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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GenAI supports idea generation, business case analysis, scenario planning, data interpretation, and professional communication, thereby transforming knowledge production and management learning.
Review of examples and arguments in the literature (conceptual synthesis within the review article); no primary empirical sample size reported.
Dengan strategi yang terarah, terukur, dan berkelanjutan, tenaga kerja Indonesia tidak hanya mampu bertahan, tetapi juga berperan aktif dalam mendorong pertumbuhan ekonomi digital yang kompetitif, inklusif, dan berkeadilan.
Kesimpulan dan rekomendasi yang diambil dari tinjauan literatur sistematis (n=33).
Diperlukan kerangka regulasi adaptif, budaya pembelajaran sepanjang hayat, dan perlindungan sosial (perluasan jaminan sosial dan program transisi karir) untuk melindungi pekerja terdampak transisi AI.
Rekomendasi kebijakan yang disimpulkan dari studi literatur sistematis (33 sumber).
Kolaborasi erat antara pemerintah, institusi pendidikan, dan industri melalui skema link and match diperlukan untuk mendukung transformasi SDM.
Rekomendasi berbasis studi literatur sistematis yang menelaah 33 sumber (n=33).
Penguatan soft skills seperti komunikasi dan adaptabilitas penting untuk menjaga daya saing tenaga kerja di era AI.
Rekomendasi berbasis studi literatur sistematis yang menelaah 33 sumber (n=33).
Diperlukan program upskilling dan reskilling yang inklusif untuk menghadapi transformasi akibat AI.
Rekomendasi berbasis studi literatur sistematis yang menelaah 33 sumber (n=33).
Strategi transformasi SDM harus dijalankan melalui peningkatan kualitas pendidikan.
Rekomendasi berbasis studi literatur sistematis yang menelaah 33 sumber (n=33).
Penerapan AI mendorong pergeseran kebutuhan kompetensi dari keterampilan teknis konvensional menuju literasi digital, analisis data, kreativitas, dan kemampuan berpikir kritis.
Sistematis studi literatur yang menelaah 33 sumber ilmiah, laporan lembaga internasional, dan kebijakan terkait (n=33).
Organizations increasingly use intelligent systems for high-stakes strategic decision-making (SDM).
Introductory/background statement in the paper summarizing trends in practice and motivating the research; based on literature review and observed practice (no study data reported here).
The combined findings enable planning for a broader skill-gap analysis of educational curricula to bridge gaps and support upskilling/reskilling of wind-energy professionals.
Conclusion/recommendation in the paper based on integrated results from interviews, surveys, and job postings; this is presented as an intended next step rather than an empirically tested intervention.
Survey results provide insights on preferred training formats for upskilling/reskilling in the wind sector.
Paper states survey collected preferences on training formats; no survey sample size or preference breakdown is provided in the summary.
Job-posting analysis shows that approximately 44% of engineering-related positions in the wind sector require advanced digital skills.
Quantified result reported from the paper's job-posting analysis; the summary gives the percentage but does not report the number of job postings analysed.
Across all sources, scientific programming and numerical modelling consistently emerge as cornerstone competencies for the wind sector.
Result reported as consistent across interviews, surveys, and job-posting analysis; no quantitative ranking, counts, or sample sizes provided in the summary.
Triangulation of survey data, expert interviews, and job-posting analysis suggests a coherent picture of advanced digital skills priorities within the wind energy sector.
Integration of qualitative interviews, quantitative survey results, and job-posting analysis reported in the paper; no numerical concordance statistics presented.
Interviews and job-postings are analysed using Natural Language Processing (NLP), enabling automated analyses that can be repeated in future years to track the evolution of required skills.
Paper states NLP was applied to interviews and job-posting corpora and frames this as enabling repeatable automated analyses; no performance metrics or NLP sample sizes provided.
This study maps demand for advanced digital skills in the wind industry using a mixed-method approach combining expert interviews, survey data, and job-posting analysis.
Methodological description in the paper; explicit listing of the three data sources and their intended complementary roles.
The wind energy industry is facing a growing need for professionals with advanced digital skills beyond traditional IT positions.
Statement in paper based on mixed-method mapping (expert interviews, survey data, and job-posting analysis); no sample size reported for the sector-wide assertion.
Research should prioritise longitudinal and theory-informed evaluations, including intersectionality-informed analyses, and assess downstream impacts on women’s career trajectories alongside robust governance and accountability practices.
Authors' recommendations based on identified gaps from the scoping review.
Using inductive thematic analysis, we identified three functional domains: (1) bias mitigation and representation, (2) skills development and empowerment and (3) career pathways and retention.
Authors' thematic analysis of the 13 empirical studies included in the scoping review.
Artificial intelligence (AI) is increasingly integrated into career guidance and organisational decision systems.
Statement in abstract indicating observed trend; supported by literature search contextualising the review (scoping review using PRISMA-ScR).
The paper proposes the Embedded Formation Degree (EFD), a four-component framework consisting of accelerated domain entry, a four-year AI fluency track, an embedded practice firm, and structurally integrated employer partners.
Conceptual proposal put forward by the author(s) in this paper (descriptive statement in the abstract).
Prior research has emphasized GenAI’s ability to enhance productivity and creative outcomes.
Literature review / background statements in the paper referencing prior studies (no sample size specified in the paper's statement).
Codeforces practice shifted toward this AI-style signature across cohorts over two AI rollouts.
Time-series/cohort analysis of CF practice data spanning two AI rollout periods (authors report cohort-level shifts; exact n not given in abstract).
Generative AI raises short-term productivity by completing tasks that learners would otherwise practice on their own.
Statement in paper's introduction/abstract; asserted as background premise (no specific sample size or empirical test reported in the abstract).
The paper concludes with policy recommendations to foster a conducive environment for AI integration, positioning Algeria to leverage technological advances for sustainable economic growth.
Concluding statement in the paper summarizing recommended policy actions; framed as guidance rather than empirically tested interventions.
Targeted investments and policy reforms could accelerate AI adoption and productivity gains in Algeria.
Policy recommendation inferred from the study's comparative findings and supported by citations to Brynjolfsson, Rock, and Syverson (2017) and McKinsey & Company (2023); presented as a prospective/conditional claim rather than an empirically estimated causal effect within the paper.
Artificial intelligence (AI) is rapidly transforming global economies by enhancing productivity, enabling innovation, and reshaping labor markets.
Framing claim supported by citations to Agrawal, Gans, & Goldfarb (2019) and Acemoglu & Restrepo (2020) as described in the paper's introduction; no primary empirical estimate reported in this paper.
Improvements in skill adaptability reduce the risk of automation substitution.
Analysis linking measures of skill adaptability to lower estimated risk/impact of occupational automation exposure in the CFPS-based models.
Vocational education background and participation in on-the-job training can mitigate the negative effects of technological shocks on wages.
Interaction analyses in the CFPS-based regressions showing that vocational education and on-the-job training attenuate the estimated negative impact of automation exposure on wages.
Technological shocks significantly widen the skill wage gap.
Empirical analysis using the CFPS panel and the occupational task automation exposure index; paper reports statistically significant estimated effect of automation exposure on the skill wage gap.
Through a case study on house price prediction, we find that AACT outperforms traditional AI-based decision-support in reducing over-reliance on AI.
Empirical comparison reported in a case study (house price prediction) between AACT and traditional AI decision-support; includes measured over-reliance and statistical comparison (sample size not reported in abstract).
We introduce the AI-Assisted Critical Thinking (AACT) framework, which leverages a domain-specific AI model’s counterfactual analysis of human decision to help decision-makers identify potential flaws in their decision argument and support the correction of them.
Paper presents a new framework (AACT) and describes its design; demonstrated via a case study (house price prediction).
Digital learning platforms and AI-based training tools are increasingly used as central mechanisms to support continuous skill acquisition and professional growth.
Synthesis of prior studies and thematic literature discussed in the editorial (Bankins et al., 2024a; other cited works).
Adoption of STARA increases the need to upskill and reskill workers across skill levels, with even high-skilled workers expected to integrate new digital competencies into their professional trajectories.
Literature synthesis and cited empirical/conceptual studies (e.g. Hani et al., 2025; Ibrahim and Abiddin, 2024; Singh and Chandra, 2026; Tariq, 2026).
The article proposes a Strategic Action Framework to support more inclusive and context-responsive AI ecosystems.
Policy recommendation/framework presented by the authors as a conclusion; not empirically evaluated within the study.
Empirical observations show that youth mobilize AI tools for translation, content creation, customer engagement, and micro-entrepreneurial activities, enabling partial and situational approximation of selected formal-sector practices.
Qualitative interview data from the 125 semi-structured interviews in three DRC cities, used as illustrative grounding for observed uses of AI by youth.
TAs remained fully in control and could use, edit, or ignore AI-generated drafts at their discretion.
Study design statement from the randomized field experiment: intervention provided AI-assisted feedback drafts to TAs after grading but kept TAs fully in control to accept, edit, or ignore drafts. 11 TAs in the course.
Qualitative findings indicate AI-assisted drafts function as editable scaffolds that lower barriers to initiating feedback rather than reducing overall effort.
Qualitative interviews conducted as part of the mixed-methods study (course included 11 TAs and 88 students); thematic/qualitative analysis reported that TAs described drafts as scaffolds that made starting feedback easier and did not simply replace TA effort.
AI-assisted feedback increases feedback length by 39.8 characters.
Randomized field experiment in the same course; comparison of feedback length between treatment and control. Reported estimate: +39.8 chars, SE=3.45, p<0.001. Student-level random assignment (n=88); 11 TAs.
AI-assisted feedback significantly increases feedback provision by 10.8 percentage points.
Randomized field experiment in a 300-level machine learning course. Student submissions (n=88) were randomly assigned to treatment (TAs received AI-assisted feedback drafts) or control. Reported estimate: +10.8 percentage points, SE=1.1, p<0.001. 11 TAs participated and could use, edit, or ignore drafts.
Using the three metrics (data product adoption, time-to-find, time-to-insight) ties platform success to measurable business value rather than internal activity.
Argument in the paper about metric selection and their role in assessing platform success (methodological rationale).
A staged framework that shifts ownership from hub to spokes avoids both centralized bottlenecks and uncoordinated decentralization.
Organizational/process recommendation presented in the paper as a way to manage decentralization (design rationale).
Natural-language conversational interfaces democratize access for business users and expose historically underutilized enterprise data.
Proposed UX/interaction benefit asserted in the paper (design claim; no empirical measurement reported in the excerpt).
Large language models (LLMs) that automate governance tasks also lower the barrier for domain practitioners to develop genuine cross-functional expertise spanning business and data engineering, enabling spoke teams to take on greater end-to-end ownership without proportionally increasing their dependence on the hub.
Argument in the paper linking AI/LLM capabilities to skill enablement and reduced hub dependence (conceptual claim; no empirical results in the excerpt).
Domain spokes own business semantics, product backlogs, and local iteration cadence, progressively assuming greater responsibility as they mature (shifting operational ownership outward over time).
Architectural/organizational design element described in the paper (procedural proposal for staged ownership transfer).
A central hub (Center of Excellence) can provide shared platform services, policy automation, and AI-enabled governance that automatically standardizes data products, generates quality rules, drafts data contracts, and reviews changes for regressions.
Functional capabilities described in the proposed architecture; presented as what the hub component will provide (design/specification).
An AI-augmented hub-and-spoke model layered on a modern lakehouse architecture can relax the flexibility-versus-control trade-off inherent in enterprise data platforms.
Proposed architectural solution and theoretical argument in the paper (design proposal; no reported experimental/field results provided in the text excerpt).
These findings suggest a dynamically adaptive LLM-teacher collaboration as student proficiency increases.
Interpretive/recommendation claim in the abstract: authors conclude that collaboration should adapt dynamically with student proficiency based on observed efficacy and ceiling effects.
Both LLM and teacher are critical for student skill improvement.
Abstract statement reporting that both LLM and teacher contributions were important for skill improvement; supported by empirical analysis on the reported dataset (57,954 essays).
Teachers act as pedagogical gatekeepers and bridges to guarantee feedback quality.
Stated in the abstract that within the triadic system teachers ensure feedback quality, implying a complementary role confirmed by the authors' empirical analysis or system design.