Teach academic subjects to students at the secondary-school level, excluding special education and career or technical education.
AI can automate meaningful preparation, grading, tutoring and administrative work, but classroom supervision, relationships, institutional staffing rules and physical presence keep the core teacher role highly human-dependent. Demographics may matter more than AI through 2035.
No clear technology-driven aggregate inflection visible
EOL remains close to the flat BLS trajectory because substantial task automation does not yet translate into teacher-role automation. The modest downside allows productivity gains or staffing-ratio changes to emerge gradually.
Student population, class-size policy and school funding dominate demand; demographics may outweigh AI near term.
Task automation without role automation; students per teacher.
AI can prepare lesson materials, tutoring content, summaries and feedback, but not fully manage classrooms.
Directional summary of whether current evidence pushes EOL toward greater or lesser human labor demand relative to the occupation’s existing structural trajectory. It is categorical, not a probability, percentile, automation share, or mathematical adjustment.
AI is changing preparation and support work, but supervision, classroom presence, class-size policy and institutional structure still make historical teacher staffing relationships broadly informative.
Projections of steady output from the supply pipeline match EOL’s relatively flat national demand outlook. Declining student enrollment may reduce demand in some regions, while certification requirements, geographic immobility, subject specialization, and uneven entrant flows may result in shortages elsewhere. Nonetheless, the national labor supply should generally be in balance with teacher demand.
How EOL analyzes labor supply →