Special Education Teachers, Secondary School: daily tasks & AI impact
This page breaks Special Education Teachers, Secondary School into the top 15 daily O*NET tasks and scores each for Generative AI automation probability. Those task scores roll up to an overall vulnerability index of 27/100 (Moderate automation risk). Struck-through rows are ≥80% automatable; highlighted safer rows are under 30% or primarily physical.
High-automation exposure
No task in this set currently exceeds the 80% threshold, so risk is spread across moderate probabilities rather than a few catastrophic duties.
Durable human work
This profile has little “safe” task mass under our thresholds. Workers should treat AI fluency as mandatory and actively map transferable skills into lower-risk roles.
Importance comes from O*NET incumbent ratings. Automation probability is model-generated for current Generative AI capability (not speculative AGI). Digital / physical / mixed labels describe the modality of the work, which strongly correlates with near-term automation potential.
| Task | Importance | AI probability | Nature |
|---|---|---|---|
| Establish and enforce rules for behavior and policies and procedures to maintain order among students. Core | 4.62 | — | — |
| Maintain accurate and complete student records, and prepare reports on children and activities, as required by laws, district policies, and administrative regulations. Core | 4.55 | — | — |
| Confer with parents, administrators, testing specialists, social workers, or other professionals to develop individual educational plans (IEPs) for students' educational, physical, and social development. Core | 4.52 | — | — |
| Employ special educational strategies and techniques during instruction to improve the development of sensory- and perceptual-motor skills, language, cognition, and memory. Core | 4.46 | — | — |
| Establish clear objectives for all lessons, units, and projects, and communicate those objectives to students. Core | 4.44 | — | — |
| Prepare materials and classrooms for class activities. Core | 4.38 | — | — |
| Teach socially acceptable behavior, employing techniques such as behavior modification and positive reinforcement. Core | 4.35 | — | — |
| Confer with parents or guardians, other teachers, counselors, and administrators to resolve students' behavioral and academic problems. Core | 4.31 | — | — |
| Develop and implement strategies to meet the needs of students with a variety of handicapping conditions. Core | 4.29 | — | — |
| Teach personal development skills, such as goal setting, independence, and self-advocacy. Core | 4.26 | — | — |
| Prepare students for later grades by encouraging them to explore learning opportunities and to persevere with challenging tasks. Core | 4.25 | — | — |
| Modify the general education curriculum for students with disabilities, based upon a variety of instructional techniques and technologies. Core | 4.18 | — | — |
| Observe and evaluate students' performance, behavior, social development, and physical health. Core | 4.18 | — | — |
| Confer with other staff members to plan and schedule lessons promoting learning, following approved curricula. Core | 4.16 | — | — |
| Meet with other professionals to discuss individual students' needs and progress. Core | 4.15 | — | — |
What remains human
No tasks currently fall under the safe threshold for this occupation.
FAQ
Which Special Education Teachers, Secondary School tasks are most exposed to AI?
No task in this set currently exceeds the 80% threshold, so risk is spread across moderate probabilities rather than a few catastrophic duties.
Which Special Education Teachers, Secondary School tasks are safest from Generative AI?
This profile has little “safe” task mass under our thresholds. Workers should treat AI fluency as mandatory and actively map transferable skills into lower-risk roles.
How should I read the Special Education Teachers, Secondary School task table?
Importance comes from O*NET incumbent ratings. Automation probability is model-generated for current Generative AI capability (not speculative AGI). Digital / physical / mixed labels describe the modality of the work, which strongly correlates with near-term automation potential.