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Physics Teachers, Postsecondary

Teach courses pertaining to the laws of matter and energy. Includes both teachers primarily engaged in teaching and those who do a combination of teaching and research.

Also known as: Acoustics Teacher · Adjunct Professor · Adjunct Instructor · Assistant Professor · Associate Professor · Aerodynamics Teacher

AI Task Exposure Score

High exposure

More exposed than 61% of 968 occupations · Rank #343 (1 = most exposed)

This score estimates how exposed the tasks in a role are to current and near-term AI capabilities. It does not predict whether a specific person will lose a job.

Your free role snapshot

Start with the work, not the headline

This occupation baseline already contains different kinds of work. These are the two ends of its current task range and one useful action you can take now.

Most exposed task

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

Automatable 80/100 exposure

Most durable task

Maintain and repair laboratory equipment.

Durable 10/100 exposure

Useful next step

Make one change to the task mix

Compare one real week with the exposed tasks, test one supervised delegation, and redirect the saved time toward more durable work.

Want this mapped to your actual week? Know what to strengthen, what to delegate to AI with review, and what to do over the next 7, 30, and 90 days.

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Most exposed tasks

Highest structured exposure values in this role’s task mix — the work AI systems can already do most of.

  • Maintain student attendance records, grades, and other required records.80

Augmentable tasks

Work where AI assists rather than replaces — the productivity frontier of this role.

  • Prepare course materials, such as syllabi, homework assignments, and handouts.60
  • Compile, administer, and grade examinations, or assign this work to others.58
  • Evaluate and grade students' class work, laboratory work, assignments, and papers.55

Most durable tasks

Lowest exposure — typically judgment, relationships, physical presence, or accountability. This is the human moat.

  • Maintain and repair laboratory equipment.10
  • Supervise students' laboratory work.20
  • Maintain regularly scheduled office hours to advise and assist students.25

Task exposure values and classifications are published, versioned estimates audited against the anchored rubric. This page reads them from the data release rather than generating them at request time. Bars show exposure contribution relative to this role’s task mix.

What this means

A score of 39 puts Physics Teachers, Postsecondary in the third quartile of analyzed occupations. In practice, exposure this high is about the mix: 1 of 20 analyzed tasks lean automatable, 8 augmentable, and 11 durable. The useful question isn’t “will AI take this job” — it’s which tasks go first, which get faster, and where to reposition time. That’s what the personalized report maps against your actual week.

Lower-exposure adjacent roles

No adjacent role in the current data release is at least 10 points lower with ≥50% skill overlap — we don’t label anything “safer” unless the data supports it.

Labor-market context

  • $100,310median wage
  • 13,090employed
  • 1,300annual openings
  • +2.3%projected growth

Context only — labor statistics are not inputs to the exposure score. See methodology.

Turn your actual week into a plan

Know which work to strengthen, what to delegate to AI with review, and what to do over the next 7, 30, and 90 days. Your $9 plan includes a confidence-rated personal score, task matrix, human moat, and only genuinely lower-exposure adjacent roles.

Build my 7/30/90-day action plan — $9

Related roles

Adjacent by skills or family — no exposure claim implied.

How this score was calculated

The score is the plain average of the AI-exposure values of this role’s 20 analyzed tasks (equal task weights in this release — no hidden factors):

  • Automatable 1 task, averaging 80/100 exposure
  • Augmentable 8 tasks, averaging 50/100 exposure
  • Durable 11 tasks, averaging 26/100 exposure

score = (1×80 + 8×50 + 11×26) ÷ 20 ≈ 39

Every task’s exposure value was scored against a published anchored rubric that scores tasks as actually performed — physical, in-person, and accountability requirements included (data release 2026.07.12-r2). The band (High) is a corpus quartile; the percentile is tie-aware across all 968 occupations.

FAQ — Physics Teachers, Postsecondary

Will AI replace Physics Teachers, Postsecondary?
This score cannot predict whether this occupation or any individual job will be replaced. It shows which tasks current and near-term AI can perform or accelerate, which tasks remain more durable, and where a worker can adapt. Employer decisions, demand, regulation, and how the role changes are outside the score.
What does a score of 39 mean for a Physics Teachers, Postsecondary?
It means that, weighted across the 20 tasks we analyzed for this role, the task mix sits at 39 on a 0–100 exposure scale — in the third quartile of analyzed occupations. It measures task exposure to current and near-term AI capabilities, not the probability of losing a job.
Which tasks in this role are most exposed to AI?
The highest-exposure tasks are: Maintain student attendance records, grades, and other required records. Exposure comes from the published, rubric-scored data release rather than being generated when you open this page.
Which parts of this job are most durable?
The most durable responsibilities are: Maintain and repair laboratory equipment; Supervise students' laboratory work; Maintain regularly scheduled office hours to advise and assist students. Durable tasks typically depend on judgment, relationships, physical presence, or accountability.
Is this score personalized to me?
No — this page shows the occupation-level baseline. Two people with the same title often do different work. The $9 personalized report recalculates the score from the tasks you actually do and builds a concrete 7/30/90-day plan around them.

Score version jr-v1 · data release 2026.07.12-r2 · updated 2026-07-13 · baseline mapping: 20 of 20 tasks carry source-level provenance · methodology