Automation index
How much of a Language teacher’s job can AI already do?
A drill and a correction come earlier. Getting a shy person talking for an hour does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Language teacher
23%
AI can already do 23% of this job.
#310 / 633
View rankingLanguage 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 |
|---|---|---|---|
Prepare lessons, worksheets and corrections.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
Class, conversation and group dynamics.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 12% | +2.6 |
Assess speaking, writing and levels.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Platform, homework and follow-up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Families, schools and official exams.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Not leaving someone mute in a language.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 23% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- teach class
- mark students
- lesson plans
- teach
- Class, conversation and group dynamics
- Assess speaking, writing and levels
- Platform, homework and follow-up
- Families, schools and official exams
- Not leaving someone mute in a language
- profesor de idiomas
Sources for this job· O*NET 25-3011.00· ESCO/ISCO 2353
IS WORK OVER?
Last-mile manager
26%
#211 / 633
View rankingLast-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.
| Task | Share | AI | Points |
|---|---|---|---|
Zones, capacity and route cuts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
KPIs, first attempt and incidents.EstimateNo 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.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Peaks, rain and hiring too many or too few.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The angry customer and not dressing delivered.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Cover the peak, and not leaving the neighbourhood orphaned.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 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