Teaching Assistants, Postsecondary
Assist faculty or other instructional staff in postsecondary institutions by performing instructional support activities, such as developing teaching materials, leading discussion groups, preparing and giving examinations, and grading examinations or papers.
Also known as: Proctor · Exam Proctor · Graduate Fellow · Graduate Student · Graduate Assistant · Research Assistant (RA)
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
Copy and distribute classroom materials.
Automatable 65/100 exposure
Most durable task
Attend lectures given by the supervising instructor.
Durable 15/100 exposure
Useful next step
Make one change to the task mix
Pilot AI on one augmentable task, then document the judgment, relationships, and accountability you still own.
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.
Build my 7/30/90-day action plan — $9Most exposed tasks
Highest structured exposure values in this role’s task mix — the work AI systems can already do most of.
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Copy and distribute classroom materials.65
Augmentable tasks
Work where AI assists rather than replaces — the productivity frontier of this role.
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Evaluate and grade examinations, assignments, or papers, and record grades.55
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Develop teaching materials, such as syllabi, visual aids, answer keys, supplementary notes, or course Web sites.55
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Return assignments to students in accordance with established deadlines.50
Most durable tasks
Lowest exposure — typically judgment, relationships, physical presence, or accountability. This is the human moat.
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Demonstrate use of laboratory equipment and enforce laboratory rules.15
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Attend lectures given by the supervising instructor.15
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Provide assistance to faculty members or staff with laboratory or field research.18
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 33 puts Teaching Assistants, Postsecondary in the second quartile of analyzed occupations. In practice, exposure this level is about the mix: 1 of 20 analyzed tasks lean automatable, 7 augmentable, and 12 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
- $42,910median wage
- 164,090employed
- 24,600annual openings
- +3.1%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 — $9Related 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 65/100 exposure
- Augmentable 7 tasks, averaging 47/100 exposure
- Durable 12 tasks, averaging 23/100 exposure
score = (1×65 + 7×47 + 12×23) ÷ 20 ≈ 33
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 (Moderate) is a corpus quartile; the percentile is tie-aware across all 968 occupations.
FAQ — Teaching Assistants, Postsecondary
- Will AI replace Teaching Assistants, 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 33 mean for a Teaching Assistants, Postsecondary?
- It means that, weighted across the 20 tasks we analyzed for this role, the task mix sits at 33 on a 0–100 exposure scale — in the second 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: Copy and distribute classroom materials. 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: Demonstrate use of laboratory equipment and enforce laboratory rules; Attend lectures given by the supervising instructor; Provide assistance to faculty members or staff with laboratory or field research. 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