AI and the future of teachers: predicting stakeholders’ expectations of teacher job loss from AI-related institutional conditions

Abstract

Artificial intelligence (AI) is transforming educational work, although claims that it will replace teachers often overlook the institutional conditions that make teacher job loss appear more or less likely. This study examined whether current AI-related institutional risk conditions predict stakeholder expectations of teacher job loss over the next decade. A purposive sample of 300 education and AI stakeholders rated the extent to which 11 conditions were currently present and provided a separate 0–10 score indicating the extent to which they expected AI to contribute to teacher job loss. Overall job-loss expectations were low to moderate, and the institutional AI condition indicators showed good internal consistency. The machine learning model showed good predictive performance in the original held-out test set ( R 2  = 0.683) and stable performance across 100 repeated train-test splits (mean R 2  = 0.598). The explainable AI analysis showed that the strongest contribution band consisted of automated feedback, large-scale AI adoption, and standardized content delivery. The findings suggest that the extent to which stakeholders perceive AI-related institutional risk conditions to be currently present in educational settings is associated with the extent of teacher job loss they expect. They further indicate that this risk is not an inevitable technological outcome, but is embedded in a broader institutional ecology, with expectations clustering more strongly around direct forms of AI-mediated instructional restructuring than around indirect institutional or governance-related vulnerabilities.