Explore the evidence / Work & learning
What changes for people?
Worker experiences and access, student use and provision, and the support teachers and pupils receive.
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- OECD technology co-useOrganisations · 28 countries and published mean
- US business and employee-use reportsOrganisations · two separate business measures
- Reuters Institute: weekly use and search exposureAccess & geography · separate survey measures
- UK business AI use and skills barriersOrganisations · separate UK survey
- One world. Two different measures.World geography
- How many people use generative AI?World geography
- Where is Claude used disproportionately?World geography
- Read one place across the evidence.World geography
- United Arab EmiratesWorld geography
- Use rises. The gaps remain.World geography
- Adoption rises; the North–South gap widens.World geography
- All 147 economies move upward.World geography
- The ten largest gainsWorld geography
- All four periods, side by side.World geography
- Population reachWorld geography
- National averages conceal local variation.United States
- Every county counts once.United States
- From states to individual counties.United States
- Adoption differs across European economies.Organisations
- Where European firms have adopted AIOrganisations
- Read sector growth with its coverage.Organisations
- Sector growth needs a coverage lensOrganisations
- Size, scaling and returns answer different questions.Organisations
- Larger firms pull ahead with agentsOrganisations
- Personal gains and organisational returnsOrganisations
- Deployment expanded across functions.Organisations
- Use differs by education and role.Work & learning
- Use across the workforceWork & learning
- Access differs by education and roleWork & learning
- Faster work is one outcome among several.Work & learning
- What AI users reportWork & learning
- Widespread use meets uneven provision.Work & learning
- Students use more than institutions provide.Work & learning
- A gap between teacher use and training.Work & learning
- Adoption does not establish critical use.Work & learning
- Critical use is uneven within adoption.Work & learning
- What adoption leaves unresolved.Work & learning
- Conversations extend beyond paid work.AI tasks
- Purpose of Claude conversationsAI tasks
- Answers, documents and guidance are common outputs.AI tasks
- Three common outputsAI tasks
- The same output can serve different purposes.AI tasks
- The same output, different purposesAI tasks
- Behind the headline numbers.Evidence library
- Microsoft research in brief.Evidence library
- Five reports. Every page.Evidence library
- Global · H1 2025Evidence library
- Global · H2 2025Evidence library
- Global · Q1 2026Evidence library
- Global · Q2 2026Evidence library
- United States · Q1 2026Evidence library
- Data, reports and source files.Evidence library
- Microsoft geographic dataEvidence library
- Survey, sector and geographic evidenceEvidence library
- Five original reportsEvidence library
- Transcriptions and provenanceEvidence library
- Methods, caveats and attribution.Evidence library
- Know what each number can carry.Evidence library
- The Microsoft collection: definitions and provenance.Evidence library
- All datasets and downloads · CSV, JSON, PDF and textEvidence library
- Read all 94 original report pagesEvidence library
- Search 65 transcribed tables and recordsEvidence library
- Complete methods, caveats, provenance and licencesEvidence library
- 147 economies · four-period table and country searchWorld geography
- US states, metros and all 3,143 counties · search, filter, exportUnited States
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Each chart keeps its own population and observation period. Wide charts can be scrolled horizontally; open the values beneath any chart for the underlying observations.
01 / Who uses AI at work
Use differs by education and role.
Gallup reports weekly or daily AI use among 28% of US employees; 54% had never used AI in their role. These are selected frequency categories. The education and role comparisons use separate subgroup bases.
Use across the workforce
US employees; 0–100%.
Exact values and populations
| Measure / group | Share | Observation period | Population |
|---|---|---|---|
| Weekly or daily AI use | 28% | Jan–Mar2026 | All surveyed US employees; full sample n=15,482 |
| Never used AI in their role | 54% | Jan–Mar2026 | All surveyed US employees; full sample n=15,482 |
Access differs by education and role
Share using AI weekly or daily; separate subgroup comparisons.
Exact values and populations
| Measure / group | Share | Observation period | Population |
|---|---|---|---|
| College graduates | 40% | Jan–Mar2026 | US employees with a college degree |
| Without a college degree | 17% | Jan–Mar2026 | US employees without a college degree |
| Managers | 37% | Jan–Mar2026 | US employees in management roles |
| Individual contributors | 25% | Jan–Mar2026 | US employees in individual-contributor roles |
02 / Benefits and demands
Faster work is one outcome among several.
Among employees using AI at least a few times in the previous year, 63% report working faster and 31% report being asked to take on more responsibility. Benefits and demands can coexist; responses overlap.
What AI users report
Employees using AI at least a few times in the past year; 0–100%.
Exact values and populations
| Measure / group | Share | Observation period | Population |
|---|---|---|---|
| Work faster | 63% | Jan–Mar2026 | AI-using employees; at least a few times in prior year; n=7,845 |
| Find more creative solutions | 56% | Jan–Mar2026 | AI-using employees; at least a few times in prior year; n=7,845 |
| More time for interesting work | 47% | Jan–Mar2026 | AI-using employees; at least a few times in prior year; n=7,845 |
| Produce higher-quality work | 46% | Jan–Mar2026 | AI-using employees; at least a few times in prior year; n=7,845 |
| Employer asks for more responsibility | 31% | Jan–Mar2026 | AI-using employees; at least a few times in prior year; n=7,845 |
Gallup / American Job Quality Study, 6 October 2026. Fieldwork: 26 January–24 March 2026. Full employee sample: 15,482; AI-user effects sample: 7,845. Self-assessments do not establish causal productivity gains.
03 / The support around learning
Widespread use meets uneven provision.
These are separate surveys of UK undergraduates and AI-aware teachers. Student-reported access to institutional tools and teacher-reported school training are distinct forms of support, with different populations and questions.
Students use more than institutions provide.
Share of surveyed UK full-time undergraduates; annual survey editions.
Read the values and definitions
| Measure | Edition | Share | Denominator | Source |
|---|---|---|---|---|
| Used AI for at least one purpose | 2024 | 66% | Full-time UK undergraduates | p. 10 |
| Used AI for at least one purpose | 2025 | 92% | Full-time UK undergraduates | p. 10 |
| Used AI for at least one purpose | 2026 | 95% | Full-time UK undergraduates | p. 10 |
| Said institution provides generative AI tools | 2024 | 9% | Full-time UK undergraduates | p. 35 |
| Said institution provides generative AI tools | 2025 | 23% | Full-time UK undergraduates | p. 35 |
| Said institution provides generative AI tools | 2026 | 38% | Full-time UK undergraduates | p. 35 |
A gap between teacher use and training.
NLT teachers who had heard of generative AI. Independent questions.
Read the values and definitions
| Measure | Edition | Share | Denominator | Source |
|---|---|---|---|---|
| Had used generative AI | 2025 | 58% | Surveyed teachers who had heard of generative AI | p. 6 |
| Had used generative AI | 2026 | 80.6% | Surveyed teachers who had heard of generative AI | p. 6 |
| Received AI training or support through school | 2026 | 32.6% | Surveyed teachers who had heard of generative AI | p. 19 |
US college students · new evidence
Guidance and self-directed learning can coexist.
In Dartmouth’s weighted US college sample, 79.3% say they mostly developed their AI skills independently and 68.1% report meaningful guidance from professors and courses. These answers can overlap. Only 49.5% say their institution adequately prepares them for future career AI use.
| Statement | Share | Item sample |
|---|---|---|
| Mostly developed AI skills independently, outside formal coursework | 79.3% | 1,003 |
| Professors and courses provided meaningful guidance on effective and ethical AI use | 68.1% | 1,003 |
| Institution adequately prepares students for AI use in their future career | 49.5% | 1,003 |
College Pulse weighted online panel of students at US four-year colleges and universities, 10–23 September 2026. Separate, overlapping agreement measures, not complements or an adoption rate. Weighted online-panel self-reports; working paper, peer review not verified. Design effects were not verified. No causal learning outcomes or direct comparison with HEPI. Working paper, Table 12 (PDF p. 22) · Source and methods.
04 / Judgement and learning
Adoption does not establish critical use.
Among AI-using pupils, 52.3% said they checked outputs for accuracy and 24.1% said they usually copied AI output for homework. These behaviours do not measure causal learning gains; subgroup differences remain associations.
Critical use is uneven within adoption.
Share agreeing with each behaviour among surveyed pupils who use AI.
Read the values and definitions
| Measure | Edition | Share | Denominator | Source |
|---|---|---|---|---|
| Add own thoughts to AI outputs | 2026 | 56.5% | Surveyed 13–18-year-olds who use AI | p. 12 |
| Check AI outputs for accuracy | 2026 | 52.3% | Surveyed 13–18-year-olds who use AI | p. 12 |
| Usually copy AI output when using it for homework | 2026 | 24.1% | Surveyed 13–18-year-olds who use AI | p. 12 |
| Usually copy AI output for homework: FSM | 2026 | 31.6% | AI-using surveyed 13–18-year-olds in the FSM subgroup | p. 13 |
| Usually copy AI output for homework: Non-FSM | 2026 | 22.4% | AI-using surveyed 13–18-year-olds in the Non-FSM subgroup | p. 13 |
What adoption leaves unresolved.
In HEPI’s survey, 94% used AI to help with assessed work, a broader category than submitting AI-written text. While 68% saw AI skills as essential, 48% felt teaching staff helped develop them for future careers. HEPI, pp. 14 and 30–31 ↗
The NLT parent survey adds the home: 67.8% reported using AI together as a family and 62.5% wanted children to learn about AI at school. These figures are from the publisher’s summary; exact fieldwork dates are not specified there. Parent findings ↗
School evidence: NLT’s early-2026 pupil survey covered 40,543 respondents aged 13–18, 92.7% in England. Schools were recruited through its networks; results are not a national probability sample. HEPI is a weighted undergraduate survey. Neither measures causal learning gains. All figures retain their own denominators.