What is happening to tutors
- Impact
Khanmigo, ChatGPT, Gemini and a wave of subject-specific apps now do much of what a tutor does: explain a concept at the learner's level, walk through a worked problem, generate practice questions and give immediate feedback, at any hour and at little or no cost. Human tutors are using the same tools to prepare sessions, produce materials and track progress. The day-to-day is shifting from explaining content toward diagnosing why a student is stuck, keeping them motivated and accountable, coaching exam technique and supporting learners whose needs the software handles badly.
- Risk
High transformation: content explanation and practice automate; motivation, accountability and complex needs keep tutors human.
Tutoring is in the official very high exposure tier, task applicability is high, observed usage is substantial and adoption is rated very high, producing a score of 71 and a 1-4 year window. The core transaction of explaining a topic and checking understanding is being replicated directly by AI tutors, which is why this role scores higher than classroom teaching. Demand for generic homework help and test-prep content delivery will fall as families and students use free or cheap AI alternatives, and online tutoring marketplaces are already embedding AI. What stays human is the relationship: motivating a reluctant learner, holding them accountable, reading confusion a model misses, working with learning differences and giving parents a trusted human account of progress. Tutors who provide structure, trust and specialist expertise will remain in demand; those who mainly deliver explanations will not.
- Sector readiness
Very High Adoption Driven by Learners
Students adopted AI for homework help before most tutoring businesses had a strategy, and tutoring platforms and test-prep companies have responded by building AI into their products. Independent tutors vary widely, from those using AI daily for materials to those ignoring it. Adoption is rated very high overall because the learners and platforms, not the tutors, are setting the pace.
Where you stand
Position yourself as a learning coach who provides structure, motivation and accountability that a chatbot cannot.
Specialise in learners and subjects where AI struggles: learning differences, younger children, high-stakes exam coaching and advanced or niche topics.
Use AI to extend your sessions, offering practice and feedback between meetings so your service does more than an hour a week.
What this means for you
Concrete changes to how the work gets done, in the order you are likely to meet them.
- 01
Teach students to use AI well. Show them how to get useful explanations and how to spot the mistakes. You become the guide to the tool rather than its competitor.
- 02
Sell outcomes and accountability. Parents pay for a student who shows up, does the work and improves. Make progress tracking and accountability explicit in your offer.
- 03
Prepare with AI, deliver in person. Use ChatGPT or Khanmigo to generate practice sets and explanations, then spend session time on diagnosis and coaching.
- 04
Specialise where it is hard. Dyslexia, maths anxiety, early reading and top-end exam coaching all involve judgement and relationship that software does not replace.
- 05
Build a between-session layer. Assign AI-assisted practice and review the results before each session, so you arrive knowing exactly where the student struggled.
- 06
Document your impact. Keep records of grades, confidence and attendance. Evidence of results is your strongest defence against cheap AI alternatives.
What is pushing this change
- 01
Conversational AI tutors. Khanmigo, ChatGPT and Gemini explain, question and give feedback interactively at near-zero cost.
- 02
Subject-specific apps. Tools such as Photomath and Quizlet solve, explain and generate practice in specific subjects.
- 03
Platform integration. Online tutoring marketplaces and test-prep companies are embedding AI to lower cost and scale sessions.
- 04
Student-led adoption. Learners already use AI for homework, changing what they expect and will pay for from a human tutor.
- 05
Cost pressure on families and schools. Free or cheap AI help is attractive compared with hourly tutoring, especially for routine homework support.
- 06
Adaptive learning data. AI systems track performance and adapt practice, taking over the monitoring tutors once did manually.
Impact by sector
The headline figure is an average. Where you work changes the picture.
- Online homework-help and test-prep platforms
The most exposed setting; AI delivers explanations and practice at scale and platforms are cutting human hours.
- Independent private tutoring
Relationship and reputation sustain demand, but tutors offering generic content explanation face price pressure.
- Special educational needs and early years
Working with learning differences and young children relies on human attunement; AI assists but does not replace.
- School-based intervention tutoring
Programmes in schools combine human tutors with AI practice tools; structure and accountability keep humans central.
Skills to build
The skills that keep the human part of this work valuable as the routine part is automated.
- 01
Diagnostic teaching. Identifying the real cause of a misunderstanding, not just the wrong answer, is what separates a tutor from a chatbot; practise questioning and observation.
- 02
Motivation and coaching. Keeping students engaged and accountable is now the core service; learn coaching techniques and build routines.
- 03
AI tool fluency. Knowing Khanmigo, ChatGPT and subject apps well lets you prepare faster and teach students to use them responsibly.
- 04
Special needs expertise. Training in dyslexia, ADHD and other learning differences opens the least exposed and most valued segment.
- 05
Exam and curriculum knowledge. Detailed understanding of specific exam boards and mark schemes remains valuable for high-stakes coaching.
- 06
Parent and student communication. Clear, honest reporting of progress builds the trust that keeps families paying for a human.
Tools in use
Kinds of tool worth knowing
- 01
Claude for Education. Learning mode that guides students through reasoning rather than giving answers directly.
- 02
Tutoring platform AI assistants. AI features in online tutoring marketplaces that handle practice, feedback and matching between human sessions.
Named tools already in use
Khanmigo
VisitAI tutor from Khan Academy that guides students through problems and supports teachers with planning.
ChatGPT
VisitUsed by tutors to create practice sets and explanations and by students for on-demand help.
Quizlet
VisitStudy platform with AI-generated flashcards, practice tests and explanations.
Photomath
VisitApp that solves and explains maths problems step by step from a photo.
In practice
Ways people in this role are already using AI, and what they get from it.
- AI-assisted session preparationExample 1
- How
A maths tutor uses ChatGPT to generate targeted practice problems on the topics a student struggled with last week, checking the answers before the session.
GainPreparation takes minutes and the session goes straight to the student's weak points.
- Between-session practiceExample 2
- How
A tutor assigns Khanmigo practice between meetings and reviews the student's attempts before each session.
GainStudents get daily support and the tutor arrives knowing what to address.
- Teaching AI literacyExample 3
- How
An English tutor works through a chatbot's essay feedback with a student, showing where it is useful and where it is wrong.
GainThe student learns to use AI critically and the tutor's role as guide is reinforced.
How this role compares
Three neighbouring roles chosen to show the direction of travel, then the roles either side of yours on the exposure scale.
- Interpreters and TranslatorsMore exposed · exposure 83
- AI impact
Machine translation and real-time speech tools replicate core language work directly.
Work moves toAccurate cross-language communication in sensitive settings.
- Special Education TeachersDifferent skills, growing · exposure 38
- AI impact
Demand for specialists in learning differences grows while AI serves as a support tool rather than a replacement.
Work moves toIndividualised teaching for students with additional needs.
- School CounselorsComplementary, less exposed · exposure 54
- AI impact
Pastoral and wellbeing work depends on trust and human judgement that AI does not provide.
Work moves toStudent wellbeing, guidance and support.
- 711–4 yrs
- 713–8 yrs
Quantitative Analysts (Quants)
711–4 yrsTutors · this report
711–4 yrs- 722–5 yrs
- 721–4 yrs
- 731–4 yrs
Put this role next to another: vs Interpreters and Translators · vs Special Education Teachers · vs School Counselors · pick any role
Closing judgement
If you tutor, the part of your work that is pure explanation is already available free from a chatbot, and your students know it. That does not make you obsolete, but it does change what you are paid for: the structure, the motivation, the accountability and the ability to see what a learner actually needs. Use AI to prepare and to extend practice between sessions, and build your offer around the human elements. The generic homework-help tutor is the role under the most pressure.
Evidence and revisions
What the exposure figure rests on, what changed when it was last revised, and the published work cited for this role.
71
Window1-4 years (unchanged)
The 5 October 2026 review held the score.
Exposure Index v2. Inputs: task applicability 58/100 (Microsoft AI applicability score 0.29 for Tutors); observed usage 54/100 (Anthropic observed exposure 0.41); official exposure tier 100/100 (BLS: very high); labour-market trajectory not yet mapped for this occupation, so its weight was spread across the other inputs; published adoption rating 85/100 (very high adoption). Weighted base 71.2. Final score 71. New report: the window of 1-4 years is set from the score band.
| Input | Scaled | Weight | Points |
|---|---|---|---|
| Task applicabilityMicrosoft Research, AI applicability score | 58 | 39% | 22.7 |
| Observed usageAnthropic Economic Index, observed exposure | 54 | 22% | 12.1 |
| Official exposure tierUS BLS AI-exposure category | 100 | 22% | 22.2 |
| Labour-market trajectoryUS BLS projected employment change 2025–35 | not measured | — | — |
| Published adoption ratingThis report’s adoption level | 85 | 17% | 14.2 |
| Weighted base | 71.2 | ||
| Exposure score | 71 | ||
Inputs not measured for this occupation are dropped and the other weights renormalised. Scaling rules and the adjustment policy are in the method note below and the research library.
US Bureau of Labor Statistics · Employment Projections 2025–35 and AI Exposure Categories
Official statistics · 27 August 2026AI-exposure tier: Very high. Projected employment change not yet mapped for this occupation. Matched to Tutors.
Microsoft Research · Working with AI: Measuring the Applicability of Generative AI to Occupations
Working paper · 10 July 2025AI applicability score 0.29 for SOC 25-3041; scaled to 58/100 as the task-applicability input.
Anthropic · Anthropic Economic Index report: Cadences
Report · 26 June 2026Observed exposure 0.41 for SOC 25-3041; scaled to 54/100 as the observed-usage input.
UK Department for Science, Innovation and Technology · Assessment of AI capabilities and the impact on the UK labour market
Report · 28 January 2026UK context: around 70% of UK workers are in occupations with tasks AI could perform or enhance, above the US average; a one-standard-deviation rise in exposure was associated with a 3.9% fall in UK job postings.
Archived copies are served only where the licence permits; otherwise the link goes to the publisher. Full research library →
Readers' view
What people who do this work make of our reading: their own scores, their reasons, and the notes they left on each section.
Our report is one reading of the evidence. This section is the other dataset: what people who do or know this work make of it. Nobody has scored this role yet. Sign in to add yours.
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71
No one has explained their score yet. A line or two about what you see in your own work is the most useful thing on this page.
No notes yet. Every section above has a “Readers' notes” line at the bottom; open one and say what you know.
Method and sources
Each report was written from a large body of published research. The exposure score itself is computed, not written: it is the CareerGuard Exposure Index, a weighted average of occupation-level measures from the US Bureau of Labor Statistics (AI-exposure classification and 2025–35 projections), Microsoft Research (AI applicability scores) and Anthropic (observed exposure), together with the adoption rating published on the report. The organisations and publications below are the standing literature behind the narrative sections. Every source, with dates, licences and archived copies where we are permitted to hold them, is catalogued in the research library.
Exposure Index v2 (October 2026). Each input is scaled to 0–100 and weighted: task applicability 35% (Microsoft AI applicability score ÷ 0.5), observed usage 20% (Anthropic observed exposure ÷ 0.75), official exposure tier 20% (BLS very high = 100, high = 70, moderate = 40, low = 10), labour-market trajectory 10% (50 − 2.5 × projected % employment change), published adoption rating 15% (very high = 85, high = 70, medium-high = 55, medium = 40, low-medium = 25, low = 10). Inputs not measured for an occupation are dropped and the remaining weights renormalised. An editorial adjustment of at most ±12 points is allowed only for automation channels the measures cannot see (robotics, self-service, machine vision, medical imaging, RPA/OCR, generative video) and is always logged with its reason. Scores are whole numbers, not rounded to five. The change window shifts one notch (a year at each end) per ten points of movement.
Research library: every source, with dates, licences and archived copies →
- World Economic Forum
- The Future of Jobs Report series — Employer survey of expected job growth and decline, skill shifts and technology adoption (2020, 2023 and 2025 editions); Artificial Intelligence and the Future of Entry-Level Work (2026).
- AI governance and transformation reports — Frameworks on ethical AI, talent strategy and industry transformation.
- McKinsey Global Institute
- AI, Automation and the Future of Work series — Research quantifying automation potential by task, sector and demographic, from "Jobs Lost, Jobs Gained" to "Agents, robots, and us" (2025).
- Industry-specific reports — Financial services, healthcare, manufacturing and others.
- PwC
- Global AI Jobs Barometer — Annual analysis of job postings and productivity by AI exposure (2024–2026 editions).
- Upskilling Hopes and Fears survey — Employee perceptions and readiness.
- Microsoft Research and Anthropic
- Working with AI (2025); Anthropic Economic Index (2025–2026) — Occupation-level usage data from Copilot and Claude conversations, the two observed-usage measures behind the 2026 revision.
- Stanford Digital Economy Lab and Stanford HAI
- Canaries in the Coal Mine? (2025–2026); AI Index Report (annual) — Payroll evidence on early-career employment in exposed occupations; annual measurement of AI capability, investment and adoption.
- Deloitte
- Human Capital Trends series — Workforce, talent and HR technology trends.
- Tech Trends series — Emerging technologies and their business implications.
- Accenture
- Technology Vision series — Forward-looking analysis of emerging technology, with emphasis on AI.
- Fjord Trends — Design, innovation and human experience in a digital world.
- Boston Consulting Group
- AI/ML insights and industry solutions — "The AI Revolution in the Workplace" and related research.
- EY
- AI and workforce reports — Adoption, talent strategy and ethics.
- IBM Institute for Business Value
- AI and automation studies — Business models, workforce evolution and leadership.
- OECD
- AI Policy Observatory — International data and policy on AI, labour markets and skills.
- Employment Outlook — Labour-market trends including technological impact (2023–2026 editions).
- International Labour Organization
- Generative AI and Jobs: A Refined Global Index of Occupational Exposure (2025) — Task-level exposure gradients for every ISCO occupation; successor to the 2023 global index.
- International Monetary Fund
- Staff Discussion Notes on AI and work (2024, 2026) — Complementarity framing: where AI augments and where it substitutes.
- UK Department for Science, Innovation and Technology
- Assessment of AI capabilities and the impact on the UK labour market (2026) — UK occupational exposure and job-posting evidence.
- Brookings Institution
- AI and automation research — Economic and social implications, displacement and skills.
- Yale Budget Lab and Goldman Sachs Research
- Tracking the Impact of AI on the Labor Market; AI and the US labour market (2026) — Aggregate labour-market monitoring; macro displacement estimates.
- Oxford University (Oxford Martin School)
- The Future of Employment — Frey & Osborne and subsequent research on susceptibility to automation.
- MIT Technology Review
- AI & Work — Reporting on AI research and its implications for industries and jobs.
- Gartner
- Hype Cycle for Artificial Intelligence — Maturity and adoption of AI technologies.
- Future of Work reports — Workplace models and talent strategy.
- U.S. Bureau of Labor Statistics
- Employment Projections 2025–35; AI Exposure Categories; Occupational Outlook Handbook — Ten-year employment projections and, from the 2025 cycle, an AI-exposure tier for every detailed occupation.
- Indeed Hiring Lab
- AI at Work Report (2025) and posting-market updates — Skill-level transformation estimates and job-posting trends by occupation.
- OpenAI
- Research papers, blog and API documentation — Large language models, generative AI, safety and societal impact.
- Google DeepMind
- Research papers and blog — Reinforcement learning, AI for science, AGI and ethics.
- Meta AI
- Research papers and blog — Large language models, computer vision, AI for social good.
- Hugging Face
- Transformers library and model hub — Open-source state-of-the-art NLP models.
- TensorFlow and PyTorch
- Documentation and community forums — Core frameworks illustrating practical capability.
- arXiv
- cs.AI, cs.LG, cs.CV, cs.CL — Pre-print research.
- NeurIPS and ICML
- Conference proceedings — Top-tier academic research.
- ACM and IEEE
- Journals and proceedings — ACM Computing Surveys; IEEE Transactions on AI.
- Kaggle
- Datasets and competition solutions — Applied machine learning on real-world problems.
- The Alan Turing Institute
- Research and reports — Responsible and applied AI.
- NIST
- AI Risk Management Framework — Voluntary framework for managing AI risk.
- European Commission
- AI Act — Risk-tiered legal framework for AI.
- Ethics Guidelines for Trustworthy AI — Principles for responsible development.
- Partnership on AI
- Research and best practice — Responsible AI development.
- AI Now Institute
- Annual reports — Social implications: power, inequality, rights.
- ACM FAccT
- Proceedings — Fairness, accountability and transparency.
- Data & Society
- Publications — Social implications of data-centric technology.
- WIPO
- Conversation on IP and AI — Intellectual-property implications of AI.
- IEEE Global Initiative on Ethics of A/IS
- Ethically Aligned Design — Recommendations for ethical AI design.
- Center for AI and Digital Policy
- Policy briefs — Accountable AI policy.