Journal of Research on Technology in Education· 2026Q1
Pathways to teacher wellbeing: AI pedagogy self-efficacy, workload, and anxiety in a structural model
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- Q1SCImago
- 2026year
Short summary
Teacher AI pedagogy self-efficacy boosts overall self-efficacy, leading to reduced workload and anxiety, and consequently higher mental wellbeing, according to a structural model of 400 U.S. K-12 teachers.
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Key points
- AI pedagogy self-efficacy positively impacts teachers' overall self-efficacy.
- Higher overall self-efficacy is associated with reduced workload and anxiety.
- Reduced workload and anxiety lead to improved mental wellbeing.
- The study analyzed data from a nationally representative sample of 400 U.S. K-12 teachers.
AI-generated from the title and abstract; the full text is not read.
Abstract
As artificial intelligence (AI) tools become more common in K–12 classrooms, questions remain about whether AI competence supports teacher wellbeing. Grounded in social cognitive theory, this study examined relationships among teachers’ AI pedagogy efficacy, instructional and engagement self-efficacy, workload, anxiety, and mental wellbeing. Survey data were collected from a nationally representative sample of 400 U.S. K–12 teachers. Hierarchical regression and structural equation modeling (SEM) were used to examine direct and indirect pathways. Results indicated that AI pedagogy efficacy strengthened teacher self-efficacy, which was associated with lower workload and anxiety and, in turn, higher mental wellbeing. Findings suggest that building teachers’ AI competence may support psychological wellbeing as AI becomes embedded in instructional practice.
The authors' abstract, as published at the source. Journal of Research on Technology in Education, 2026 · DOI ↗
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Field: Social Psychology
Social PsychologyPsychology