SOC 31-1131

Nursing Assistants AI displacement risk

Feeding, bathing, repositioning, and comforting patients is physical, relational care delivered at the bedside. Monitoring technology watches for falls and vitals changes, but lifting, turning, and comforting human beings remains entirely manual.

Exposure18

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

Automation6%

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

Risk bandLow

Chronic understaffing in nursing homes and hospitals defines this occupation far more than any automation trend. Technology augments surveillance; it does not perform care.

Distribution

Where Nursing Assistants sits across 620 tracked roles

Nursing Assistants · 12050100

Displacement pressure 12 — higher than 2% 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.

30 O*NET task statements matched to SOC 31-1131. The displayed task profile combines these official task statements with the current public score model.

Median wage context: $42,260 (May 2025, US national). The latest BLS row matched SOC 31-1131.

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 Nursing Assistants

SOC 31-1131 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 12/100 role score and are not an occupation forecast.

Modest change

+1.1% group wage

Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.

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

Substantial change

+5.9% group wage

Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.

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

Extreme change

+33.6% group wage

Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.

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 Nursing Assistants

The current evidence import matched 30 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 tasks30
SOC31-1131
  • Core task / ID 19341

    Turn or reposition bedridden patients.

  • Core task / ID 19317

    Answer patient call signals, signal lights, bells, or intercom systems to determine patients' needs.

  • Core task / ID 19325

    Feed patients or assist patients to eat or drink.

  • Core task / ID 19327

    Measure and record food and liquid intake or urinary and fecal output, reporting changes to medical or nursing staff.

  • Core task / ID 19331

    Provide physical support to assist patients to perform daily living activities, such as getting out of bed, bathing, dressing, using the toilet, standing, walking, or exercising.

  • Core task / ID 19324

    Document or otherwise report observations of patient behavior, complaints, or physical symptoms to nurses.

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

physical

Assist patients with daily activities

Exposure 12, automation 3%, augmentation 18%.

O*NET evidence: Provide physical support to assist patients to perform daily living activities, such as... (ID 19331)

physical

Turn and reposition patients

Exposure 10, automation 2%, augmentation 14%.

O*NET evidence: Turn or reposition bedridden patients. (ID 19341)

information

Measure and record intake and output

Exposure 40, automation 20%, augmentation 48%.

O*NET evidence: Measure and record food and liquid intake or urinary and fecal output, reporting change... (ID 19327)

social

Report patient changes to nurses

Exposure 30, automation 12%, augmentation 46%.

O*NET evidence: Document or otherwise report observations of patient behavior, complaints, or physical ... (ID 19324)

TaskExposureAutomationAugmentation
Assist patients with daily activities123%18%
Turn and reposition patients102%14%
Measure and record intake and output4020%48%
Report patient changes to nurses3012%46%

Transition pathways

Adjacent moves that preserve existing skills

credentialed transition

Licensed Practical Nurse

Training horizon: 12-18 months. Skill overlap 72. Wage preservation signal 158.

  • Enter an LPN program
  • Complete clinical rotations
  • Pass state licensure
Low
role redesign

Restorative Care Aide

Training horizon: 3-6 months. Skill overlap 66. Wage preservation signal 110.

  • Learn mobility assistance programs
  • Document functional progress
  • Partner with therapy teams
Low

Comparison guides

Compare the next move before you commit

What the AI risk score means for Nursing Assistants

The displacement pressure score for Nursing Assistants is 12. 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. Measure and record intake and output carries 20% automation pressure, while Measure and record intake and output carries 48% 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: $42,260 (May 2025, US national). Employment context: One of the largest hands-on care occupations. Typical education: State-approved training program and certification.

Wage vulnerability is 72, while transition feasibility is 64. 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.

  • Very low displacement pressure
  • Severe staffing shortages
  • Monitoring tech augments surveillance

Upskilling priorities

Skills that make this role more resilient

The safest upskilling plan starts with skills already close to the work. For Nursing Assistants, 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

Safe patient handling

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

Observation and reporting

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

Compassionate care

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

Infection control

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 Licensed Practical Nurse, such as enter an lpn program.
  3. By 90 days, compare internal openings and external postings for Licensed Practical Nurse or Restorative Care Aide and update your resume around measurable workflow outcomes.

FAQ

Questions about AI and Nursing Assistants

Will AI replace Nursing Assistants?

Feeding, bathing, repositioning, and comforting patients is physical, relational care delivered at the bedside. Monitoring technology watches for falls and vitals changes, but lifting, turning, and comforting human beings remains entirely manual. 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 Nursing Assistants work are most exposed to AI?

Measure and record intake and output and Report patient changes to nurses show the strongest automation pressure in this model. Measure and record intake and output and Report patient changes to nurses are better treated as AI-augmented work.

What should Nursing Assistants learn next?

Start with Safe patient handling, Observation and reporting, Compassionate care. The most practical adjacent paths in this model are Licensed Practical Nurse and Restorative Care Aide.

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