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
IS WORK OVER?
Special-education teacher
22%
AI can already do 22% of this job.
#349 / 633
View rankingSpecial-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.
| Task | Share | AI | Points |
|---|---|---|---|
IEP, aims and coordination.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Classroom, support and the one who overflows.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Family, the report and not selling miracles.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Yard, inclusion and the one left alone.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Staff, resources and fighting for the place.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Really in the group, not on paper.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 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
IS WORK OVER?
ML engineer
29%
#64 / 633
View rankingML engineer
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.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511