Share and intensity of work current AI systems can materially affect.
Elementary School Teachers AI displacement risk
Lesson prep, differentiated materials, and feedback loops are augmentable. Classroom management, care, student relationships, and local accountability remain central.
Likely potential for exposed tasks to move to software after workflow integration.
AI may change prep time and instructional materials faster than staffing levels. Privacy, age-appropriate use, and local policy matter heavily.
Task profile
Where AI changes the work
Draft lesson materials
Exposure 68, automation 21%, augmentation 74%.
Differentiate worksheets
Exposure 61, automation 18%, augmentation 70%.
Grade routine work
Exposure 46, automation 26%, augmentation 55%.
Classroom facilitation
Exposure 12, automation 2%, augmentation 24%.
Transition pathways
Adjacent moves that preserve existing skills
adjacent role
Low Instructional Designer
Training horizon: 6-12 months. Skill overlap 64. Wage preservation signal 96.
- Build learning modules
- Measure outcomes
- Apply accessibility standards
role redesign
Low Learning Technology Coach
Training horizon: 4-8 months. Skill overlap 72. Wage preservation signal 95.
- Pilot classroom tools
- Train teachers
- Create privacy-safe use policies
Labor-market context
Median wage: $61,350. Employment context: Large public-service workforce. Typical education: Bachelor's degree and state license.
- Low displacement pressure
- High workload relief
- Policy and privacy constraints
Sources
Evidence trail
International Labour Organization / 2025 Generative AI and Jobs: A Refined Global Index of Occupational Exposure OECD / 2023 Employment Outlook 2023: Artificial Intelligence and the Labour Market O*NET / current public database Occupation, task, skill, and work-activity data US Bureau of Labor Statistics / current public release Occupational Employment and Wage Statistics