Child Care Workers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Child Care Workers (ISCO-08 5311) score an average of 0.19 on a 0–1 exposure scale — more exposed than about 31% 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 Not exposed 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Keeping records on individual children, including daily observations and information about activities, meals served, and medications administered.”

Scores 0.40 on the 2025 scale. The task of keeping records on individual children, including daily observations and information about activities, meals served, and medications administered, primarily involves structured data entry and document management. Generative AI can significantly aid in automation by organizing records, synthesizing daily observations into reports, and even providing preliminary analysis or suggestions. This shares similarities with maintaining sanitary-epidemiological documentation (score 0.3555) and maintaining patient records (score 0.4575), where the routine nature of documentation is amenable to AI assistance, although nuanced judgment and individual interpretation still require human oversight. However, unlike patient documentation which involves critical medical decisions, documentation for children is less sensitive, aligning more closely with tasks like maintaining documentation of services (score 0.255) and care services cataloguing (score 0.285), where AI can manage repetitive tasks efficiently. While human intervention remains necessary to ensure personalized care aspects are accurately documented and understood, the potential for AI to streamline the daily documentation process in a high-income country like Poland is substantial, justifying an adjusted score of 0.35.

Moving fastest, 2023 → 2025

“Taking children to and from school or outdoors for recreation;”

Model capability on this task changed by +0.06 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 5311, 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.

• Childcare Workers
• Nannies

In context

Part of the 5 - Service and sales workers major group. Return to the full gradient to see how the whole group sits.

Child Care Workers sit at the 31st percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Child Care Workers rank in the 31st 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 0.02 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Keeping records on individual children, including daily observations and information about activities, meals served, and medications administered.". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Child Care Workers". https://singulariki.com/gradient/5311-child-care-workers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.