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High School Teacher
Three components - Automation Resistance, Structural Moat, and Demand - add up to 61.
Automation Resistance is lower than elementary because subject content, grading, tutoring, course materials, and feedback are more reachable by AI. The classroom still needs a teacher, but the exposed layer is closer to the center of the job.
Observed AI exposure for high school teachers is 29.0%, while modeled median job-loss risk is about 9.7%. AI reaches lesson drafts, examples, quizzes, rubrics, writing feedback, tutoring support, summaries, and first-pass grading. Live classroom management, adolescent trust, labs or activities, and school accountability still need people.
AI can help with examples, lesson plans, quizzes, rubrics, summaries, tutoring prompts, translation, and parent or student communication. The employee upside is capped because pay usually follows public-school salary schedules, and districts can capture the productivity lift through standardized materials or larger workload expectations.
The moat comes from state secondary certification, subject preparation, student teaching, school accountability, and a classroom setting robots cannot replace. The formal gate is real, even though software reaches a larger share of grading and content work.
Federal physical-requirements data shows light lifting and a high standing/walking share. High school teaching is school-building work: classrooms, hallways, labs for some subjects, lunch or detention supervision, student behavior, crowds, and public-facing stress. It is not heavy field work, but it is not pure desk work.
Public high-school teachers generally need state certification or licensure for the grade level, often tied to subject background. That creates a meaningful gate to public-school employment. Private schools, alternative routes, and emergency credentials keep it below the strongest licensed professions, but the public path is not open-entry work.
Classroom supervision, adolescent behavior, labs and activities, family communication, school rules, and the adult authority inside a changing room have no credible robot replacement path. The relevant pressure is software reaching content and grading tasks, not machines replacing teachers in normal high-school classrooms.
The public job profile lists secondary teaching as Job Zone 4, and the entry path normally includes a bachelor's degree, subject preparation, teacher preparation, student teaching, certification exams, and state authorization. That is substantial preparation, but shorter than the deepest professional-school paths.
Demand has scale, not momentum: replacement hiring keeps many openings visible, while the national employment line declines. Subject shortages can improve the local case, but enrollment, class-size policy, district budgets, and real wage pressure keep broad demand in the middle.
Secondary teaching remains a million-worker market: federal projections show about 1.09 million jobs and about 66,000 annual openings. The same source shows decline near 2%, so the size of the base does not make the hiring signal strong.
Evidence points in two directions. High schools need accountable adults, replacement hiring is large, and subject shortages can be real. At the same time, the national occupation is tied to enrollment patterns, state and local budgets, class-size policy, and district staffing choices.
Instruction, assessment, supervision, safety, family contact, and school rules still require human teachers. The weak spots are public budgets, class sizes, enrollment, and pay. The 2015 median wage benchmark inflates above the current median wage, so the real-wage pressure is visible.
The case weakens if districts use AI tutoring, prebuilt materials, automated feedback, and larger classes to reduce demand for teachers in standardized courses. The trigger is staffing and class-size change across normal schools over time, not one software pilot alone.
The case improves if math, science, bilingual, career-technical, or other hard-to-staff subjects show sustained vacancies, stipends, and stable class sizes. The proof is sustained local hiring power in a specific subject area, not the broad teacher title alone over time.
The case improves a little if districts use AI to reduce grading, feedback, translation, and preparation workload while keeping human class sizes and teacher authority intact. The proof is teachers actually getting time back, not only more software in the workflow.