Nursing Associate Professionals

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 6 task statements that define Nursing Associate Professionals (ISCO-08 3221) score an average of 0.22 on a 0–1 exposure scale — more exposed than about 38% of the 427 placed occupations. Roughly 0% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Minimal band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 6 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Updating information on patients' condition and treatments received in record-keeping systems;”

Scores 0.62 on the 2025 scale. The task of updating information on patients' conditions and treatments in record-keeping systems is similar to maintaining and storing patient health records accessible by Generative AI, which can handle structured data entry, suggest corrections, and automate standardized documentation tasks. Compared to tasks like maintaining patient medical records (score: 0.375) and maintaining documentation on a convicted person (score: 0.365), this task offers more potential for automation due to its structured nature. Additionally, tasks like deleting non-archival documentation (score: 0.6) indicate a high automation potential for routine, rule-based documentation tasks when human judgment is not heavily involved. Given the structured aspect of updating records and the routine nature allowing for significant AI automation, especially in a well-digitized infrastructure of a high-income country like Poland, the adjusted score is set at 0.585 to reflect the potential for AI tools to manage these processes, while acknowledging that professional oversight is necessary for critical decision making and data accuracy.

Moving fastest, 2023 → 2025

“Providing nursing and personal care and treatment and health advice to patients according to care plans established by health professionals;”

Model capability on this task changed by +0.05 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 3221, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Licensed Practical and Licensed Vocational Nurses

In context

Part of the 3 - Technicians and associate professionals major group. Return to the full gradient to see how the whole group sits.

Nursing Associate Professionals sit at the 38th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Nursing Associate Professionals rank in the 38th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 0% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.03 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Updating information on patients' condition and treatments received in record-keeping systems;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Nursing Associate Professionals". https://singulariki.com/gradient/3221-nursing-associate-professionals.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.