Special Needs Teachers

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 11 task statements that define Special Needs Teachers (ISCO-08 2352) score an average of 0.28 on a 0–1 exposure scale — more exposed than about 53% 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 11 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Preparing and maintaining student data and other records and submitting reports;”

Scores 0.56 on the 2025 scale. The task of preparing and maintaining student data and other records, and submitting reports shares similarities with tasks that involve structured data handling and documentation, such as "Maintaining and storing patient health records" (score: 0.575) and "Maintaining required breeding and economic documentation" (score: 0.425). These tasks involve organizing and managing information where Generative AI can assist through data entry, report generation, and ensuring consistency. Nonetheless, for a high level of accuracy and compliance with privacy regulations, human oversight is crucial, particularly in an educational context where data might be dynamic and personalized. Given the task’s routine nature, it is amenable to partial automation. The context of a high-income country with widespread digital access amplifies the AI potential. Therefore, the adjusted score of 0.48 reflects the potential for automation, acknowledging AI's capacity to streamline the process while highlighting the necessity for human intervention to handle complexities and ensure data integrity.

Moving fastest, 2023 → 2025

“Employing special educational strategies and techniques during instruction to improve the development of sensory- and perceptual-motor skills, language, cognition, and memory;”

Model capability on this task changed by +0.14 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 2352, 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.

• Special Education Teachers, All Other
• Adapted Physical Education Specialists
• Special Education Teachers, Preschool

In context

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

Special Needs Teachers sit at the 53rd percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Special Needs Teachers rank in the 53rd 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 0.04 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Preparing and maintaining student data and other records and submitting reports;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Special Needs Teachers". https://singulariki.com/gradient/2352-special-needs-teachers.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.