Benchmark Job

Special Education Teachers, Middle School

Special Education Teachers, Middle School is a hybrid role where automation assists execution but does not fully replace ownership.

Teach academic, social, and life skills to middle school students with learning, emotional, or physical disabilities. Includes teachers who specialize and work with students who are blind or have visual impairments; students who are deaf or have hearing impairments; and students with intellectual disabilities.

44% exposure • High confidenceToday

Category: Educational Instruction and Library • Industry: Administration of Veterans' Affairs (92)

Why this score

How we calculate

Top 3 drivers

  • Routine process execution: 32% task weight, 58% automation potential.
  • Documentation and reporting: 28% task weight, 48% automation potential.
  • Stakeholder communication: 22% task weight, 34% automation potential.

Top 2 blockers

  • Exception handling: Edge cases and accountability ownership.
  • Stakeholder communication: Empathy, negotiation, and situational nuance.

Confidence reasons

  • 1,016 benchmark role descriptions are mapped across O*NET + NAICS sources.
  • Coverage spans 23 categories and 1,012 industries.
  • Adjacent role consistency band is ±2 points for this benchmark family.

Task Mix

Routine process execution (32% weight • 19 contribution)58%

Mixed automation potential with meaningful human oversight.

Documentation and reporting (28% weight • 13 contribution)48%

Mixed automation potential with meaningful human oversight.

Stakeholder communication (22% weight • 7 contribution)34%

High judgment, trust, or contextual complexity keeps this human-led.

Exception handling (18% weight • 4 contribution)24%

High judgment, trust, or contextual complexity keeps this human-led.

Recommended Stack

Click a stack item to open setup difficulty, time-to-value, and workflow details.

Replaceable tasks vs human tasks

AI-suitable tasks

  • Routine process execution58% automation potential • Workflow automation + RPA
  • Documentation and reporting48% automation potential • Document AI + reporting automation
  • Stakeholder communication34% automation potential • Template assistant + CRM automation

Human-needed tasks

  • Exception handlingAI struggles because edge cases and accountability ownership.
  • Stakeholder communicationAI struggles because empathy, negotiation, and situational nuance.
  • Documentation and reportingAI struggles because liability and audit-quality requirements.

Tools coverage meter

This stack covers 33% of automatable tasks.

Remaining gaps: exceptions, communication, edge cases.

Automation timeline

Current mode: TodayExposure estimate: 44%tools now

What to pivot into

Compare roles

Safer adjacent roles

Higher-paying adjacent roles

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  • Legal Support Workers, All OtherExposure: 42% • Salary: $123,000+$38,000 • Exposure tradeoff: +2 pts
  • PhysicistsExposure: 24% • Salary: $119,500+$34,500 • Exposure tradeoff: -16 pts
  • Animal ScientistsExposure: 30% • Salary: $118,500+$33,500 • Exposure tradeoff: -10 pts
  • SociologistsExposure: 34% • Salary: $117,500+$32,500 • Exposure tradeoff: -6 pts
  • Anthropologists and ArcheologistsExposure: 38% • Salary: $116,500+$31,500 • Exposure tradeoff: -2 pts

Examples of automations

Scheduling automation examples
  1. Capture requests from form/email into one queue
  2. Auto-propose slots using Scheduling workflow
  3. Send reminders and update status automatically
  4. Escalate conflicts to a supervisor with context
Reporting automation examples
  1. Aggregate daily operations into one reporting table
  2. Generate dashboards with Reporting automation
  3. Create weekly summary narratives with risk flags
  4. Distribute summaries by role with required actions
Communication templating examples
  1. Classify inbound messages by intent and urgency
  2. Draft replies with Communication copilot
  3. Insert policy-approved language and context snippets
  4. Queue sensitive drafts for manager approval
Escalation handling examples
  1. Detect exception triggers from job events
  2. Route to specialist based on severity policy
  3. Attach timeline, customer context, and prior actions
  4. Track closure and feed outcomes into playbooks

Benchmark data source

Last updated: March 4, 2026

Benchmarked descriptions used: 1,016 role descriptions mapped across 1,012 industries.

Data references for this role

  • Role profile and task mix

    Job title, SOC 25-2057.00, role description, task statements, and job-zone context.

    Source: O*NET Database 29.0 (Occupation Data + Job Zones)O*NET Resource Center / U.S. Department of Labor

  • Industry and sector mapping

    Industry code 923140, sector 92, and category mapping shown on this role.

    Source: NAICS 2022 6-Digit CodesU.S. Census Bureau

  • Exposure score, confidence, and timeline views

    Deterministic benchmark scoring, confidence tiers, stack coverage, and timeline projections derived from role/task inputs.

    Source: replaced.fyi methodologyreplaced.fyi

  • Related roles and pivot recommendations

    Adjacent-role comparisons and safer/higher-pay pivot suggestions computed from the same benchmark catalog.

    Source: replaced.fyi rankings datasetreplaced.fyi

Primary source list

Confidence definition: High confidence means broad role coverage, benchmark consistency across adjacent roles, and stable task-level scoring signals.

MethodologyRankings

Special Education Teachers, Middle School benchmark - replaced.fyi