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Occupations

Secondary School Teachers

SOC 25-2031Knowledge / AIOctober 2026 assessment2025 employment 1.09M

Teach academic subjects to students at the secondary-school level, excluding special education and career or technical education.

Occupation description · O*NET
Current assessmentOctober 2026Independent analysis for a changing world of work.
Current EOL outlook

Forecast and analytical state

See how this assessment evolved →
2030 working outlook−1% to +2%
−10%0%+10%
2035 working outlook−3% to +2%
−10%0%+10%
2035 reference points
BLS structural projection−0.2%
Metaculus probabilistic forecast−5.6%
Reference points, not inputs mechanically averaged into EOL.
Bottom line

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.

Six-stage assessment

Where the evidence stands now

Same framework used across every EOL occupation
01CapabilityAdvanced for preparation, content, tutoring, grading and administrative supportView evidence → 02Economic ViabilityStrong for support/preparation; Weak-to-Moderate for direct substitutionView evidence → 03AdoptionModerate-to-high and broadeningView evidence → 04Task SubsumptionMaterial in preparation/admin work; limited in core classroom deliveryView evidence → 05Human Labor DependencyHighView evidence → 06Labor-Market EffectsNo clear AI-driven displacement effectView evidence →

Transparent by design. Every stage judgment is traceable to the evidence supporting the current assessment.

View all Secondary School Teachers evidence →
Historical ContinuityReasonable
What this means →
ReasonableIncreasingly questionableUnlikely reliable
View all evidence in Signal Tracker →Read the methodology →Jump to labor supply →
Detailed occupation assessment
Forecast ContextView detail ›
Employment / inflection

No clear technology-driven aggregate inflection visible

Forecast rationale

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.

Demand / countervailing pressure

Student population, class-size policy and school funding dominate demand; demographics may outweigh AI near term.

Key mechanism

Task automation without role automation; students per teacher.

How EOL translates evidence into a forecast range →
04

Six-Stage Transition Assessment

Expanded analysis

Capability

AI can prepare lesson materials, tutoring content, summaries and feedback, but not fully manage classrooms. | Qualification: UK primary versus US secondary.

Advanced for preparation, content, tutoring, grading and administrative support
Moderate-high confidence
05

Evidence Synthesis

Evidence tilt
Balanced evidence

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.

Historical continuity
Reasonable

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.

06

Labor Supply

Pipeline
Bachelor’s degree + state certification or licensure
Supply trend
Mixed and difficult to assess nationally
Replenishment burden
5.8% of employment annually
Demand-supply alignment
Broadly balanced nationally, with meaningful risk of mismatch by subject and geography
Supply implication for the forecast

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 →
07

Forecast Bridge

The analysis above is the evidence base. The bridge below summarizes how those findings translate into the 2030 and 2035 ranges without repeating the full six-stage assessment.

What BLS already captures

BLS already reflects student demographics, school funding, class-size and staffing relationships, and the historical effects of education technology. Those forces remain the dominant structural reference for EOL.

Why EOL still differs

The residual departure is deliberately small because task automation has not yet established a mechanism for broad teacher-role substitution. EOL allows only gradual staffing leverage if students per teacher can rise without unacceptable losses in supervision or outcomes.

2030−1% to +2%

Neutral structural bridge: roughly flat. This is an EOL benchmark derived from the BLS ten-year path, not a BLS 2030 forecast.

Lower-employment edge

At the lower-employment edge, EOL assumes AI-driven preparation, grading, tutoring, and administrative productivity gradually permit higher student-to-teacher ratios without a wholesale replacement of the teacher role. That puts employment about 0.9% below the neutral structural reference. The 2030 bound remains constrained by the limited time available for diffusion and organizational redesign.

Upper-employment edge

At the upper-employment edge, EOL assumes class-size policy, supervision, school structure, and student needs prevent productivity gains from translating into materially lower teacher staffing. That puts employment about 2.1% above the neutral structural reference.

2035−3% to +2%

BLS structural projection: −0.2%.

Lower-employment edge

At the lower-employment edge, EOL assumes AI-driven preparation, grading, tutoring, and administrative productivity gradually permit higher student-to-teacher ratios without a wholesale replacement of the teacher role. With more time for adoption and workflow redesign, that puts employment about 2.8% below the BLS structural path.

Upper-employment edge

At the upper-employment edge, EOL assumes class-size policy, supervision, school structure, and student needs prevent productivity gains from translating into materially lower teacher staffing. That puts employment about 2.2% above the BLS structural path.

Why not materially higher or lower?

A materially lower outcome would require the greater-displacement conditions to be established at broader scale than current evidence supports: Sustained increases in students per teacher without adverse outcomes; AI takes over grading, preparation and tutoring at scale; districts redesign staffing around technology. A materially higher outcome would require the employment-supportive conditions to dominate more strongly than current evidence supports: Class-size, supervision and relationship requirements remain binding; teacher shortages persist; AI reduces workload and improves quality without changing staffing ratios.

September revalidationv1.8 independent validation adjudicated
08

What Would Change Our View?

Toward greater displacement
Sustained increases in students per teacher without adverse outcomes.
⌄
Sustained increases in students per teacher without adverse outcomes; AI takes over grading, preparation and tutoring at scale; districts redesign staffing around technology.
Toward greater employment
Class-size, supervision and relationship requirements remain binding.
⌄
Class-size, supervision and relationship requirements remain binding; teacher shortages persist; AI reduces workload and improves quality without changing staffing ratios.
Signal Tracker

See how this assessment evolves

EOL preserves meaningful assessment updates, forecast holds, forecast changes, and BLS structural rebases over time.

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09

Sources and Assessment History

Current assessment
October 2026EOL occupation assessment
Structural reference
BLSEmployment Projections · SOC 25-2031
External calibration
MetaculusK-12 Teachers · Broader proxy
Supply evidence
BLS / state credential sourcesTeacher pipeline, certification and replacement demand