Evidence (1902 claims)
Adoption
5126 claims
Productivity
4409 claims
Governance
4049 claims
Human-AI Collaboration
2954 claims
Labor Markets
2432 claims
Org Design
2273 claims
Innovation
2215 claims
Skills & Training
1902 claims
Inequality
1286 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 369 | 105 | 58 | 432 | 972 |
| Governance & Regulation | 365 | 171 | 113 | 54 | 713 |
| Research Productivity | 229 | 95 | 33 | 294 | 655 |
| Organizational Efficiency | 354 | 82 | 58 | 34 | 531 |
| Technology Adoption Rate | 277 | 115 | 63 | 27 | 486 |
| Firm Productivity | 273 | 33 | 68 | 10 | 389 |
| AI Safety & Ethics | 112 | 177 | 43 | 24 | 358 |
| Output Quality | 228 | 61 | 23 | 25 | 337 |
| Market Structure | 105 | 118 | 81 | 14 | 323 |
| Decision Quality | 154 | 68 | 33 | 17 | 275 |
| Employment Level | 68 | 32 | 74 | 8 | 184 |
| Fiscal & Macroeconomic | 74 | 52 | 32 | 21 | 183 |
| Skill Acquisition | 85 | 31 | 38 | 9 | 163 |
| Firm Revenue | 96 | 30 | 22 | — | 148 |
| Innovation Output | 100 | 11 | 20 | 11 | 143 |
| Consumer Welfare | 66 | 29 | 35 | 7 | 137 |
| Regulatory Compliance | 51 | 61 | 13 | 3 | 128 |
| Inequality Measures | 24 | 66 | 31 | 4 | 125 |
| Task Allocation | 64 | 6 | 28 | 6 | 104 |
| Error Rate | 42 | 47 | 6 | — | 95 |
| Training Effectiveness | 55 | 12 | 10 | 16 | 93 |
| Worker Satisfaction | 42 | 32 | 11 | 6 | 91 |
| Task Completion Time | 71 | 5 | 3 | 1 | 80 |
| Wages & Compensation | 38 | 13 | 19 | 4 | 74 |
| Team Performance | 41 | 8 | 15 | 7 | 72 |
| Hiring & Recruitment | 39 | 4 | 6 | 3 | 52 |
| Automation Exposure | 17 | 15 | 9 | 5 | 46 |
| Job Displacement | 5 | 28 | 12 | — | 45 |
| Social Protection | 18 | 8 | 6 | 1 | 33 |
| Developer Productivity | 25 | 1 | 2 | 1 | 29 |
| Worker Turnover | 10 | 12 | — | 3 | 25 |
| Creative Output | 15 | 5 | 3 | 1 | 24 |
| Skill Obsolescence | 3 | 18 | 2 | — | 23 |
| Labor Share of Income | 7 | 4 | 9 | — | 20 |
Skills Training
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Active, collaborative AI use preserves perceived meaningfulness of work at levels comparable to independent work and does not produce the lasting psychological costs seen with passive use.
Pre-registered experiment (N = 269) with post-manipulation and post-return measures; Active-collaboration condition matched No-AI on meaningfulness and showed no persistent declines after returning to manual tasks.
Active, collaborative AI use preserves psychological ownership of outputs at levels comparable to independent work.
Pre-registered experiment (N = 269); Active-collaboration condition reported ownership levels similar to No-AI condition on self-report scales.
Active, collaborative AI use (human drafts first, then uses AI to refine) preserves self-efficacy at levels comparable to independent (no-AI) work.
Pre-registered experiment (N = 269) comparing Active-collaboration and No-AI conditions; no statistically meaningful differences in self-efficacy between them (self-reported measures).
The work is qualitative and exploratory — presenting naturalistic phenomena rather than causal empirical estimates, and is intended to be hypothesis-generating rather than definitive.
Methodology explicitly stated: naturalistic, qualitative daily observations over one month across multiple platforms; comparative observational documentation without experimental manipulation or causal identification.
Results are from role-play contexts and short-term interventions; economic estimates of benefit require validation in field settings, across diverse populations, and with different LLM models.
Authors' caveats and limitations stated in the paper noting external validity concerns and the experimental context (role-play, short-term follow-up).
Outcome measures included alignment to the normative taxonomy (coding/automated), recipient-rated perceptions of being heard/validated, and blinded empathy judgments.
Methods section description listing primary and secondary outcomes used in the trial and evaluations.
A data-driven taxonomy was derived mapping common idiomatic empathic moves (e.g., validation, perspective-taking, emotional labeling, offers of support) used in naturalistic support conversations.
Textual analysis of the collected corpus (33,938 messages) produced an operational taxonomy of idiomatic empathic expressions used in the role-play dialogues.
The Lend an Ear platform collected a large conversational corpus: 33,938 messages across 2,904 conversations with 968 participants.
Dataset description reported in the paper specifying counts of participants, conversations, and messages used to build and analyze communication patterns.
Key empirical metrics introduced and used are: AI adoption rates (sector-level intensity), Skill shift index, Hybrid job share, and employment levels/net changes by sector.
Methods description listing the constructed metrics used in the simulated dataset and subsequent analyses (definitions and calculation procedures provided in the paper).
The study's main limitations include reliance on a simulated dataset rather than exhaustive administrative microdata, literature limited to selected publishers/years, and correlational (not causal) identification of some effects.
Authors' explicitly stated limitations in the paper's methods and discussion sections describing data choices (simulated dataset, selected publishers 2020–2024) and the observational/correlational nature of several analyses.
This work is conceptual/theoretical and reports no original empirical dataset; it explicitly calls for mixed-methods empirical validation (case studies, field experiments, longitudinal studies), measurement development, and multi-level data collection.
Explicit methodological statement in the paper describing its nature as a theoretical synthesis and listing empirical needs; no empirical sample provided.
Four autonomous agents were benchmarked on the same fresh CTF challenge set alongside human teams.
Benchmarking experiment described in the study: four autonomous AI agents evaluated on the identical fresh challenge set used in the live onsite CTF.
The study's empirical base consists of 40 semi-structured interviews with cross-industry project practitioners in the UK, analyzed using thematic qualitative methods.
Stated data and methods in the paper: sample size (40), interview method, cross-industry sampling, and thematic analysis.
Limitation: Implementation heterogeneity — the costs and feasibility of the recommended HR changes vary by context and may affect generalisability.
Explicit limitation acknowledged in the paper; drawn from theoretical reasoning about contextual heterogeneity and practitioner variability.
Limitation: The framework is conceptual and requires empirical validation across sectors, firm sizes and AI‑intensity levels.
Explicit limitation acknowledged by the authors; based on the paper's method (theoretical synthesis, no original data).
The paper generates empirically testable propositions (e.g., how leader practices affect AI adoption speed, task reallocation, productivity, error rates, employee well‑being and turnover) and suggests natural‑experiment settings for evaluation.
Stated methodological output of the conceptual synthesis; the paper lists candidate empirical tests and research opportunities but contains no original empirical tests.
The available evidence consists mainly of promising empirical studies and case studies, but there are few long-run, generalized ROI or productivity estimates; results are heterogeneous across therapeutic areas.
Self-described limitation of the narrative review: heterogeneity of study designs and outcomes precluded pooled quantitative estimates and long-run ROI assessment.
AI applications span the full drug development pipeline, including target discovery, in silico screening and de novo design, preclinical safety models, clinical trial design and patient selection/monitoring, and post-marketing surveillance.
Comprehensive literature synthesis across preclinical, clinical, and post-marketing sources in the narrative review summarizing documented uses across these stages.
Current evidence is illustrative rather than systematic; there is a lack of long-run, quantitative measures of AI’s effect on late-stage clinical outcomes in the literature reviewed.
Explicit methodological statement in the paper: study is an expert/opinion synthesis and narrative review with no new causal econometric estimates or primary experimental data.
Suggested metrics for researchers and investors to monitor include R&D cycle time, cost per IND/NDA, proportion of projects using AI, success rates at development stages, market concentration measures, and investment flows into AI-enabled biotech vs incumbents.
Recommendations made in the Implications section as metrics to watch; no empirical tracking or baseline measures provided.
Limitations of the analysis include limited empirical validation of archetypes or impacts and potential selection bias toward prominent firms and technologies.
Explicit limitations stated in the Data & Methods section of the paper.
The paper is an editorial/conceptual synthesis rather than a primary empirical study: it uses qualitative analysis and illustrative examples, and reports no new quantitative estimates.
Explicit statement in the Data & Methods section of the paper describing document type, approach, evidence base, and limitations.
Ethical oversight and governance (addressing bias, consent, downstream risks) are critical constraints that must be addressed for AI to generate sustained benefits.
Normative synthesis referencing common ethical concerns; no empirical evaluation of oversight mechanisms in the paper.
Transparency and auditability for model behavior, provenance, and decisions are essential for trustworthy deployment and regulatory acceptance.
Policy and governance synthesis drawing on regulatory dynamics; no empirical study of regulatory outcomes included.
Rigorous model validation and reproducibility across datasets and settings are necessary constraints for successful AI deployment.
Normative claim in the editorial based on reproducibility concerns in ML and biomedical research; no reported validation trials within the paper.
The paper is primarily discursive and invitational: it opens a dialogue and proposes a research agenda rather than providing definitive empirical answers.
Stated methodological stance and limits: conceptual/philosophical analysis, interdisciplinary literature synthesis, qualitative/illustrative examples, and explicit note of no systematic empirical evaluation.
The paper identifies three core mechanisms underlying calibrated trust and complementarity: (1) calibrated trust balancing reliance and oversight, (2) complementarity–trust interaction for optimal performance, and (3) dynamic feedback loops producing reinforcing learning cycles.
Explicit identification of mechanisms claimed in the paper's synthesis; this is a descriptive claim about the paper's content rather than an empirical finding—no sample or empirical test reported in the abstract.
It remains unclear how developers' general programming and security-specific experience, and the type of AI tool used (free vs. paid), affect the security of the resulting software — motivating this study.
Paper's stated research gap/motivation: the authors identify uncertainty in the literature regarding interactions between developer experience, AI tool tier (free vs. paid), and resulting code security.
Participants were assigned a security-related programming task using either no AI tools, the free version, or the paid version of Gemini.
Experimental design described in the paper: random/conditional assignment of participants into three groups (no AI, free Gemini, paid Gemini) performing the same security-related programming task.
We conducted a quantitative programming study with software developers (n = 159) exploring the impact of Google's AI tool Gemini on code security.
Explicit methodological statement in the paper: a quantitative study with 159 participating software developers assigned to experimental conditions to evaluate Gemini's impact on security-related programming tasks.
The article introduces a novel Bayesian Item Response Theory framework that quantifies human–AI synergy by separately estimating individual ability, collaborative ability, and AI model capability while controlling for task difficulty.
Methodological contribution described in the paper: development and application of a Bayesian Item Response Theory model that includes separate parameters for individual ability, collaborative ability, AI model capability, and task difficulty (method section of the paper).
A quantitative methodology was employed, utilizing a structured questionnaire administered to 400 small business owners.
Explicit methodological statement in the paper: structured questionnaire survey with sample size N=400 small business owners.
This research conducts a critical analysis of the ethical implications of artificial intelligence in terms of job displacement during the fifth industrial revolution.
Author-declared methodology: a literature-based critical analysis drawing on novel studies and the existing body of literature; no further methodological details (e.g., inclusion criteria, databases searched) provided in the excerpt.
This study analyzes comments and statements from party members in OECD countries from 2016 to 2025 through content analysis, examining media interviews, speeches, and debates.
Description of the study's data and method: content analysis of party member comments and statements drawn from media interviews, speeches, and debates across OECD countries over the 2016–2025 period (sample size and selection details not reported in the excerpt).
The study contributes to the literature by integrating evidence across higher education, vocational training, and lifelong learning to emphasize the need for balanced policy approaches to skill formation.
Stated contribution in the paper: cross-pathway synthesis of existing empirical evidence and secondary data (methods described as comparative synthesis; no primary empirical contribution reported in the summary).
The study uses secondary data and comparative evidence from prior empirical studies to analyze relationships between higher education, vocational education, and lifelong learning.
Stated methodology in the paper: analysis of secondary data and synthesis of prior empirical/comparative studies (no primary data collection; no sample sizes reported).
Drawing on leadership theory, emotional intelligence research and AI ethics informs the proposed framework.
Methodological/design statement in the paper describing its intellectual grounding; indicates literature-based synthesis rather than primary data collection.
Chatbot suggestions were artificially varied in aggregate accuracy across treatment conditions from low (53%) to high (100%).
Paper describes experimental manipulation of chatbot suggestion accuracy with aggregate accuracies ranging from 53% to 100%; manipulation method (how suggestions were generated or sampled) described in methods (not fully detailed in excerpt).
Caseworkers in the control condition (no chatbot suggestions) had a mean accuracy of 49%.
Reported experimental outcome: mean accuracy for control group = 49%; based on the randomized experiment using the 770-question benchmark.
We conducted a randomized experiment with caseworkers recruited from nonprofit outreach organizations in Los Angeles.
Paper describes a randomized experiment recruiting caseworkers from nonprofit outreach organizations in Los Angeles; sample size and recruitment details not given in the excerpt.
The benchmark questions have corresponding expert-verified answers.
Paper states benchmark questions have expert-verified answers; verification method and number/credentials of experts not specified in the excerpt.
We created a 770-question multiple-choice benchmark dataset of difficult, but realistic questions that a caseworker might receive.
Paper reports creation of a benchmark dataset containing 770 multiple-choice questions described as difficult and realistic; questions and dataset construction described in methods (no sample-of-questions or external validation details provided in the excerpt).
The study's conclusions draw on three complementary evidence bases: (a) task-level evidence on what generative AI can already do in practice; (b) occupational exposure and complementarity analysis using Philippine labor force data; and (c) firm- and worker-level evidence on AI adoption.
Description of methods and data sources in the paper: task-level capability testing/assessment, analysis of national labor force/occupation data for exposure/complementarity, and firm/worker surveys or qualitative adoption evidence.
The review focuses on AI applications within small‑scale business environments, with a special focus on women‑owned micro firms in Jaipur, India.
Scope and aim articulated in the paper; geographic and demographic focus explicitly stated by the authors.
The systematic review follows PRISMA 2020 guidelines.
Methodological statement in the paper indicating adherence to PRISMA 2020 for the review process.
After screening and eligibility filtering, 55 open‑access journal articles were included for in‑depth analysis.
PRISMA‑guided screening and eligibility process reported in the review; final included sample explicitly stated as 55 open‑access journal articles.
A Scopus search identified 265 records using keywords related to women’s entrepreneurship and AI.
Systematic literature search reported in the paper following PRISMA 2020; search executed in Scopus with specified keywords; initial yield stated as 265 records.
Viable transition pathways are operationally defined in this study as sharing at least 3 skills and achieving at least 50% skill transfer.
Methodological definition stated in the paper used to determine whether a job-to-job transition is considered viable.
We identified 4,534 feasible transitions between jobs in the dataset.
Count of feasible job-to-job transition pairs found in the knowledge graph analysis (4,534 transitions reported).
We constructed and validated a knowledge graph of 9,978 Egyptian job postings, 19,766 skill activities, and 84,346 job-skill relationships with a 0.74% error rate.
Empirical construction and validation of a knowledge graph using a dataset of 9,978 job postings, 19,766 distinct skill/activity nodes, and 84,346 job–skill edges; reported overall error rate 0.74% (validation method not detailed in the excerpt).