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AI Automation Index
Will AI Replace Social Sciences Teachers, Postsecondary, All Other?
Social Sciences Teachers, Postsecondary, All Other: median pay, employment level, and why O*NET publishes no task inventory for this occupation code.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
Not scored
O*NET publishes no task inventory for this code, so there is nothing to score. We show the title, description, pay, and employment rather than invent a number.
Median Salary
$72,990 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Low
16,580 people hold this job — size, not growth
Projected 2025–35
+1.6%
employment change
1,300 openings a year · BLS line item
Pay by state
BLS OEWS publishes 41 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$37,490 – $132,920
Highest medians
California $132,920
Rhode Island $126,630
Minnesota $102,430
Utah $101,970
Massachusetts $98,260
Lowest medians
Arkansas $37,490
Pennsylvania $48,710
Kansas $51,540
Georgia $53,660
Florida $57,180
State figures come from the 25-1069 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
O*NET rates no tasks for this occupation, so there is no task mix to decompose. The number is absent on purpose — a zero here would read as "safe", which is a different claim.
Constituent Task Mix
This is a residual O*NET code — "All Other" catch-alls and military branches have no published task inventory, which is why there is no mix to show.
Constituent Tasks
No task statements exist for this code in O*NET release 31.0, so there is nothing to list. Pay and employment above are separate BLS data and are shown only where BLS covers the occupation — the military branches are covered by neither.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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