SOC 25-1071

Health Specialties Teachers, Postsecondary AI displacement risk

Teaching future clinicians blends current practice with instruction: lectures and grading augment easily with AI, while laboratory supervision, clinical precepting, and modeling professional judgment for students remain practitioner work.

Exposure46

Share and intensity of work current AI systems can materially affect.

Automation20%

Likely potential for exposed tasks to move to software after workflow integration.

Risk bandLow

Health programs expand to meet workforce shortages, and faculty must hold current clinical credentials — a structural limit on substitution. Content delivery automates at the margins; lab and clinic instruction does not.

Distribution

Where Health Specialties Teachers, Postsecondary sits across 620 tracked roles

Health Specialties Teachers, Postsecondary · 26050100

Displacement pressure 26 — higher than 37% of the 620 occupations tracked on displacement.ai.

Score version

This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.

22 O*NET task statements matched to SOC 25-1071. The displayed task profile combines these official task statements with the current public score model.

Median wage context: $107,310 (May 2025, US national). The latest BLS row matched SOC 25-1071.

Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.

2030 economic stress test

How Anthropic's scenarios classify Health Specialties Teachers, Postsecondary

SOC 25-1071 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 26/100 role score and are not an occupation forecast.

Modest change

+0.4% group wage

-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.

Economy-wide: +1.6% GDP and 3.9% unemployment.

Substantial change

-0.3% group wage

-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.

Economy-wide: +8.3% GDP and 4.6% unemployment.

Extreme change

-11.5% group wage

-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.

Economy-wide: +32.4% GDP and 11.9% unemployment.

Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.

Official task evidence

O*NET task matches for Health Specialties Teachers, Postsecondary

The current evidence import matched 22 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.

Dataset31.0 (August 2026)
Matched tasks22
SOC25-1071
  • Core task / ID 6103

    Prepare course materials, such as syllabi, homework assignments, and handouts.

  • Core task / ID 6100

    Keep abreast of developments in the field by reading current literature, talking with colleagues, and participating in professional conferences.

  • Core task / ID 6102

    Evaluate and grade students' class work, assignments, and papers.

  • Core task / ID 6109

    Supervise laboratory sessions.

  • Core task / ID 6101

    Compile, administer, and grade examinations, or assign this work to others.

  • Core task / ID 6110

    Maintain student attendance records, grades, and other required records.

Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.

Task profile

Where AI changes the work

language

Prepare and deliver health lectures

Exposure 54, automation 24%, augmentation 72%.

O*NET evidence: Prepare and deliver lectures to undergraduate or graduate students on topics such as pu... (ID 6104)

social

Supervise laboratory sessions

Exposure 18, automation 5%, augmentation 34%.

O*NET evidence: Supervise laboratory sessions. (ID 6109)

analytical

Evaluate and grade student work

Exposure 50, automation 26%, augmentation 64%.

O*NET evidence: Evaluate and grade students' class work, assignments, and papers. (ID 6102)

social

Advise students on clinical careers

Exposure 26, automation 8%, augmentation 46%.

O*NET evidence: Advise students on academic and vocational curricula and on career issues. (ID 6112)

TaskExposureAutomationAugmentation
Prepare and deliver health lectures5424%72%
Supervise laboratory sessions185%34%
Evaluate and grade student work5026%64%
Advise students on clinical careers268%46%

Transition pathways

Adjacent moves that preserve existing skills

credentialed transition

Department Chair

Training horizon: 12-24 months. Skill overlap 66. Wage preservation signal 118.

  • Lead curriculum review
  • Manage faculty teams
  • Own accreditation outcomes
Low
role redesign

Clinical Education Coordinator

Training horizon: 2-5 months. Skill overlap 72. Wage preservation signal 96.

  • Manage placement logistics
  • Standardize evaluation rubrics
  • Adopt AI-assisted teaching tools
Low

Comparison guides

Compare the next move before you commit

What the AI risk score means for Health Specialties Teachers, Postsecondary

The displacement pressure score for Health Specialties Teachers, Postsecondary is 26. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.

For this role, the clearest risk pattern is visible at the task level. Evaluate and grade student work carries 26% automation pressure, while Prepare and deliver health lectures carries 72% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.

Labor-market context and wage risk

Median wage: $107,310 (May 2025, US national). Employment context: Medical and health faculty role with practitioner-educator demand. Typical education: Doctoral or professional degree plus clinical practice.

Wage vulnerability is 34, while transition feasibility is 68. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.

  • Low displacement pressure
  • Health program expansion drives demand
  • Credential requirements limit substitution

Upskilling priorities

Skills that make this role more resilient

The safest upskilling plan starts with skills already close to the work. For Health Specialties Teachers, Postsecondary, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.

Priority 1

Clinical expertise

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 2

Curriculum design

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 3

Student assessment

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

Priority 4

Laboratory supervision

Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.

90-day transition plan

The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.

  1. In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
  2. By 60 days, complete one small project connected to Department Chair, such as lead curriculum review.
  3. By 90 days, compare internal openings and external postings for Department Chair or Clinical Education Coordinator and update your resume around measurable workflow outcomes.

FAQ

Questions about AI and Health Specialties Teachers, Postsecondary

Will AI replace Health Specialties Teachers, Postsecondary?

Teaching future clinicians blends current practice with instruction: lectures and grading augment easily with AI, while laboratory supervision, clinical precepting, and modeling professional judgment for students remain practitioner work. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.

Which parts of Health Specialties Teachers, Postsecondary work are most exposed to AI?

Evaluate and grade student work and Prepare and deliver health lectures show the strongest automation pressure in this model. Prepare and deliver health lectures and Evaluate and grade student work are better treated as AI-augmented work.

What should Health Specialties Teachers, Postsecondary learn next?

Start with Clinical expertise, Curriculum design, Student assessment. The most practical adjacent paths in this model are Department Chair and Clinical Education Coordinator.

How should this score be used?

Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.

Sources

Evidence trail