Educational Instruction and Library · United States (national)
Special Education Teachers, Elementary School
How exposed this work is to automation, shown as ranges with the reasoning and sources.
The short version: this role is less exposed to automation, and a near-term shift looks unlikely.
It is a sourced range, not a verdict, and the detail is below.
Built on O*NET tasks, BLS pay and projections, and peer-reviewed AI-exposure research. Sources shown.
Data snapshot SNAPSHOT-2026-11-06. How it works
AI / softwarerange 23.0 to 47.0
Physical / roboticsrange 0.0 to 12.0
When the change is likely
Largely unaffectedlocal demand stable
What to do about it
Use AI to outpace AI.
Strategies to protect your livelihood, most important first.
1
Stay sharp; urgency is low
This role looks durable. Keep your skills current and watch for shifts in your field.
2
Pivot toward Special Education Teachers, Secondary School
It is skill-adjacent and less exposed (29.5 vs 35). A lateral move now beats a forced one later.
3
Build a financial buffer
Keep 6 to 12 months of expenses and diversify your income, so a disruption is a setback rather than a catastrophe.
4
Add a recognised credential
A certification or license raises the bar against automation and signals value in a tightening market.
Keep the monitor on
Free shows where you stand today. JobBreak Monitor writes your full AI-researched plan and keeps watching, so you know the moment your outlook shifts.
Free
$0
See exactly where you stand.
Full job-risk score & timeline
Task-by-task exposure breakdown
One starter strategy
AI-researched personal plan
Ongoing monitoring & alerts
Current plan
Keep ahead
JobBreak Monitor
$3.99/mo
$29/yr
or $29/yr save 39%
that's about $2.42/mo, billed yearly
Your full researched plan, plus a watch on your job.
Everything in Free
Full AI-researched, cited action plan
Resume-aware personalisation
Re-run your plan any time
Alerts when your outlook shifts
Start monitoring
Secure checkout by Stripe. Cancel anytime.
Which of my tasks are exposed
Administer standardized ability and achievement tests to elementary students with special needs.
50.0
Attend professional meetings, educational conferences, or teacher training workshops to maintain or improve professional competence.
0.0
Collaborate with other teachers or administrators to develop, evaluate, or revise elementary school programs.
100.0
Confer with other staff members to plan or schedule lessons promoting learning, following approved curricula.
100.0
Confer with parents, administrators, testing specialists, social workers, or other professionals to develop individual educational plans (IEPs) for students' educational, physical, or social development.
100.0
Coordinate placement of students with special needs into mainstream classes.
100.0
Develop or implement strategies to meet the needs of students with a variety of disabilities.
100.0
Encourage students to explore learning opportunities or persevere with challenging tasks to prepare them for later grades.
0.0
Longer, orange bars are more automatable; short green bars are resistant.
What is driving my result
lowers risk
raises risk
Task: Collaborate with other teachers or administrators to develop, evaluate, or revise elementary school programs.
Task: Confer with other staff members to plan or schedule lessons promoting learning, following approved curricula.
Lower-risk adjacent roles
Special Education Teachers, Secondary School
29.5
Special Education Teachers, Middle School
30.5
Special Education Teachers, Preschool
33.7
A full, ranked mitigation plan is part of the paid tiers.
How this score is calculated
Map Special Education Teachers, Elementary School to its standard O*NET task list (8 core tasks).
Score each task for AI/software exposure (Eloundou et al. 2023) and physical/robotics exposure (an O*NET work-context proxy), each 0 to 100.
The two axis scores are the task averages: AI 35.0, robotics 0.0.
The timeline comes from the higher axis (35.0), shifted for local demand (stable), and seniority. It is read against your remaining working years.
Ranges are about ±12 points. A backtest against real BLS employment change shows the risk ranking holds in aggregate, but individual outcomes are predicted weakly, so results are ranges, never a flat verdict.
Sources: O*NET, Eloundou et al. (2023), GPTs are GPTs, O*NET Work Context, BLS CPI-U, BLS OEWS.
Snapshot SNAPSHOT-2026-11-06. Full method and backtest on How it works.