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AI Automation Index
Will AI Replace Career/Technical Education Teachers, Postsecondary?
Task-level automation exposure for Career/Technical Education Teachers, Postsecondary: 20 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
56%
weighted task index
20 of 20 tasks scored
Median Salary
$63,820 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
High
114,110 people hold this job — size, not growth
Projected 2025–35
-0.3%
employment change
8,000 openings a year · BLS line item
Pay by state
BLS OEWS publishes 52 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$42,470 – $82,620
Highest medians
Wisconsin $82,620
South Carolina $79,610
Massachusetts $78,780
Washington $77,530
Alaska $77,310
Lowest medians
Puerto Rico $42,470
Kansas $48,790
Arkansas $50,120
Mississippi $50,670
Utah $50,740
State figures come from the 25-1194 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.
Automation potential 56%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 0%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 76%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 13%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 17/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
20 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 59%
12 tasks
Medium exposure (≥ 35%) 32%
6 tasks
Low exposure (below 35%) 10%
2 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#6446 · Core · 66% automatable
Observe and evaluate students' work to determine progress, provide feedback, and make suggestions for improvement.
decision support — Grading aided; rubric judgment and accountability human
Monitor student performance. Evaluate student work.
#6447 · Core · 45% automatable
Present lectures and conduct discussions to increase students' knowledge and competence using visual aids, such as graphs, charts, videotapes, and slides.
interpersonal — Selecting and applying methods to real learners is human
Apply multiple teaching methods.
#6445 · Core · 62% automatable
Supervise and monitor students' use of tools and equipment.
data analysis — AI grades and tracks metrics; human interprets holistically
Monitor student performance.
#6448 · Core · 45% automatable
Administer oral, written, or performance tests to measure progress and to evaluate training effectiveness.
interpersonal — Test administration requires proctoring and live interaction
Administer tests to assess educational needs or progress.
#6452 · Core · 55% automatable
Provide individualized instruction and tutorial or remedial instruction.
interpersonal — AI can tutor content; diagnosing learner needs is human
Tutor students who need extra assistance.
#6449 · Core · 80% automatable
Prepare reports and maintain records, such as student grades, attendance rolls, and training activity details.
text generation — Draft student activity/performance reports from records
Maintain student records. Prepare reports detailing student activities or performance.
#6454 · Core · 62% automatable
Develop curricula and plan course content and methods of instruction.
text generation — Drafts activity plans; educator adapts to learners
Plan educational activities.
#6451 · Core · 60% automatable
Determine training needs of students or workers.
decision support — Needs student interaction and professional judgment
Assess educational needs of students.
#6450 · Core · 30% automatable
Supervise independent or group projects, field placements, laboratory work, or other training.
interpersonal — Mentoring students requires relationship and judgment
Supervise laboratory work. Supervise student research or internship work.
#6456 · Core · 42% automatable
Integrate academic and vocational curricula so that students can obtain a variety of skills.
interpersonal — Vocational instruction blends demo, supervision, hands-on
Teach vocational courses.
#6458 · Core · 65% automatable
Select and assemble books, materials, supplies, and equipment for training, courses, or projects.
data analysis — Materials inventory records and forecasting automatable
Select educational materials or equipment. Maintain inventories of materials, equipment, or products.
#6453 · Core · 42% automatable
Conduct on-the-job training classes or training sessions to teach and demonstrate principles, techniques, procedures, or methods of designated subjects.
interpersonal — Vocational instruction blends demo, supervision, hands-on
Teach vocational courses.
#20106 · Core · 70% automatable
Acquire, maintain, and repair laboratory equipment and tools.
data analysis — Materials inventory records and forecasting automatable
Maintain inventories of materials, equipment, or products.
#6455 · Core · 72% automatable
Prepare outlines of instructional programs and training schedules and establish course goals.
text generation — Drafts instructional objectives; educator refines and validates
Develop instructional objectives.
#6459 · Core · 60% automatable
Advise students on course selection, career decisions, and other academic and vocational concerns.
interpersonal — Career guidance drafted; mentor relationship human
Advise students on academic or career matters.
#6460 · Core · 18% automatable
Participate in conferences, seminars, and training sessions to keep abreast of developments in the field, and integrate relevant information into training programs.
interpersonal — Attending meetings requires physical human presence
Attend training sessions or professional meetings to develop or maintain professional knowledge.
#6457 · Core · 72% automatable
Develop teaching aids, such as instructional software, multimedia visual aids, or study materials.
text generation — Generate learning content; human owns pedagogy
Create technology-based learning materials.
#6461 · Core · 35% automatable
Serve on faculty and school committees concerned with budgeting, curriculum revision, and course and diploma requirements.
interpersonal — Committee service requires presence, negotiation, accountability
Serve on institutional or departmental committees.
#6463 · Core · 68% automatable
Arrange for lectures by experts in designated fields.
data analysis — AI builds instructional schedules; human adjusts
Schedule instructional activities.
#6462 · Supplemental · 62% automatable
Review enrollment applications and correspond with applicants to obtain additional information.
data analysis — Registration data entry automatable; verification and exceptions human
Perform student enrollment or registration activities.
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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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