scale.
- Analyzed data using repeated-measures ANOVA, correlational analyses, and hierarchical multiple regression.
- Examined self-efficacy across domains: Basic Understanding, Teaching, Professional Engagement, and Research.
Main Results:
- Overall GenAI self-efficacy was moderate, masking significant domain variations.
- Teachers reported higher confidence in Basic Understanding and Teaching with GenAI.
- Lowest confidence was found in Research with GenAI, particularly for data analysis.
Conclusions:
- General measures of GenAI readiness obscure critical domain-specific confidence gaps.
- Targeted, domain-sensitive professional development is essential for effective GenAI integration.
- Support should address all career stages, focusing on areas like AI-driven data analysis.
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