Artificial Intelligence and the Teaching Workforce: Evidence From National Survey Data in the United States

Artificial intelligence (AI) has shifted rapidly from speculative innovation to active policy concern in U.S. education, yet nationally representative evidence on teachers’ capacity and well-being remains limited. Using U.S. data from TALIS 2024 and design-based linear models, this quantitative study examines how AI-focused professional development (PD), beliefs, perceived barriers, and AI use intensity relate to teachers’ instructional fulfillment and occupational well-being. About 40% of teachers reported AI PD participation and AI use. Positive beliefs predicted higher satisfaction and well-being but also greater stress, whereas critical beliefs predicted lower satisfaction yet higher self-efficacy. Perceived barriers to AI use were associated with lower well-being. Results highlight AI policy as workforce policy, emphasizing high-quality PD, collaboration, and reduced time costs.