Share and intensity of work current AI systems can materially affect.
SOC 25-9043
Teaching Assistants, Special Education AI displacement risk
Supporting students with disabilities means one-on-one presence: behavior support, mobility assistance, daily-living instruction, and implementing IEP accommodations. No software provides the patient, physical, relational support these students need.
Likely potential for exposed tasks to move to software after workflow integration.
Districts cannot hire enough special education paraprofessionals, and the work's core — de-escalating a meltdown, guiding a wheelchair transfer, reinforcing a communication skill — is irreducibly human. Documentation tools only lighten the clerical load.
Distribution
Where Teaching Assistants, Special Education sits across 620 tracked roles
Displacement pressure 14 — higher than 8% 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.
30 O*NET task statements matched to SOC 25-9043. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $36,430 (Fallback estimate; May 2025 median unavailable, US national). BLS does not publish an exact current median for this occupational split, so the page retains a clearly labeled fallback estimate.
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, Special Education
SOC 25-9043 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 14/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-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, Special Education
The current evidence import matched 30 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 tasks30
SOC25-9043
- Core task / ID 22506
Provide assistance to students with special needs.
- Core task / ID 22511
Teach socially acceptable behavior, employing techniques such as behavior modification or positive reinforcement.
- Core task / ID 22509
Supervise students in classrooms, halls, cafeterias, school yards, and gymnasiums, or on field trips.
- Core task / ID 22507
Provide students with disabilities with assistive devices, supportive technology, and assistance accessing facilities, such as restrooms.
- Core task / ID 22487
Carry out therapeutic regimens, such as behavior modification and personal development programs, under the supervision of special education instructors, psychologists, or speech-language pathologists.
- Core task / ID 22512
Tutor and assist children individually or in small groups to help them master assignments and to reinforce learning concepts presented by teachers.
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
Provide one-on-one student support
Exposure 12, automation 2%, augmentation 24%.
Implement behavior and therapy regimens
Exposure 16, automation 4%, augmentation 32%.
O*NET evidence: Carry out therapeutic regimens, such as behavior modification and personal development ... (ID 22487)
Support daily living and mobility needs
Exposure 10, automation 2%, augmentation 18%.
O*NET evidence: Instruct students in daily living skills required for independent maintenance and self-... (ID 22495)
Record performance and progress data
Exposure 48, automation 24%, augmentation 60%.
O*NET evidence: Observe students' performance, and record relevant data to assess progress. (ID 22498)
TaskExposureAutomationAugmentation
Provide one-on-one student support122%24%
Implement behavior and therapy regimens164%32%
Support daily living and mobility needs102%18%
Record performance and progress data4824%60%
Transition pathways
Adjacent moves that preserve existing skills
credentialed transition
LowSpecial Education Teacher
Training horizon: 12-24 months. Skill overlap 68. Wage preservation signal 182.
- Enter a teacher preparation program
- Complete student teaching in special education
- Pass licensure exams
credentialed transition
LowBehavior Technician
Training horizon: 3-9 months. Skill overlap 66. Wage preservation signal 112.
- Earn RBT certification
- Learn applied behavior analysis basics
- Document behavior data systematically
Comparison guides
Compare the next move before you commit
12-24 months
Teaching Assistants, Special Education to Special Education Teacher
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Teaching Assistants, Special Education into Special Education Teacher.
3-9 months
Teaching Assistants, Special Education to Behavior Technician
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Teaching Assistants, Special Education into Behavior Technician.
What the AI risk score means for Teaching Assistants, Special Education
The displacement pressure score for Teaching Assistants, Special Education is 14. 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. Record performance and progress data carries 24% automation pressure, while Record performance and progress data carries 60% 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: $36,430 (Fallback estimate; May 2025 median unavailable, US national). Employment context: Special education support role with acute district shortages. Typical education: Some college or associate degree common.
Wage vulnerability is 74, 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.
- Very low displacement pressure
- Severe staffing shortage
- IEP-driven demand is legally anchored
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Teaching Assistants, Special Education, 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.
Behavior 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.
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.
Patience and reliability
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.
Documentation
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.
- 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.
- By 60 days, complete one small project connected to Special Education Teacher, such as enter a teacher preparation program.
- By 90 days, compare internal openings and external postings for Special Education Teacher or Behavior Technician and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Teaching Assistants, Special Education
Will AI replace Teaching Assistants, Special Education?
Supporting students with disabilities means one-on-one presence: behavior support, mobility assistance, daily-living instruction, and implementing IEP accommodations. No software provides the patient, physical, relational support these students need. 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, Special Education work are most exposed to AI?
Record performance and progress data and Implement behavior and therapy regimens show the strongest automation pressure in this model. Record performance and progress data and Implement behavior and therapy regimens are better treated as AI-augmented work.
What should Teaching Assistants, Special Education learn next?
Start with Behavior support, Student support, Patience and reliability. The most practical adjacent paths in this model are Special Education Teacher and Behavior Technician.
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
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics