Occupation score change
IMPACT 10tutor exposure increased 1.0 points
AI Exposure Score moved from a previous calculation to 42/100 under methodology v1.0. 1 evidence item(s) contributed.
42/100
increased by +1.0 vs last
Low exposure
3 Oct 2026
19 hours ago · sources: BLS OES (approx) · O*NET
1 sources
16 core tasks shown · 3 secondary hidden
Tutors Moderate exposure: only 6 of 16 core tasks show high task automation potential : exposure 42/100 (low exposure).
No model has published evidence mapped to this occupation yet - the score comes from generic task assessment.
Score breakdown
Why this score?
Every component is stored with the score row and can be recomputed from the evidence table.
AI Exposure (composite)42/100
Methodology 2026-09-a · score v1.0 · input hash e77dea55d671d743
AI capability exposure73/100
Strength of current models on the capabilities this job's tasks require.
Task automation potential55/100
Task-level exposure, weighted by task importance. A component of the headline, not the headline itself.
Observed AI adoption0/100
Evidence that AI is actually being used for this work.
Labour-market signal0/100
Observed changes in postings, employment or earnings.
Evidence strength35/100
Quantity and source quality of the evidence behind the score.
1 evidence item(s) contribute. Calculated 19 hours ago. 3 Oct 2026 UTC.
Overview
Instruct individual students or small groups of students in academic subjects to support formal class instruction or to prepare students for standardized or admissions tests.
Employment
≈120,000
approx. BLS OES
Median wage
≈$38,000
approx. BLS OES
Tasks tracked
19
linked to this occupation
Employment and wage values are approximate reference figures from the BLS Occupational Employment and Wage Statistics programme, stored with an approximation flag. Verify against the O*NET record and BLS before quoting them.
Tasks
Task-level automation exposure
Core tasks only. Exposure describes how much of the task output current AI can produce or substantially assist - not whether the task disappears. Secondary shared-baseline edges are hidden until per-occupation curation is complete.
core task · importance 4.9/5
core task · importance 4.7/5
core task · importance 4.7/5
core task · importance 4.5/5
core task · importance 4.5/5
core task · importance 4.2/5
core task · importance 4.2/5
core task · importance 4.1/5
core task · importance 4/5
core task · importance 3.9/5
core task · importance 3.9/5
core task · importance 3.8/5
core task · importance 3.8/5
core task · importance 3.7/5
core task · importance 3.6/5
core task · importance 3.4/5
Evidence
Evidence trail
1 attributed item(s). Confidence reflects study design: randomised and production deployments score higher than platform observations, which score higher than projections.
- task automationincreasesconfidence 68/100 · magnitude 4818 Sept 2025
Task-level usage data shows AI most used for lesson and material preparation and for feedback drafting, with far less usage recorded for live instruction and classroom management.
A good example of capability and adoption diverging inside one occupation: preparation is heavily automated while the defining activity of the job is not.
Source: Anthropic Economic Index — education analysis · Updated 18 Sept 2025
Historical trend
Exposure over time
Backcast series: recomputed at quarterly checkpoints using only the models and evidence published by each date.
Reconstruction, not measurement. 5 observed daily snapshot(s) exist for this occupation; the earlier part of the curve is derived by applying the current methodology to the model releases and published evidence available at each past date.
Recent events
Signals affecting this occupation
- Research publishedIMPACT 40
Examining Variation in How Guided AI Tutors Resolve Student Impasses
arXiv:2609.38346v1 Announce Type: new Abstract: When a student is stuck, a tutor faces the assistance dilemma: help given too early can hinder productive struggle, while help withheld too long leaves the student in a frustrating, persistent impasse (i.e., wheel spinning). Generative AI tutors increasingly use guardrails restricting answer-giving, yet little is known about how such tutors behave once an impasse persists. We analyze 20,462 student turns from 1,260 authentic sessions with a guided LLM chemistry tutor, identifying 6,630 impasse turns of three major types: conceptual errors, expres
- AI adoption signalIMPACT 48
Measured AI usage in education is concentrated in preparation and feedback
Task-level usage data shows AI most used for lesson and material preparation and for feedback drafting, with far less usage recorded for live instruction and classroom management.
Related
Related Occupations
Occupations sharing major SOC group (25) with Tutors.
faq
Frequently Asked Questions
Common questions regarding Tutors and AI exposure.
Will AI replace Tutors?
Current AI models show an exposure score of 42/100 for Tutors. This measures technical capability to assist or automate specific tasks rather than predicted immediate job loss. Moderate exposure reflects assistance in routine information-handling while core responsibilities require human judgment.
Which tasks in Tutors have the highest AI exposure?
The most exposed tasks for Tutors include "Schedule tutoring appointments with students or their parents." (85% exposure) and "Maintain records of students' assessment results, progress, feedback, or school performance, ensuring confidentiality o…" (85% exposure).
What aspects of Tutors can AI not do yet?
Tasks with the lowest AI exposure for Tutors include "Participate in training and development sessions to improve tutoring practices or learn new tutoring techniques." (5% exposure) and "Organize tutoring environment to promote productivity and learning." (8% exposure). These responsibilities typically require physical dexterity, licensed human accountability, or complex interpersonal navigation.
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Notice: All ratings shown are AI-estimated technical exposure scores measuring technical capabilities across O*NET task statements, not predicted job loss. Contains information from O*NET 31.0 by the U.S. Department of Labor, ETA, used under CC BY 4.0.