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).
Browse by theme
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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The triadic collaboration system is efficacious in improving writing quality.
Empirical claim in the abstract supported by analysis of the large dataset (57,954 essays from 10,195 students across 120 schools over two years). The paper states findings confirm the system's efficacy in improving writing quality.
We introduce a multidimensional evaluation framework grounded in Systemic Functional Linguistics and the suggestion trajectory tracing pipeline.
Methodological contribution explicitly reported in the abstract: a new evaluation framework combining SFL and a suggestion trajectory tracing pipeline.
We developed a triadic collaboration system to support K-12 writing learning that coordinates LLMs, teachers, and students.
Methodological claim stated in the abstract that the authors designed and developed a triadic collaboration system for K-12 writing learning; presumably implemented and evaluated using the dataset.
Changes in skill demand in online labour markets are an outcome of introducing platform-embedded GenAI.
Synthesis of the study's empirical findings (difference-in-differences results showing increased skill diversity in logo jobs post-logo-AI and mediation evidence via competition) leading to the broader conclusion that platform-embedded GenAI can change skill demand on online labour platforms.
Stronger competition among freelancers partially mediates the effect of the platform-embedded logo-AI on higher skill diversity in logo jobs.
Mediation analysis within the difference-in-differences framework linking measures of freelancer competition to changes in requested skill diversity after the logo-AI launch. Specific mediation estimation details and sample size not provided in the abstract.
Logo jobs exhibit higher skill diversity than other design jobs after the platform introduced logo-AI.
Difference-in-differences comparison of skill-diversity metrics extracted via the authors' LLM-based skill extraction and embedding framework on EPWK job posts for logo design (treatment) versus other design jobs (control), pre- and post-introduction of the platform-embedded logo-AI tool. Sample size not reported in the abstract.
The review synthesizes fragmented evidence and links AI use to SME performance improvements, while outlining directions for future research on sustainable AI adoption.
Self-description of the article's contribution based on the authors' focused literature review (2016-2024).
Cloud-based AI solutions, targeted employee training, and explainable AI are identified strategies to overcome AI adoption challenges in SMEs.
Recommendations synthesized from the reviewed literature (2016-2024); presented as enabling strategies rather than results from a single empirical intervention).
AI supports more data-driven financial planning for SMEs.
Identified across the reviewed empirical and conceptual studies in the 2016-2024 literature (synthesis rather than new empirical estimate).
AI enables real-time fraud detection for SMEs.
Synthesis of empirical and conceptual literature reporting AI applications in fraud detection (review-level claim; no aggregated quantitative effect provided).
AI enables more accurate credit risk assessment for SMEs.
Review synthesizing studies on credit scoring and risk assessment within the 2016-2024 corpus (no single pooled sample size or unified effect estimate provided).
AI improves cash flow and financial forecasting for SMEs.
Synthesis of empirical studies and conceptual papers in the 2016-2024 literature reviewed (review article does not report primary sample sizes/effect estimates).
AI offers strong potential to enhance the financial stability and growth of SMEs when supported by suitable organizational capacities and governance.
Focused review of high-quality research (2016-2024) synthesizing empirical and conceptual studies on AI applications in SME finance (no single-sample primary data reported).
AI is not a simple labor replacement but a powerful enabler, pushing the overall labor structure toward higher skills and added value.
Author interpretation based on the paper's empirical findings (DiD results) that show upward movement of labor-skill composition; specific empirical measures not provided in excerpt.
The findings provide strong empirical support for the 'skill-biased technological change' theory, revealing a significant complementary synergy between technological progress and high-skilled labor in the AI era.
Empirical analysis reported in the paper using a Difference-in-Differences design showing complementarity between AI-related technological progress and high-skilled labor; details on coefficients, confidence intervals, or sample not provided in excerpt.
AI innovation exerts a significant positive impact on the labor structure, optimizing the proportion of high-skilled and low-skilled labor.
Paper's empirical result using a Difference-in-Differences (DiD) empirical strategy; specific sample size or data source not reported in provided excerpt.
The paper provides a conceptual foundation for designing AI systems that model expert sensing over time, positioning cognition as an infrastructural, operational, and professional domain in persistent human-AI systems.
Stated contribution of the paper (conceptual/theoretical contribution rather than empirical evidence).
The Cognitive Operations Research and Training Framework (CORTF) is introduced to support research, education, and workforce development.
Conceptual framework proposed in the paper (no empirical implementation or evaluation presented).
The Cognitive Operations Manager is proposed as a prototype AI-native professional role for coordinating tacit signal modelling, semantic modelling, AI system calibration, expert validation, and ethical governance.
Proposal of a new professional role in the paper (conceptual/visionary; no pilot study, job analysis, or workforce data reported).
Long-term Cognitive Operations are defined as the practices required to maintain and govern such systems, including memory curation, semantic organisation, tacit signal modelling, reasoning calibration, and cognitive governance.
Conceptual taxonomy/definition introduced in the paper (theoretical framing; no empirical validation).
Tacit Signal Infrastructure is introduced as a layer for capturing, structuring, modelling, interpreting, and validating expert tacit signals over time.
Conceptual design/proposal presented in the paper (architectural description; no empirical implementation or evaluation reported).
Next-generation AI systems should move beyond explicit knowledge processing toward the longitudinal modelling of expert tacit sensing.
Normative proposal / recommendation made in the paper as part of a vision; supported by conceptual rationale rather than empirical data.
High-level expertise also depends on tacit sensing: perceiving weak signals, recognising emerging tensions, detecting coherence degradation, and anticipating instability before formal indicators appear.
Conceptual claim grounded in cognitive-science-informed argumentation presented in the paper (no empirical study or sample size reported).
Current generative AI systems are increasingly effective at processing explicit knowledge, including retrieving information, summarising documents, generating explanations, and supporting codified workflows.
Asserted in the paper as a descriptive trend; based on literature synthesis and observations of current generative AI capabilities (no empirical sample or experiment reported in the paper).
Augment Engineering completes a three-discipline progression: Prompt Engineering (one tool), Context Engineering (reproducible pipelines), Augment Engineering (a portfolio of tools across domains).
Conceptual framing presented in the paper describing a proposed progression of disciplines.
A Wright's Law fit (n = 82 artifacts, p < 0.01) shows production acceleration across the artifact portfolio.
Quantitative model reported in the paper: Wright's Law fit on 82 artifacts with reported p-value < 0.01.
A Cochran-Armitage trend test (n = 200 interactions across two chat LLMs, p < 0.01) shows first-pass acceptance rising with prompt-sophistication level.
Quantitative test reported in the paper: Cochran-Armitage trend test on 200 interactions across two chat LLMs, reported p-value < 0.01.
A 5-month formative case study (Nov 2025 to Mar 2026) documents a single practitioner applying Augment Engineering skills across a ten-component orchestration stack spanning seven professional domains, producing work products that would traditionally involve separate domain specialists.
Case study reported in the paper describing one practitioner's activities over five months across a 10-component stack in seven domains; sample size = 1 practitioner.
The paper presents a six-phase orchestration methodology and four portability metrics for Augment Engineering.
Stated methodological contribution within the paper (description of methodology and metrics).
Augment Engineering is a discipline of orchestrating multiple purpose-built AI tools across distinct professional domains, applying prompt and context engineering as portable competencies that transfer across tool boundaries.
Definition and conceptual development presented in the paper (methodological contribution).
Prompt engineering (interaction-level optimization) and context engineering (structured input pipeline design) are domain-portable meta-skills: a practitioner who masters them can apply them to any purpose-built AI tool in any domain.
Conceptual claim supported by the paper's argumentation and exemplified by a single-practitioner case study.
Although AI creates obstacles, it also has the potential to be an important tool for creating innovative opportunities and continued growth if managed with sound practices.
Concluding statement in the paper's abstract presenting a normative/conditional conclusion based on the paper's evaluation and synthesis of evidence (no primary quantified results provided in the supplied text).
AI leads to the creation of new jobs.
The paper explicitly states it examines the creation of new jobs as a ramification of AI (abstract); claim presented qualitatively without reported sample sizes or quantified effect in the provided text.
Policy makers and education/training organizations should comprehensively consider AI and EPU to cope with market uncertainty and ensure the stability and sustainability of China’s ETM.
Policy recommendation derived from the paper's empirical findings on causality, quantile dependence, and asymmetric risk spillovers (argumentative/conclusion statement rather than a direct empirical result).
There is an interaction between AI and EPU: EPU promotes AI during periods of economic stability.
Cross-quantilogram analysis indicating quantile-specific causality/interactions, with EPU predicting AI in stable-period quantiles (method reported; sample size not stated).
There is an interaction between AI and EPU: AI promotes EPU in bullish markets.
Cross-quantilogram analysis showing quantile-dependent interaction (method reported; sample size not stated); specific result described for bullish-market quantiles.
The cross-quantilogram indicates quantile dependence among AI, EPU and ETM: the positive predictive effect of AI on ETM is mainly concentrated in bullish markets.
Cross-quantilogram analysis (quantile cross-dependence test) applied to AI and ETM time-series in China (method reported; sample size not stated).
The nonparametric quantile causality test shows a unidirectional causal relationship from AI to China’s education and training market (ETM).
Nonparametric quantile causality test applied to time-series data on AI and ETM in China (method reported; sample size not stated).
The nonparametric quantile causality test shows a unidirectional causal relationship from AI to EPU.
Nonparametric quantile causality test applied to time-series data on AI and Economic Policy Uncertainty (EPU) in China (method reported; sample size not stated in the provided text).
This study proposes a Workforce Resilience Governance Framework (WRGF) that includes task-level exposure assessment, human augmentation design, reskilling, redeployment, transparent communication, psychological safety, workforce impact accountability, and policy alignment.
Conceptual framework proposed by the authors in the paper (design/proposal; no empirical test described in the excerpt).
The paper calls for action by stakeholders to consider human and environmental moderators when adopting AI.
Policy/recommendation statement in the paper's conclusion/abstract; normative recommendation rather than empirical finding.
We revise the existing framework to redefine effective organizational determinants and shed light on practical implications including industry and education.
Authors' proposed theoretical revision of an existing framework and discussion of implications; presented as a conceptual contribution within the paper.
Most practitioners assume that AI brings productivity boosts owing to enhanced technical capabilities.
Statement of common practitioner belief reported by the authors in the paper's framing; no supporting survey or sample reported in the abstract.
The paper proposes a policy architecture for 'shared gains' centered on learning equity, transition protections, accountable algorithmic management, and distribution-sensitive metrics beyond GDP.
Paper's normative policy proposal presented in abstract, based on the integrative framework and synthesis of secondary sources; no empirical sample size reported.
India's macro growth remains robust.
Statement in abstract referencing official Indian statistics (MoSPI–NSO GDP estimates, 2025); no numerical sample size provided in abstract.
Evidence indicates accelerating AI adoption among firms in advanced economies.
Abstract cites validated secondary sources including OECD (2026) and other global reports; no primary sample size reported in paper abstract.
AI is increasingly embedded in production, services, and workforce management.
Statement in paper's abstract supported by integrative socio-technical political economy framework and validated secondary sources (OECD, ILO, UNDP, WTO, WEF). No primary sample size reported.
A human-centered approach is needed that integrates technological advancement with reskilling initiatives, labor protections, and inclusive policies.
Authors' prescriptive/recommendation based on their thematic synthesis of the reviewed literature (2010–2024).
The integration of AI into manufacturing offers substantial gains in efficiency, productivity, and operational performance.
Authors' systematic literature review of interdisciplinary studies (2010–2024) using thematic synthesis; synthesis of prior empirical and conceptual studies reporting efficiency/productivity effects of AI in manufacturing.
A human-centred approach underpinned by ongoing reskilling and ethical governance is vital for sustainable workforce evolution in the Indian IT sector.
Authors' policy/recommendation derived from their literature synthesis and thematic analysis (qualitative conclusion).