Share and intensity of work current AI systems can materially affect.
SOC 25-3041
Tutors AI displacement risk
AI tutoring apps now answer questions, generate practice problems, and explain concepts on demand, competing directly with routine homework help. Motivation, accountability, diagnosis of misconceptions, and parent trust keep human tutors relevant.
Likely potential for exposed tasks to move to software after workflow integration.
Price-sensitive drill-and-practice tutoring is most exposed. Tutors who specialize in learning differences, test strategy, or reluctant learners hold stronger positions.
Distribution
Where Tutors sits across 620 tracked roles
Displacement pressure 42 — higher than 70% 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.
19 O*NET task statements matched to SOC 25-3041. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $43,350 (May 2025, US national). The latest BLS row matched SOC 25-3041.
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 Tutors
SOC 25-3041 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 42/100 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 Tutors
The current evidence import matched 19 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 tasks19
SOC25-3041
- Core task / ID 17027
Provide feedback to students, using positive reinforcement techniques to encourage, motivate, or build confidence in students.
- Core task / ID 17026
Review class material with students by discussing text, working solutions to problems, or reviewing worksheets or other assignments.
- Core task / ID 17034
Assess students' progress throughout tutoring sessions.
- Core task / ID 17036
Teach students study skills, note-taking skills, and test-taking strategies.
- Core task / ID 17037
Provide private instruction to individual or small groups of students to improve academic performance, improve occupational skills, or prepare for academic or occupational tests.
- Core task / ID 17023
Participate in training and development sessions to improve tutoring practices or learn new tutoring techniques.
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
Prepare practice materials
Exposure 70, automation 30%, augmentation 66%.
Assess student progress
Exposure 52, automation 24%, augmentation 60%.
O*NET evidence: Assess students' progress throughout tutoring sessions. (ID 17034)
Review problems with students
Exposure 44, automation 18%, augmentation 56%.
O*NET evidence: Review class material with students by discussing text, working solutions to problems, ... (ID 17026)
Motivate and coach students
Exposure 18, automation 4%, augmentation 32%.
O*NET evidence: Provide feedback to students, using positive reinforcement techniques to encourage, mot... (ID 17027)
TaskExposureAutomationAugmentation
Prepare practice materials7030%66%
Assess student progress5224%60%
Review problems with students4418%56%
Motivate and coach students184%32%
Transition pathways
Adjacent moves that preserve existing skills
adjacent role
ModerateInstructional Designer
Training horizon: 6-12 months. Skill overlap 62. Wage preservation signal 148.
- Build a learning-module portfolio
- Study learning science basics
- Measure learner outcomes
role redesign
ModerateLearning Coach
Training horizon: 2-5 months. Skill overlap 78. Wage preservation signal 110.
- Design accountability plans
- Integrate AI practice tools
- Coach study habits and strategy
Comparison guides
Compare the next move before you commit
6-12 months
Tutors to Instructional Designer
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Tutors into Instructional Designer.
2-5 months
Tutors to Learning Coach
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Tutors into Learning Coach.
What the AI risk score means for Tutors
The displacement pressure score for Tutors is 42. 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 practice materials carries 30% automation pressure, while Prepare practice materials carries 66% 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: $43,350 (May 2025, US national). Employment context: Large supplemental education workforce with gig growth. Typical education: Bachelor's degree or enrollment common.
Wage vulnerability is 64, while transition feasibility is 72. 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.
- AI tutoring apps compete directly
- Human accountability retains demand
- Specialization raises rates
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Tutors, 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.
Learning diagnosis
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.
AI tool integration
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.
Progress communication
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 Instructional Designer, such as build a learning-module portfolio.
- By 90 days, compare internal openings and external postings for Instructional Designer or Learning Coach and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Tutors
Will AI replace Tutors?
AI tutoring apps now answer questions, generate practice problems, and explain concepts on demand, competing directly with routine homework help. Motivation, accountability, diagnosis of misconceptions, and parent trust keep human tutors relevant. 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 Tutors work are most exposed to AI?
Prepare practice materials and Assess student progress show the strongest automation pressure in this model. Prepare practice materials and Assess student progress are better treated as AI-augmented work.
What should Tutors learn next?
Start with Learning diagnosis, Student motivation, AI tool integration. The most practical adjacent paths in this model are Instructional Designer and Learning Coach.
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