Will AI replace Nursing Assistants?

Overall automation risk is very low because the role fundamentally relies on physical presence, manual dexterity, and empathetic human touch. Physical tasks such as lifting, bathing, and feeding provide durable insulation against automation, whereas reporting and dietary verification duties face moderate augmentation. Workers should familiarize themselves this quarter with voice-enabled documentation and connected medical device interfaces to streamline routine reporting.

Partially. Nursing Assistants scores 22/100 — some core duties are highly automatable, but enough durable human work remains that the occupation is transforming rather than disappearing overnight. This is a task-exposure index, not a guarantee that hiring stops.

The score is an importance-weighted average of automation probabilities across the top 15 O*NET tasks for SOC 31-1131.00. 0 tasks score at or above 80% automatable; 9 fall into the safer band (under 30% or labeled physical). Roughly 7% of scored tasks are primarily digital.

None of the top 15 tasks currently clear the ≥80% automation threshold — exposure is more diffuse across mid-range probabilities.

Few tasks in this profile clear the “safe” threshold, which is why the aggregate score skews higher and why adjacent lower-risk careers deserve serious consideration.

Official BLS data places median pay for this occupation family at $42,260. with projected employment change of +2.6% over the latest 10-year outlook window. Typical entry education: Postsecondary nondegree award. Wage and growth context ($42,260, +2.6%) should be read alongside the AI score — not as a substitute for it.

Nursing assistants face minimal displacement risk from generative AI because the vast majority of their responsibilities require physical bedside presence and tactile care. Workers should focus on building fluency with AI-driven ambient documentation and electronic health record (EHR) tools to minimize manual data entry. To increase career mobility and earnings, workers should pursue bridge programs toward Licensed Practical Nurse (LPN) or Registered Nurse (RN) credentials.

We pull Core O*NET task statements for Nursing Assistants, score each for Generative AI automation probability, weight by O*NET importance, and merge the result with BLS wages and employment projections on the SOC code. Full methodology, limitations, and prompt versioning are documented on the methodology page.

Nursing Assistants possesses robust structural insulation (65/100, verdict: "Moderate Hybrid Moat"). Large language models and automated software copilots operate exclusively within digital runtimes. Because this role demands significant physical proximity (84/100), direct interpersonal presence (71/100), and psychomotor coordination (43/100), it remains heavily defended against pure software substitution. Physical Proximity & On-Site Presence is the primary barrier (84/100), protecting human workers from algorithmic displacement.

Federal OEWS data reveals an earning spread of $18,760 from the 10th percentile ($30,020) to the 90th percentile ($48,780). The middle 50% of practitioners earn between $34,990 and $44,540. Compensation for Nursing Assistants is anchored heavily by physical presence and on-site operational demands rather than abstract symbolic manipulation. While physical roles often exhibit narrower wage compression at baseline, they possess durable wage floors because automated software runtimes cannot physically execute hands-on work.

Academic research from OpenAI and UPenn strongly aligns with our AI Career Stats assessment. Both algorithmic task evaluations (Gemini 3.8 Flash Task Model: 22/100, GPT-4 direct exposure: 5/100) and human expert panels (14/100) arrive at a shared consensus on the automation trajectory for Nursing Assistants. Software tooling expansion increases exposure by +5 points (from 5/100 to 10/100), demonstrating that integrating AI into existing software suites significantly expands automated task throughput.