Job Displacement | By 2030
Education Sector Displacement by 2030
Weighted average across 5 sources. Observed so far: ~14.7% (3 measurements from Yale Budget Lab, Brookings, Dallas Fed, BLS). Projections range 8.3–20% (median ~14.2%).
11.2% of education support roles could be displaced by AI. This includes tutoring, grading, content creation, and administration, but notably not core teaching, which remains among the least automatable occupations. The collapse of companies like Chegg (subscribers down 50% due to ChatGPT) shows how quickly AI can disrupt adjacent education services even while classroom teaching persists. AI adoption in education is high: 92% of students use AI tools (up from 66% in 2024), 60% of teachers report using AI, and 93% of higher education staff expect to expand AI use (Ellucian). However, research consistently frames AI as augmenting educators rather than replacing them. The 33,000 higher education jobs eliminated in 2025 (BLS) were primarily driven by federal funding policy, not AI. Direct AI-specific displacement estimates remain unusually sparse for this sector. SHRM found education has the lowest automation rate of any sector at just 7.3%.
Blended estimate across 5 sources ranging 3–23%. Higher-tier evidence and more recent data are weighted more heavily. See the full methodology for details on weighting, source validity, and recency bias.
Observed Data & Projections
This prediction has two fundamentally different types of evidence: observed employment data (what has actually happened) and forward-looking projections (what researchers estimate will happen). They are shown separately below because they answer different questions.
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Note: Insufficient evidence for confident projection. Only 4 data points, two of which are corporate proxies (Chegg, Pearson) rather than sector-wide measurements. BLS projects just 3% displacement in education support roles, and SHRM found education has the lowest automation rate of any sector at 7.3%. Treat this estimate with more caution than others on this site.
What has happened
Measured employment data from government statistics, large-scale surveys, and administrative records. This is ground truth: what has actually occurred in the labor market.
Each dot is a different measurement source. Click any dot to jump to its source below.
What researchers project
Forward-looking estimates from structural models, institutional surveys, and expert forecasts. All projections target by 2030, shown by the reference line. The wide range (8.3–20%) reflects different model assumptions about reinstatement effects, demand elasticity, and adoption speed, not just parameter uncertainty.
Each dot is a different projection source. The x-axis shows when the projection was published. Click any dot to jump to its source. Overlay bars show directional signals from related studies.
Task Visualizer
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Full Economy Picture
AI and the US Economy
Automation impact by occupation and income tier.
Sources (23)
Educational Services 8.3%
Ellucian: 66% of higher ed institutions leveraging AI, up from 49% YoY (n=779)
The survey shows institutional adoption is accelerating, with 66% of respondents reporting their institution is currently leveraging AI, an increase from 49% year over year. Eighty-eight percent of respondents say they expect institutional AI use to increase over the next two years. Survey of 779 higher education faculty and administrators from 300+ institutions.
MIT FutureTech: 60.4% AI task success in education roles (N=1,615)
Educational Instruction and Library job family: 60.4% AI task success rate across 1,615 evaluations. Slope coefficient beta=-0.28 (marginally significant), suggesting moderate task-duration dependence in education.
NBER (Cruces et al.): AI closes ~75% of education-based productivity gap in RCT (n=1,174)
In a randomized experiment with 1,174 adults ages 25-45, AI access closed approximately three-quarters of the education-based productivity gap: higher-education participants outperform lower-education participants by 0.548 standard deviations without AI; with AI, this gap falls to 0.139 standard deviations.
Anthropic/Shen: AI-assisted devs score 17pp lower on comprehension (50% vs 67%, n=52)
AI use impairs conceptual understanding, code reading, and debugging abilities. The AI group averaged 50% on the quiz, compared to 67% in the hand-coding group. The AI didn't make developers faster on average.
IMF: 60% of advanced-economy jobs exposed to AI
Around 40% of global employment is exposed to AI; in advanced economies 60%. Demand for new skills is rising with 10% of jobs in advanced economies requiring at least one new skill.
Cognizant: education exposure jumped from 11% to 49% — a 4.5x increase
Education task exposure jumped from 11% to 49% — a 4.5x increase driven by multimodal AI, advanced reasoning, and agentic systems. Based on reassessment of 18,000 tasks across 1,000 O*NET occupations.
EDUCAUSE: 94% of higher ed workers use AI; 67% flag job loss as urgent risk (n=1,960)
94% of higher education workers use AI tools for work, but only 54% are aware of institutional AI policies. 67% identified six or more urgent AI-related risks, including job loss. Survey of 1,960 staff and faculty across 1,800+ institutions.
WEF: moderate risk, core teaching protected
Education support roles face moderate displacement risk while core teaching remains protected. Administrative and content creation roles most affected by AI adoption.
SHRM: only 7.3% of education jobs highly automated — lowest of any sector (n=20,262)
At least 50% of tasks are automated in only 7.3% of education and library occupations — the lowest of any occupational group. Survey of 20,262 U.S. workers fielded March-April 2025.
Teacher aide and educational support employment projected to decline 3%. Post-secondary teacher demand flat. Private tutoring category declining.
PwC: AI-exposed professions saw 55% faster skill churn in job postings (US)
In job postings in the US, those professions most exposed to AI have seen a 55% greater change in skills requested by employers.
Chronicle: AI will decimate faculty ranks; TAs first, then non-tenured, then tenured
Over the next decade AI will decimate faculty ranks; TAs will be first casualties, then non-tenured faculty, then tenured positions via attrition. Predicts significant culling of doctoral programs.
Pew: Only 31% of AI experts predict fewer teacher jobs in 20 yrs (n=1,013)
31% of AI experts predict AI will lead to fewer teacher jobs in 20 years, vs 43% of general public. Survey of 1,013 AI experts (Aug-Oct 2024) and 5,410 US adults. AI experts far more optimistic: 73% say positive impact on jobs vs 23% of public.
Pearson pivoted to AI-first content delivery, reducing editorial headcount by 18% while growing AI-generated adaptive learning content 300%.
Chegg: 636 employees eliminated (67% of workforce), citing AI
Chegg eliminated 636 employees across two rounds of layoffs (23% in 2024, then additional cuts in early 2025), totaling ~67% of peak workforce. CEO explicitly cited AI as existential threat to homework-help business model.
OECD: 30-40% task displacement for support staff
Teaching roles are among the least automatable (15% task exposure), but education support staff, administration, and tutoring face 30-40% task displacement. Blended estimate across the full education workforce: ~20%.
Duolingo: cut 10%+ of contractors, replaced by AI content generation
Duolingo cut 10%+ of its contractor workforce, explicitly replacing human content creators with AI-generated language exercises. CEO announced 'AI-first' strategy for all content production.
Harvard: AI tutor doubled learning gains vs. active classroom
Learning gains for students in the AI-tutored group were about double those for the in-class group. Students using the AI tutor reported significantly more engagement and motivation to learn.
Chegg subscribers fell 50% from peak as students shifted to ChatGPT. Company laid off 23% of workforce, citing AI as primary competitive threat.
RAND: 18% of K-12 teachers used AI; 60% of districts planned AI training
Surveyed 1,020 teachers nationally; 18% of K-12 teachers used AI for teaching with most using adaptive learning systems weekly. 60% of districts planned to train teachers on AI.
UNESCO: AI tutoring handles 60% of routine queries; TAs face most displacement risk
AI tutoring systems can handle 60% of routine student queries. Teaching assistants and tutors face the most immediate displacement risk.
McKinsey: 12M+ occupational shifts by 2030; up to 30% of hours could be automated
An additional 12 million occupational shifts expected by 2030. Workers in lower-wage jobs are up to 14 times more likely to need to change occupations. Activities accounting for up to 30% of hours could be automated.
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