Primary School Teachers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 10 task statements that define Primary School Teachers (ISCO-08 2341) score an average of 0.26 on a 0–1 exposure scale — more exposed than about 48% 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 10 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Preparing daily and longer term lesson plans in accordance with curriculum guidelines;”

Scores 0.47 on the 2025 scale. Preparing daily and longer term lesson plans in accordance with curriculum guidelines requires a mix of data processing, knowledge of educational standards, and pedagogical skills. Generative AI can significantly automate the generation of content for lesson plans, including drafting schedules, suggesting activities, and aligning with curriculum standards, similar to tasks such as "Developing methodological materials and textbooks" which had a relatively high automation score of 0.58. However, the need for human expertise in tailoring lesson plans to specific classroom dynamics, engaging with students' needs, and employing creative teaching strategies adds complexity that Generative AI alone cannot handle fully. The task is less subjective than directly teaching or improvising (as in the task of "Planning appropriate exercises and games, and improvising music" with an automation score of 0.385) but still requires significant human insight, as reflected in similar tasks related to curriculum planning and education-specific roles. Considering the capabilities of AI and the necessity for personalization and adaptation in lesson planning, a balanced score of 0.38 reflects the potential for AI assistance while maintaining the indispensable human role, especially in a context like Poland where technological infrastructure is robust.

Moving fastest, 2023 → 2025

“Observing and evaluating children‚Äôs performance and behaviour;”

Model capability on this task changed by +0.10 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 2341, 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.

• Elementary School Teachers, Except Special Education
• Middle School Teachers, Except Special and Career/Technical Education

In context

Part of the 2 - Professionals major group. Return to the full gradient to see how the whole group sits.

Primary School Teachers sit at the 48th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Primary School Teachers rank in the 48th 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 0.06 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Preparing daily and longer term lesson plans in accordance with curriculum guidelines;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Primary School Teachers". https://singulariki.com/gradient/2341-primary-school-teachers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.