Other Language Teachers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 8 task statements that define Other Language Teachers (ISCO-08 2353) score an average of 0.34 on a 0–1 exposure scale — more exposed than about 62% 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 8 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Assigning and correcting work, and preparing and grading exams;”

Scores 0.52 on the 2025 scale. The task of assigning and correcting work, and preparing and grading exams involves significant cognitive and decision-making skills, requiring adaptability to different students' needs, understanding educational contexts, and giving nuanced feedback, which limits the automation potential by Generative AI. Compared to semantically similar tasks, such as "Supervising the proper organization and progression of tests and examinations" (0.27) and "Conducting educational classes" (0.215), which require human judgment, this task leans slightly higher due to its potential for automation in repetitive tasks like grading that AI can assist with. Yet, like "Preparing printed materials for students" (0.46) and "Applying corrections, text editing" (0.65), there are aspects AI can streamline, particularly in grading standardized components and generating exam structures, but nuanced assessment and feedback remain human-centric. Thus, aligning it closer to tasks that involve cognitive but not purely creative processes, the score is increased to 0.48 to reflect AI's supportive role while acknowledging the need for significant human involvement in the instructional context of a high-income country like Poland.

Moving fastest, 2023 → 2025

“Assessing students' progress;”

Model capability on this task changed by −0.08 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 2353, 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.

• Self-Enrichment Teachers
• Teachers and Instructors, All Other
• Adult Basic Education, Adult Secondary Education, and English as a Second Language Instructors

In context

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

Other Language Teachers sit at the 62nd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Other Language Teachers rank in the 62nd 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 0.20 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Assigning and correcting work, and preparing and grading exams;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Other Language Teachers". https://singulariki.com/gradient/2353-other-language-teachers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.