SOC 25-2021.00

ESL Teacher

VerdictHigher Risk — exposed on paper only
Signal 01 · structural exposure
68
/ 100

ESL Teacher shares its numeric structural-exposure score with Elementary School Teacher because O*NET/BLS classify K-12 teachers by grade band rather than subject — see that page for the full task-level breakdown of what's driving the number.

Signal 02 · real-world AI usage
Medium
band, not a number

In real usage data, AI shows up in this job about as often as the average occupation: not high, not negligible.

Technical detail

Based on Microsoft's "Working with AI" study of real Copilot conversations mapped to O*NET tasks (arXiv 2507.07935): Elementary School Teachers, Except Special Education scored 0.189 on AI applicability, within one standard deviation of the cross-occupation mean (0.159, stdev 0.098) across all 785 SOC codes studied — banded here as Medium real-world AI usage relative to other occupations.

Corroborating signal from the Anthropic Economic Index (Claude.ai/API conversations, a different product and userbase than the Microsoft data above): Elementary School Teachers, Except Special Education shows measurable Claude usage tied to this occupation's tasks, though at low absolute volume — treat as directional confirmation, not a second precise score.

What this is not
Neither reading measures whether any single esl teacher has lost work to AI. That data does not exist anywhere yet — both signals describe tasks, not headcount.
How we calculated this →

What a esl teacher actually does

ESL teachers (also called ELL or bilingual-education teachers) work with students who are still developing English proficiency, delivering language instruction alongside — or embedded within — general academic content.

Depending on the school's program model, an ESL teacher might pull small groups of newcomer students out of their regular classroom for targeted language instruction, co-teach inside a mainstream classroom alongside the general-education teacher, or run a self-contained bilingual classroom that delivers core subjects in both English and a student's home language.

A recurring, specialized task is administering and scoring oral-language proficiency assessments — most states use the WIDA ACCESS test, which requires the test administrator to complete annual training and pass a scoring-calibration check before they're authorized to rate a student's Speaking domain.

Beyond direct instruction, ESL teachers track each student's progress toward English-proficiency exit criteria, coordinate with general-education teachers on how to scaffold grade-level content, and often serve as a cultural liaison between immigrant or refugee families and the rest of the school.

Because English-learner enrollment is concentrated in early grades nationally, this role is most often folded into elementary staffing, though middle and high schools run their own ESL programs too — high schools currently report the highest ESL-specific vacancy rates of any grade band. Entry requires a bachelor's degree, a state teaching license, and a specific ESL or bilingual-education endorsement layered on top of that base license.

Note: O*NET/BLS classify teachers by grade level rather than subject, so this page shares its underlying occupational code with Elementary School Teacher — the numeric structural-exposure score below is therefore identical between the two pages, while the licensing, task, and demand evidence below is specific to the ESL/bilingual specialty.

Why it reads this way

ESL Teacher shares its numeric structural-exposure score with Elementary School Teacher because O*NET/BLS classify K-12 teachers by grade band rather than subject — see that page for the full task-level breakdown of what's driving the number. The ESL-specific case for AI resistance layers on top of that shared foundation.

Most states require an ESL teacher to hold a full teaching license plus a separate ESL or bilingual endorsem*nt — a two-layer credentialing wall most jobs on this site don't have to clear. Assessing a student's oral-language proficiency for program placement, using assessments like the WIDA ACCESS test most states rely on, requires live, in-person listening and speaking interaction with a trained, calibrated human rater — not something current AI systems are authorized or positioned to do.

Building trust with English-learner families, often navigating cultural and language barriers directly as the school's de facto liaison, is a relationship-based task rather than a content-generation one. This occupation isn't fully insulated from the broader pressure documented on the Elementary School Teacher page, though: several states introduced 2025-2026 legislation specifically regulating AI use in K-12 classrooms (South Carolina's H.

5253 would bar AI from replacing licensed teachers in core instruction), and at least one fully AI-driven 'teacherless' school model has opened campuses in multiple cities as of 2026. None of that reporting describes ESL-specific displacement, but it's a live, moving trend worth disclosing rather than ignoring.

Skills this role draws on

We don't yet have task-by-task time-share data for this occupation, so we can't show which specific tasks carry the exposure score above. This is the real skill set instead.

English Language Acquisition InstructionOral Proficiency Assessment (WIDA ACCESS)Bilingual/Co-Teaching CollaborationCultural Liaison & Family CommunicationDifferentiated InstructionProgress Monitoring Toward Exit CriteriaClassroom Management
Pay & demand

Typical salary range (USD)

$45,000–$80,000

Average: $72,650/yr

Demand
High
Growth outlook
Stable
Projected growth
-2% (2024-2034)

Range source: BLS Occupational Employment Statistics, May 2024
Average source: BLS Occupational Employment and Wage Statistics, May 2025 (Annual Mean Wage, SOC 25-2021)

Training
4-5 years
Bachelor's DegreeState Teaching LicenseESL/Bilingual-Education EndorsementStudent Teaching
Research verdict
Assessed as of 2026-08-10
AI-resistant despite the paperwork

As of 2026-08-10, ESL Teacher scores 32/100 on structural exposure — identical to Elementary School Teacher, since both pages share the O*NET-SOC code 25-2021.00 (O*NET/BLS classify K-12 teachers by grade band, not subject). See the Elementary School Teacher verdict for the shared task-level evidence behind that number, including active 2025-2026 state legislation regulating AI in classrooms (e.g. South Carolina's H.5253) and the emergence of fully AI-driven 'teacherless' school models (Alpha School) in multiple cities. Layered on top of that shared foundation, ESL-specific evidence is protective: category 2, a real two-layer credentialing wall (teaching license plus a separate state ESL/bilingual endorsem*nt); category 3, live oral-language proficiency assessment (e.g. WIDA ACCESS) requires an in-person, calibrated human rater, and family/cultural-liaison work is inherently relationship-based. No category 1 evidence of AI displacing ESL teachers specifically was found. Net verdict: AI-resistant despite the paperwork, Medium confidence — consistent with, and dependent on, the Elementary School Teacher verdict, so this page is flagged for the same accelerated 6-month re-review cycle given the active, moving AI-in-classrooms legislative and product trend.

Last updated: December 2025Source: BLS Occupational Employment Statistics, May 2024

Data sources & methodology

Salary data: BLS Occupational Employment Statistics, May 2024. Min/max figures represent the 10th–90th percentile annual wage range across the United States. Average figure sourced separately: BLS Occupational Employment and Wage Statistics, May 2025 (Annual Mean Wage, SOC 25-2021).

Automation Risk Score: Based on O*NET occupational analysis (25-2021.00) evaluating task complexity, physical requirements, social intelligence, and environmental variability. Methodology based on research from Frey & Osborne (Oxford, 2017).

Growth projections: -2% (2024-2034), based on BLS Occupational Outlook Handbook.

Learn more about our methodology