IS WORK OVER?

Automation index

How much of a Special-education teacher’s job can AI already do?

An IEP comes earlier. Adapting so they are really in the group do not.

This percentage does not predict how many people will lose their jobs.

Task comparison

Special-education teacher

Where the time goes

Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.

The time saving for each task is estimated separately.

TaskShareAIPoints
IEP, aims and coordination.
Estimate

No public source provides a precise figure. This is our estimate.

16%40%+6.4
Classroom, support and the one who overflows.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
Family, the report and not selling miracles.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
Yard, inclusion and the one left alone.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Staff, resources and fighting for the place.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Really in the group, not on paper.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%22%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • IEP
  • classroom support
  • family reports
  • inclusion
  • the yard
  • resources
  • places
  • overflow

Sources for this job· O*NET 25-2051.00· ESCO/ISCO 2352

Last-mile manager

Where the time goes

Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.

The time saving for each task is estimated separately.

TaskShareAIPoints
Zones, capacity and route cuts.
Estimate

No public source provides a precise figure. This is our estimate.

16%44%+7
KPIs, first attempt and incidents.
Estimate

No public source provides a precise figure. This is our estimate.

16%36%+5.8
The courier, the lobby and the one who will not open.
Estimate

No public source provides a precise figure. This is our estimate.

18%16%+2.9
Peaks, rain and hiring too many or too few.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The angry customer and not dressing delivered.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Cover the peak, and not leaving the neighbourhood orphaned.
Estimate

No public source provides a precise figure. This is our estimate.

16%14%+2.2
Total100%26%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • delivery zones
  • first attempt
  • delivery KPIs
  • last-mile peaks
  • lobbies
  • delivery incidents
  • courier capacity
  • cut routes

Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324

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