Evidence (3308 claims)
Search and filter individual claims pulled from the papers. Looking for a specific finding ("what's the effect on wages?"), you're in the right place. Want to compare whole outcome categories against each other instead? Use the Evidence Explorer.
The board below groups claims two ways: by broad theme (nine paper-level topics) and by outcome category (the 34 claim-level outcomes that the Explorer and Syntheses also use).
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Nine broad, paper-level topics. Click one to filter the claims below.
Adoption
9875 claims
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Productivity
8807 claims
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Governance
7870 claims
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Human-AI Collaboration
7560 claims
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Org Design
4892 claims
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Innovation
4781 claims
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Labor Markets
4004 claims
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Skills & Training
3308 claims
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Inequality
2332 claims
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Claims by outcome category
Counts by direction of finding. These are the same 34 outcome categories the Explorer compares and the Syntheses are written for. A linked row has a published synthesis.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 870 | 233 | 116 | 1066 | 2363 |
| Governance & Regulation | 976 | 451 | 218 | 133 | 1809 |
| Organizational Efficiency | 949 | 224 | 144 | 88 | 1416 |
| Technology Adoption Rate | 764 | 287 | 141 | 122 | 1325 |
| Research Productivity | 501 | 152 | 74 | 362 | 1101 |
| Output Quality | 542 | 216 | 69 | 69 | 896 |
| Decision Quality | 387 | 198 | 94 | 54 | 740 |
| Firm Productivity | 513 | 67 | 101 | 27 | 714 |
| AI Safety & Ethics | 249 | 303 | 73 | 36 | 667 |
| Market Structure | 190 | 192 | 134 | 27 | 548 |
| Task Allocation | 243 | 77 | 91 | 36 | 452 |
| Innovation Output | 291 | 33 | 55 | 20 | 401 |
| Skill Acquisition | 206 | 72 | 65 | 21 | 364 |
| Employment Level | 133 | 63 | 115 | 22 | 335 |
| Fiscal & Macroeconomic | 153 | 79 | 52 | 32 | 323 |
| Task Completion Time | 206 | 37 | 12 | 15 | 272 |
| Firm Revenue | 179 | 52 | 29 | 5 | 266 |
| Consumer Welfare | 130 | 76 | 47 | 13 | 266 |
| Inequality Measures | 48 | 137 | 51 | 6 | 242 |
| Worker Satisfaction | 101 | 81 | 25 | 13 | 220 |
| Error Rate | 84 | 110 | 11 | 5 | 210 |
| Wages & Compensation | 98 | 47 | 30 | 10 | 185 |
| Regulatory Compliance | 88 | 73 | 17 | 7 | 185 |
| Automation Exposure | 66 | 64 | 33 | 16 | 182 |
| Team Performance | 105 | 29 | 30 | 11 | 176 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 114 | 21 | 14 | 8 | 158 |
| Job Displacement | 12 | 90 | 24 | 1 | 127 |
| Hiring & Recruitment | 57 | 9 | 9 | 5 | 80 |
| Skill Obsolescence | 6 | 56 | 9 | 1 | 72 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 21 | 17 | 1 | 57 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
Skills Training
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We searched seven databases (plus backward and forward citation searching) and synthesised 13 empirical studies published between 2018 and 2025.
Methods reported in abstract: PRISMA-ScR scoping review with a preregistered protocol; explicit count of included studies and publication date range.
Self-evaluated creative performance remained unchanged when using GenAI.
Same experiment with 82 participants; authors report no significant difference in self-evaluated creative performance between GenAI users and controls.
From Codeforces histories we build an AI-prompt signature characterised by more first-attempt acceptances and fewer attempts and retries, consistent with AI-assisted practice.
Empirical construction from CF submission histories (pattern: increased first-try accepts, fewer retries). Method: analysis of historical submission logs; sample size not stated in abstract.
The International Collegiate Programming Contest (ICPC) and the International Olympiad in Informatics (IOI) prohibit AI under proctoring and admit entrants through qualification rounds, whereas online Codeforces (CF) contests are unproctored and open to all.
Descriptive factual claim about contest rules and formats (institutional description in paper); based on contest rules and organizational formats referenced by authors.
This study benchmarks Algeria’s readiness to adopt AI against Morocco, Egypt, and Turkey using data from the World Bank (2022), the Oxford Insights Government AI Readiness Index, and sector-specific studies.
Methodological statement in the paper specifying data sources used for the comparative assessment (World Bank 2022, Oxford Insights index, sector studies).
The study uses four waves of data from the China Family Panel Studies (CFPS) from 2022 to 2025, constructs an individual-level indicator of the skill wage gap, and adopts an occupational task automation exposure index as a proxy variable for technological shocks.
Authors report using four waves of CFPS (2022–2025); they state they constructed an individual-level skill-wage-gap indicator and use an occupational task automation exposure index as the proxy for technological shocks (methodological description in paper).
The article aims to provide systematic literature support for subsequent research and adaptive policy formulation.
Statement of the paper's stated objective; methodological and policy-intent claim from the authors.
This article is based on a systematic literature review and summarizes the four core theoretical mechanisms of substitution, complementarity, new task creation, and skill mismatch.
Methodological claim from the paper: the authors conducted a systematic literature review and identified these four theoretical mechanisms.
The study integrates ICT4D, socio-technical systems theory, and the capability approach as its theoretical framing.
Methodological/theoretical statement in the paper describing the integrative framework used for analysis.
While grounded in the DRC, the findings offer broader insights into AI adoption dynamics across informal economies in Sub-Saharan Africa and beyond.
Authors' claim of broader relevance/generalizability based on the DRC case study and theoretical framing.
AI adoption in the DRC emerges through hybrid socio-technical interactions between bottom-up youth innovation and weakly coordinated institutional frameworks, rather than following policy-led or infrastructure-first trajectories.
Theoretical integration (ICT4D, socio-technical systems, capability approach) and qualitative interview evidence used to characterize observed adoption pathways.
The article introduces 'compressed professionalization', defined as the accelerated acquisition and immediate market enactment of professional-level digital capabilities outside formal institutional pathways.
Conceptual/theoretical contribution presented and defined in the paper, supported by illustrative field observations from the interviews.
The study drew on 125 semi-structured interviews conducted in Kinshasa, Lubumbashi, and Goma.
Primary qualitative fieldwork reported in the paper: 125 semi-structured interviews across three DRC cities (Kinshasa, Lubumbashi, Goma).
AI-assisted feedback does not reduce time per character (i.e., it does not increase time cost per unit of feedback).
Time-per-character was measured in the randomized field experiment; authors report no reduction (no increase in time per character) associated with the AI-assisted drafts. Student-level/completion-level data from the experiment (n=88); 11 TAs.
AI-assisted feedback does not negatively affect student usefulness ratings.
Measured student ratings of usefulness in the randomized field experiment; authors report no negative effect of the treatment on these ratings (no significant decrease reported). Student-level sample n=88; 11 TAs.
The paper evaluates the proposed architecture using the outcome metric 'time-to-insight'.
Methodological statement in the paper listing evaluation metrics.
The paper evaluates the proposed architecture using the outcome metric 'time-to-find'.
Methodological statement in the paper listing evaluation metrics.
The paper evaluates the proposed architecture using the outcome metric 'data product adoption'.
Methodological statement in the paper listing evaluation metrics.
This paper contributes a large-scale empirical dataset involving 57,954 essays from 10,195 students across 120 schools over two years.
The paper explicitly states the dataset size and coverage in the abstract: 57,954 essays, 10,195 students, 120 schools, two-year period.
We leverage logo design job posts before and after the launch of an early-stage platform-embedded logo-AI tool on the online labour market EPWK, using a difference-in-differences design and a new large language model-based skill extraction and embedding framework.
Paper's described empirical design and methods: dataset of logo design job posts on EPWK around the logo-AI tool launch; difference-in-differences analytic approach; LLM-based skill extraction and embedding pipeline. No sample size provided in the abstract.
Existing research mainly examines general-purpose GenAI, such as ChatGPT, and focuses on aggregate outcomes, including falling demand and compressed prices in easily automated tasks, while revealing little about the demand for work skills and the role of platform-embedded GenAI.
Paper's literature review / background statement summarizing prior empirical work on general-purpose GenAI (e.g., studies documenting falling demand and price compression in automatable tasks). No sample size reported in this statement.
This paper uses the Difference-in-Differences method for empirical research.
Methodological statement in the excerpt explicitly naming the DiD approach.
Because all observations come from a single practitioner, the inferential statistics are exploratory and hypothesis-generating rather than confirmatory; portability across the full portfolio awaits multi-practitioner replication.
Explicit limitation stated in the paper about the single-practitioner design and its implications for inference.
The study contributes a taxonomy of AI workforce impact, a Workforce Resilience Readiness Score (WRRS), an AI Workforce Trust Index (AWTI), an Ethical Automation Boundary concept, and a pilot empirical validation design.
Declared methodological and conceptual contributions in the paper (these are presented as deliverables of the study; no validated results reported in the excerpt).
The International Labour Organization's 2025 update highlights the need to assess the exposure of generative AI at the task level using task data, expert input, and AI model predictions.
Reference to ILO 2025 update recommendation described in the paper (policy/technical guidance rather than primary empirical data in the excerpt).
The analysis is structured across past, present, and future phases using an integrative socio-technical political economy framework and validated secondary sources (OECD, ILO, UNDP, WTO, WEF) alongside official Indian statistics and sector evidence.
Methodological claim stated in abstract describing the approach and data sources used in the paper (OECD, ILO, UNDP, WTO, WEF, MoSPI/NSO, PLFS, HCES, Reuters, Nasscom).
There is a significant deficiency in India-centric qualitative investigations on human-AI collaboration in the IT sector.
Authors' review of peer-reviewed literature and secondary data concluding a gap in India-focused qualitative studies (literature gap analysis). No numeric count provided.
The study examines the impact of AI technologies on Uzbekistan's labor market transformation in the context of implementing the national strategy 'Digital Uzbekistan - 2030' and the Strategy for the Development of AI Technologies until 2030.
Framing and scope statement in the paper; analysis based on national strategy documents, statistical data, industry reviews, and regulatory legal documents.
We interviewed 24 product-focused individuals at a large technology firm about how AI has impacted their own work, their work within their product team, and their professional interactions.
Qualitative semi-structured interviews with 24 product-focused employees at a single large technology firm; sample size = 24.
Identification of effects uses within-firm variation with firm and city-by-year fixed effects.
Identification strategy reported in abstract: within-firm variation under firm and city-by-year fixed effects.
The study measures four skill-category demand shares and their within-category importance from job-description text.
Methodological statement in abstract: measurement of four skill-category demand shares and within-category importance via job-description text.
AI exposure is decomposed into displacement and augmentation components based on task routineness.
Methodological claim in abstract: decomposition of exposure into displacement and augmentation using a routineness criterion for tasks.
The authors construct firm-by-year potential AI exposure via semantic matching between AI patent texts and detailed occupation task descriptions.
Method description in abstract: semantic matching of AI patent texts to occupation task descriptions to build firm-by-year exposure.
The study uses approximately 67 million online job postings from two major Chinese recruitment platforms (2019–2024).
Statement in paper abstract describing dataset size and source (job postings from two major Chinese recruitment platforms over 2019–2024).
The study extends the Technology Acceptance Model (TAM), Dynamic Capabilities Theory, and the Technology-Organisation-Environment (TOE) framework into the qualitative, emerging-economy entrepreneurial context.
Authors' stated theoretical contribution based on mapping thematic results to TAM, Dynamic Capabilities, and TOE frameworks within analysis and discussion sections.
This study employed an interpretivist, qualitative research design using sixteen in-depth semi-structured interviews with entrepreneurs across fintech, edtech, health-tech, logistics, retail, and SaaS in Delhi/NCR, India, and used Braun & Clarke's (2006) six-phase thematic analysis framework.
Explicit methodological description in the paper: interpretivist qualitative design; n=16 in-depth semi-structured interviews across specified sectors in Delhi/NCR; thematic analysis following Braun & Clarke (2006).
Light AI users perform similarly to matched users who do not use AI.
Same controlled logical reasoning experiment with on-demand AI assistance comparing light AI users to matched non-users (sample size not stated in abstract).
We will release the reanalysis pipeline to support replication.
Authors' statement of intent in the paper to release code/pipeline for replication.
In offensive cybersecurity, the marginal benefit of Skills collapses: the spread between the no-Skills and full-Skills conditions is only 8.9 percentage points (p = 0.71, χ²; p = 0.25, Cochran–Armitage trend test; five of six pairwise Cohen's h values fall below the 0.2 small-effect threshold).
Statistical re-analysis of the 180-run CTF study comparing no-Skills vs full-Skills conditions: reported spread = 8.9 percentage points; reported p-values from χ² and Cochran–Armitage trend tests; reported Cohen's h comparisons.
Those four documentation conditions correspond almost exactly to a No-Skills, Experiential-Skills, Curated-Skills, and Comprehensive-Skills ablation.
Authors map the four documentation-line-count conditions from the re-analyzed study to skill-ablation categories (No/Experiential/Curated/Comprehensive) as part of their interpretive re-analysis.
We re-analyze a recently published 180-run controlled study of an MCP-grounded autonomous Capture-the-Flag (CTF) agent under four documentation conditions (55, 1,478, 1,976, and 4,147 lines).
Authors' re-analysis of an existing controlled study consisting of 180 runs and four documentation conditions with the stated line counts; this is a descriptive claim about the re-analysis dataset and experimental conditions.
This study used a controlled mixed-design experiment with 60 participants who completed analytical survival ranking tasks in multi-turn human–AI collaborations, with pre/post measurements and two types of prompting training (general or sycophancy-focused).
Methodological description in the paper's abstract/summary.
Following PRISMA 2020 guidelines, searches across Google Scholar, Web of Science, Scopus, ScienceDirect, and CNKI yielded 1,562 initial records, of which 21 studies published between 2019 and 2026 met inclusion criteria.
Methodological description of the systematic literature review reported in the paper: initial records = 1,562; included studies = 21; publication years 2019–2026.
Small and medium-sized enterprises (SMEs) constitute over 98.5% of businesses in many economies including China.
Descriptive statistic reported in the paper's background/intro; source of the statistic not specified within the summary provided.
We conducted a randomized controlled experiment in which participants—analogs of early-career knowledge workers—were assigned to self-study a technical domain using either traditional resources or large-language-model (LLM) assistance.
Statement of experimental design in the paper (randomized controlled experiment assigning participants to either traditional resources or LLM assistance; participants described as analogs of early-career knowledge workers).
Skills can be mapped into three categories: those AI is absorbing, those needed to work alongside AI today, and those that make humans irreplaceable tomorrow.
Conceptual taxonomy offered in the chapter, based on labour market data and workplace evidence; presented as an analytical framework rather than a quantified finding.
Fear and hype about technological transitions are temporary.
One of five lessons drawn from historical analogy and labour market history as presented in the chapter.
Virtually every job is being touched by AI.
Stated in chapter summary; claimed on the basis of labour market data and emerging workplace evidence (no numeric sample given in excerpt).
Only 9% of jobs are fully automatable.
Reported directly in chapter; based on labour market data (specific data source and sample size not stated in the excerpt).
AI automates tasks, not jobs.
Conceptual argument in chapter drawing on labour market data and historical analogy; presented as a framing claim rather than a specific empirical estimate.