Share and intensity of work current AI systems can materially affect.
SOC 25-3021
Self-Enrichment Teachers AI displacement risk
Online platforms and AI-generated courses compete directly with generic enrichment content. Live classes — pottery, dance, cooking, language circles — sell presence, feedback, and community, which pre-recorded or generated content does not deliver.
Likely potential for exposed tasks to move to software after workflow integration.
The squeeze is real for lecture-style instruction that video does as well. Instructors whose value is demonstration, correction, and group energy — and who build a local following — keep enrollment while content-only teaching commoditizes.
Distribution
Where Self-Enrichment Teachers sits across 620 tracked roles
Displacement pressure 32 — higher than 52% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
30 O*NET task statements matched to SOC 25-3021. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $46,800 (May 2025, US national). The latest BLS row matched SOC 25-3021.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Self-Enrichment Teachers
SOC 25-3021 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 32/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Self-Enrichment Teachers
The current evidence import matched 30 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks30
SOC25-3021
- Core task / ID 8668
Instruct students individually and in groups, using various teaching methods, such as lectures, discussions, and demonstrations.
- Core task / ID 8664
Adapt teaching methods and instructional materials to meet students' varying needs and interests.
- Core task / ID 8671
Prepare students for further development by encouraging them to explore learning opportunities and to persevere with challenging tasks.
- Core task / ID 8667
Observe students to determine qualifications, limitations, abilities, interests, and other individual characteristics.
- Core task / ID 8676
Maintain accurate and complete student records as required by administrative policy.
- Core task / ID 8666
Monitor students' performance to make suggestions for improvement and to ensure that they satisfy course standards, training requirements, and objectives.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Instruct students in enrichment subjects
Exposure 32, automation 12%, augmentation 52%.
Prepare lesson plans and materials
Exposure 66, automation 32%, augmentation 72%.
O*NET evidence: Prepare instructional program objectives, outlines, and lesson plans. (ID 8675)
Assess and track student progress
Exposure 44, automation 20%, augmentation 58%.
O*NET evidence: Meet with other instructors to discuss individual students and their progress. (ID 8680)
Recruit and schedule class participants
Exposure 48, automation 24%, augmentation 56%.
O*NET evidence: Schedule class times to ensure maximum attendance. (ID 8684)
TaskExposureAutomationAugmentation
Instruct students in enrichment subjects3212%52%
Prepare lesson plans and materials6632%72%
Assess and track student progress4420%58%
Recruit and schedule class participants4824%56%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
ModerateCommunity Education Program Coordinator
Training horizon: 2-5 months. Skill overlap 68. Wage preservation signal 120.
- Manage course catalogs
- Measure enrollment outcomes
- Recruit instructor talent
industry switch
ModerateCorporate Workshop Facilitator
Training horizon: 3-6 months. Skill overlap 62. Wage preservation signal 128.
- Package topics for business audiences
- Build a facilitation portfolio
- Track participant feedback
Comparison guides
Compare the next move before you commit
2-5 months
Self-Enrichment Teachers to Community Education Program Coordinator
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Self-Enrichment Teachers into Community Education Program Coordinator.
3-6 months
Self-Enrichment Teachers to Corporate Workshop Facilitator
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Self-Enrichment Teachers into Corporate Workshop Facilitator.
What the AI risk score means for Self-Enrichment Teachers
The displacement pressure score for Self-Enrichment Teachers is 32. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Prepare lesson plans and materials carries 32% automation pressure, while Prepare lesson plans and materials carries 72% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $46,800 (May 2025, US national). Employment context: Community and recreational instruction role with online-course competition. Typical education: Varies by subject; portfolio and expertise matter most.
Wage vulnerability is 58, while transition feasibility is 68. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- Online courses compete on content
- Live community instruction persists
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Self-Enrichment Teachers, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Instruction
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Demonstration technique
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Student motivation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Scheduling
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Community Education Program Coordinator, such as manage course catalogs.
- By 90 days, compare internal openings and external postings for Community Education Program Coordinator or Corporate Workshop Facilitator and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Self-Enrichment Teachers
Will AI replace Self-Enrichment Teachers?
Online platforms and AI-generated courses compete directly with generic enrichment content. Live classes — pottery, dance, cooking, language circles — sell presence, feedback, and community, which pre-recorded or generated content does not deliver. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Self-Enrichment Teachers work are most exposed to AI?
Prepare lesson plans and materials and Recruit and schedule class participants show the strongest automation pressure in this model. Prepare lesson plans and materials and Assess and track student progress are better treated as AI-augmented work.
What should Self-Enrichment Teachers learn next?
Start with Instruction, Demonstration technique, Student motivation. The most practical adjacent paths in this model are Community Education Program Coordinator and Corporate Workshop Facilitator.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureOECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics