SOC 25-9042

Teaching Assistants AI displacement risk

Grading support, material preparation, and record keeping are augmentable with AI tools. Supervising students, one-on-one support for children with special needs, and classroom presence remain hands-on work that schools continue to hire for.

Exposure48

Share and intensity of work current AI systems can materially affect.

Automation24%

Likely potential for exposed tasks to move to software after workflow integration.

Risk bandModerate

This record uses a K-12 assistant proxy SOC. Special-education support is the most durable segment; clerical grading and material prep are the most augmentable.

Distribution

Where Teaching Assistants sits across 620 tracked roles

Teaching Assistants · 36050100

Displacement pressure 36 — higher than 61% of the 620 occupations tracked on displacement.ai.

Score version

This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.

28 O*NET task statements matched to SOC 25-9042. The displayed task profile combines these official task statements with the current public score model.

Median wage context: $37,010 (May 2025, US national). The latest BLS row matched SOC 25-9040.

Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.

2030 economic stress test

How Anthropic's scenarios classify Teaching Assistants

SOC 25-9040 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 36/100 role score and are not an occupation forecast.

Modest change

+0.4% group wage

-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.

Economy-wide: +1.6% GDP and 3.9% unemployment.

Substantial change

-0.3% group wage

-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.

Economy-wide: +8.3% GDP and 4.6% unemployment.

Extreme change

-11.5% group wage

-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.

Economy-wide: +32.4% GDP and 11.9% unemployment.

Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.

Official task evidence

O*NET task matches for Teaching Assistants

The current evidence import matched 28 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.

Dataset31.0 (August 2026)
Matched tasks28
SOC25-9042
  • Core task / ID 22478

    Supervise students in classrooms, halls, cafeterias, school yards, and gymnasiums, or on field trips.

  • Core task / ID 22481

    Tutor and assist children individually or in small groups to help them master assignments and to reinforce learning concepts presented by teachers.

  • Core task / ID 22463

    Enforce administration policies and rules governing students.

  • Core task / ID 22480

    Teach social skills to students.

  • Core task / ID 22465

    Instruct and monitor students in the use and care of equipment and materials to prevent injuries and damage.

  • Core task / ID 22460

    Discuss assigned duties with classroom teachers to coordinate instructional efforts.

Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.

Task profile

Where AI changes the work

social

Supervise students across settings

Exposure 14, automation 3%, augmentation 20%.

social

Tutor students individually or in groups

Exposure 38, automation 14%, augmentation 52%.

O*NET evidence: Tutor and assist children individually or in small groups to help them master assignmen... (ID 22481)

information

Grade homework and record results

Exposure 56, automation 32%, augmentation 54%.

O*NET evidence: Grade homework and tests, and compute and record results, using answer sheets or electr... (ID 22464)

language

Prepare lesson materials

Exposure 64, automation 30%, augmentation 62%.

O*NET evidence: Prepare lesson materials, bulletin board displays, exhibits, equipment, and demonstrati... (ID 22474)

TaskExposureAutomationAugmentation
Supervise students across settings143%20%
Tutor students individually or in groups3814%52%
Grade homework and record results5632%54%
Prepare lesson materials6430%62%

Transition pathways

Adjacent moves that preserve existing skills

adjacent role

Special Education Paraprofessional

Training horizon: 2-6 months. Skill overlap 76. Wage preservation signal 104.

  • Learn IEP support basics
  • Practice behavior support strategies
  • Document student progress data
Moderate
credentialed transition

Licensed Teacher

Training horizon: 12-24 months. Skill overlap 66. Wage preservation signal 168.

  • Enter a teacher preparation program
  • Complete student teaching
  • Pass state licensure exams
Moderate

Comparison guides

Compare the next move before you commit

What the AI risk score means for Teaching Assistants

The displacement pressure score for Teaching Assistants is 36. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.

For this role, the clearest risk pattern is visible at the task level. Grade homework and record results carries 32% automation pressure, while Prepare lesson materials carries 62% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.

Labor-market context and wage risk

Median wage: $37,010 (May 2025, US national). Employment context: Large classroom support role with special-education demand. Typical education: Some college or associate degree common.

Wage vulnerability is 72, while transition feasibility is 66. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.

  • Moderate augmentation pressure
  • Special education demand is strong
  • Classroom presence stays human

Upskilling priorities

Skills that make this role more resilient

The safest upskilling plan starts with skills already close to the work. For Teaching Assistants, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.

Priority 1

Student support

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 2

Behavioral supervision

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 3

Material preparation

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 4

Assistive technology

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

90-day transition plan

The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.

  1. In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
  2. By 60 days, complete one small project connected to Special Education Paraprofessional, such as learn iep support basics.
  3. By 90 days, compare internal openings and external postings for Special Education Paraprofessional or Licensed Teacher and update your resume around measurable workflow outcomes.

FAQ

Questions about AI and Teaching Assistants

Will AI replace Teaching Assistants?

Grading support, material preparation, and record keeping are augmentable with AI tools. Supervising students, one-on-one support for children with special needs, and classroom presence remain hands-on work that schools continue to hire for. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.

Which parts of Teaching Assistants work are most exposed to AI?

Grade homework and record results and Prepare lesson materials show the strongest automation pressure in this model. Prepare lesson materials and Grade homework and record results are better treated as AI-augmented work.

What should Teaching Assistants learn next?

Start with Student support, Behavioral supervision, Material preparation. The most practical adjacent paths in this model are Special Education Paraprofessional and Licensed Teacher.

How should this score be used?

Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.

Sources

Evidence trail