Evidence (2954 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 |
Human Ai Collab
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Partial adoption of artificial agents can still improve aggregate outcomes.
Mixed-population analysis and simulation results reported in the paper showing aggregate welfare improvements under partial adoption scenarios.
Unilateral entry of artificial-agent technology is feasible: adopters are not structurally penalized.
Analysis of mixed populations of adopters and non-adopters presented in the paper (mixed-population evolutionary analysis and simulations); exact parameter sweeps and sample sizes are not provided in the abstract.
Artificial agents can shift the learning dynamics to favour coordination outcomes.
Findings from evolutionary dynamics analysis and reinforcement learning experiments demonstrating changes in learning trajectories and equilibrium selection when artificial agents are present.
Introducing artificial agents that use globally observable signals increases coordination among agents.
Experimental results reported in the paper using reinforcement learning experiments and evolutionary-dynamics simulations with artificial agents that observe global signals (details of experimental setup and sample sizes are not specified in the abstract).
Workers who reported clear career pathways, internal mobility, and opportunities to apply newly acquired skills demonstrated higher optimism and stronger retention intentions.
Subgroup analyses within the 5,000-worker survey showing that respondents reporting clear career pathways, internal mobility, and opportunities to apply new skills had higher career optimism scores and greater self-reported retention intentions.
Career optimism is strongly associated with perceptions of AI-related competencies.
Survey measures of respondents' perceptions of their AI-related competencies were analyzed against career optimism scores in the national sample; paper reports a strong association.
Career optimism is strongly associated with financial stability.
Reported associations in the cross-sectional survey linking respondents' financial stability indicators with their career optimism measures (national sample of 5,000 workers).
Career optimism is strongly associated with organizational support for skill development.
Survey analyses correlating measures of perceived organizational support for skill development with respondents' career optimism scores in the 5,000-worker sample.
Career optimism is strongly associated with access to advancement opportunities.
Cross-sectional analyses of the nationally representative survey (5,000 workers) examining organizational factors associated with career optimism; reported strong association between self-reported access to advancement opportunities and measured career optimism.
Generative AI (GenAI) systems have assumed increasingly crucial roles in selection processes, personnel recruitment and analysis of candidates' profiles.
Contextual/introductory claim in the paper; supported by cited literature and domain observation rather than primary data from this study (no sample size required).
Focused, small Skills (2–3 modules) are more effective than comprehensive documentation-style Skills.
Experimental analysis comparing Skill granularity: authors report higher pass-rate gains for Skills composed of 2–3 focused modules versus larger, comprehensive documentation-style Skills within the SkillsBench experiments. (Details on exact sample counts per granularity condition are reported in the paper's Skill-design analyses.)
Adoption of AI in accounting can raise firm-level productivity via faster close cycles, better control, and improved forecasting, potentially affecting profitability and investment decisions.
Theoretical and literature-based claim; the paper suggests mechanisms but does not present a specified empirical estimation in the abstract.
The paper advocates a complementary (augmenting) view of AI in accounting instead of a pure substitution view.
Argumentative conclusion based on synthesis of reviewed studies and theoretical considerations presented in the paper.
AI adoption changes accountants' roles from data entry and routine processing to analysis, interpretation, and strategic decision support.
Inferred from qualitative literature, surveys, and case studies discussed in the paper rather than from a specified empirical identification strategy.
Documented benefits of AI in accounting include increased efficiency, fewer manual errors, faster close cycles, improved report accuracy, and better fraud/irregularity detection.
Reported from literature and industry reports/case examples cited by the paper; the paper does not provide detailed sample sizes or econometric estimates in the abstract.
AI complements accountants rather than substituting them, raising productivity and shifting accountants' focus toward strategic financial management.
Argument based on literature review and qualitative interpretation of workflow changes (surveys/case studies likely); no randomized or quasi-experimental evidence reported in the abstract.
AI technologies (machine learning, robotic process automation, and advanced analytics) are materially improving accounting by automating repetitive tasks, reducing errors, detecting fraud, and providing predictive insights.
Stated as the paper's main finding and supported by cited literature and industry/case examples; the abstract does not specify an empirical design or sample for causal estimation.
Serious-game DSTs can reduce informational frictions by making model outputs (including AI-based recommendations) more interpretable and actionable, lowering barriers to adoption and improving translation of technical advice into economic behavior.
Conceptual synthesis and illustrative practice examples where visualization and interactivity improved understanding; empirical evidence is limited to qualitative user reports and small demonstrations.
Games can act as front-ends to underlying models and datasets or bridge multiple DSTs, improving interoperability and workflow fit for farmers.
Examples of prototypes and deployed tools that connected game interfaces to models/datasets or multiple DSTs; evidence is case-based and demonstrates feasibility rather than large-scale adoption.
Serious games can explicitly model economic outcomes alongside environmental metrics, showing how mitigation/adaptation actions affect enterprise resilience and income.
Prototype demonstrations and case studies that combined economic models with environmental outputs in game interfaces; economic outcome data in these examples are limited and typically short-term or simulated rather than long-term observed incomes.
Dynamic, scenario-based visual outputs in serious games help users understand trade-offs over time (for example, carbon sequestration versus yields).
Comparative demonstrations and workshop observations where scenario visualization was used to communicate temporal trade-offs; evaluation mostly via self-reported comprehension and qualitative feedback from participants.
Interactive, transparent simulations in games reduce skepticism by letting users explore assumptions and model behavior, thereby building trust in DST recommendations.
Qualitative interviews, user testing in workshops, comparative demonstrations where participants explored model assumptions and reported increased confidence; evidence primarily anecdotal and from small pilots.
Co-design through serious games facilitates participatory design with farmers and stakeholders, producing tools that better match on-farm decision contexts and preferences.
Reports from participatory workshops and co-design sessions, case studies of prototype development with farmer groups; evidence largely qualitative (user feedback, design iterations) and based on small-group engagements.
Serious games—interactive, simulation-based decision support tools—can materially increase farmer uptake of land-use decision support tools (DSTs) needed to meet global net zero targets by enabling co-design, building trust, visualizing outcomes, demonstrating profitability–environment links, and integrating with other tools.
Synthesis of literature and practice examples including case studies and deployed game prototypes used with farmer groups, participatory workshops, and qualitative interviews/surveys. Evidence is primarily from small-scale pilots and demonstrations rather than large randomized trials; sample sizes are heterogeneous and often small or not reported.
AI governance for training should require content validation, transparency of model use, data minimisation, human accountability, and auditable logs to prevent hidden biases and exclusion.
Policy recommendation from conceptual risk analysis and best-practice governance principles; no field implementation or audit data provided.
Skills recognition should emphasize functional, employer‑usable verification and portability (e.g., micro‑credentials, QA/transparency instruments), not formal legal harmonisation.
Policy recommendation coming from conceptual analysis and review of transferable instrument layers (drawing from EU tools); no empirical comparison provided.
Mandatory pre-departure training in South–South labour corridors (examined via the Myanmar–Malaysia corridor) is a highly implementable, cross-level lever for improving regularity and rights-protecting mobility in contexts with limited enforcement and coordination capacity.
Conceptual analysis anchored in the Myanmar–Malaysia corridor using a structured desk review of policy/program materials, corridor process mapping, and governance gap analysis. No new causal field experiments or quantitative impact estimates reported.
The paper's qualitative framework can be operationalized for economists into measurable constructs such as task-level time use, output quality metrics, billable hours, client satisfaction, wages, and employment composition.
Authors propose next steps and measurement opportunities; suggestion comes from translating interview-derived categories into empirical variables for future work.
Architectural education should integrate AI tool training and algorithmic thinking to align workforce skills with evolving task demands.
Authors' recommendation grounded in interview evidence that students are adopting algorithmic strategies and in the constructed conceptual framework; presented as pedagogical implication.
Algorithmic thinking strategies—procedural, iterative, and prompt-based reasoning—are central to how students engage with GenAI during co-design.
Inductive thematic analysis of student interviews identified recurring descriptions of procedural/iterative prompting and tool orchestration as core practices.
Integrating lived temporality into design and evaluation is necessary to preserve and enhance the qualitative aspects of human life in transhumanist transformation.
Normative/philosophical argument supported by literature synthesis and conceptual reasoning; no empirical demonstration (N/A).
Ethical and policy considerations require disclosure of synthetic participant use, protection against contamination of human-data pools, and attention to consent and representation issues.
Authors' ethical recommendations based on harms and risks identified across the reviewed studies (contamination, misrepresentation, labor-market effects for participants).
There is a need for standardized benchmarks for economic behaviors (e.g., strategic interaction, intertemporal choice, risk, social preferences) to enable cross-study comparisons and rigorous validation of synthetic participants.
Authors' synthesis and recommendations motivated by heterogeneity in metrics and methods across the reviewed literature.
LLM-generated synthetic participants are a promising low-cost, flexible adjunct for research and data-collection tasks (useful for pilots, prototyping, hypothesis generation, stress-testing, and augmenting small human samples).
Synthesis of reviewed literature identifying applied use-cases and reported benefits across multiple studies; authors' recommendations based on aggregated findings.
Included studies (n=27) reported improvements in learner outcomes mapped to Kirkpatrick‑Barr levels 1–3 (learner reaction/satisfaction; attitudes/perceptions; knowledge/skills; behavior change).
Outcome extraction and mapping reported in the review: evaluations in included studies used learner surveys, knowledge/skill tests, and self-reported behavior-change measures to classify outcomes into Kirkpatrick‑Barr levels 1–3 across the 27 programs.
AI-enabled upskilling and AI-guided procedures weaken the negative effect of workplace stress on employee retention (AI moderates the stress→retention link).
Moderation test in PLS-SEM on N = 350. Reported moderator effect (AI × Stress → Retention): β = 0.078, p < 0.005 (interpreted as a buffering/weakening effect of AI interventions on the stress→retention relationship).
The framework’s emphasis on traceability and IT integration creates rich datasets suitable for econometric evaluation (causal impact on earnings, placement) and for training ML models (curriculum recommendation, skill-gap prediction).
Argument in paper about secondary uses of integrated data (conceptual); no datasets or empirical model training described.
Modelling artefacts (flowcharts/logigrams and algorigrams) can encode repeatable lesson-planning, assessment and audit algorithms.
Paper's modelling artefacts description (conceptual/tools).
Firms and hospitals need differentiated investment and governance strategies by interaction level: integration and workflow redesign for AI-assisted; training and decision-support protocols for AI-augmented; process redesign, liability allocation, and oversight for AI-automated systems.
Prescriptive recommendations derived from cross-case findings (n=4) and the conceptual mapping to innovation management implications.
Different interaction levels produce heterogeneous productivity gains (throughput increases, faster/safer decisions, process cost reductions); economic evaluation should be level-specific.
Theoretical/generalization drawn from observed effects across the four qualitative cases and conceptual analysis linking interaction level to types of productivity gains.
Adoption of healthcare AI is better framed as an evolution toward 'Human+' professionals (complementarity) rather than wholesale replacement of clinicians.
Cross-case interpretive analysis of the four qualitative case studies and theoretical framing with Bolton et al. (2018); presented as the paper's core insight.
AI-automated solutions streamline end-to-end processes (e.g., automated reporting pipelines) while keeping humans in supervisory/exception roles, producing process reconfiguration and efficiency gains and shifting roles toward exception management and governance.
Observed characteristics of the AI-automated case(s) in the qualitative multiple case study (n=4) and synthesized in cross-case comparison.
AI-assisted applications automate highly repetitive tasks (e.g., triage routing, routine image preprocessing), producing increased service availability and throughput while freeing clinician time but requiring oversight and workflow integration.
Empirical observations from one or more of the four qualitative case studies illustrating AI-assisted use-cases; interpreted via the Bolton et al. framework and cross-case comparison.
Policy guidance should target pairing AI diffusion with training, management practices, and organizational reforms to maximize social returns, and evaluations should assess both short-run costs and longer-run productivity trajectories.
Synthesis of evidence that complementarities and contextual factors matter, combined with identified gaps in causal and longitudinal evidence, led to this policy recommendation in the review.
Empirical evidence highlights strong complementarities between AI technologies and human capital (digital skills), organizational practices, and management—models should incorporate these complementarities.
Multiple included studies reported interaction/moderation effects showing higher productivity when AI adoption co-occurs with higher digital skills or supportive management practices; synthesized recommendation follows from findings.
Many digital transformation studies implicate AI and automation as key drivers of observed productivity gains, conditional on complementary factors.
Synthesis of included studies where AI/automation was identified as a contributing technological component correlated with productivity improvements; review notes these effects are conditional on complements like skills and management.
Digital transformation components most consistently tied to productivity gains are technological integration (including automation/AI), process digitization, employee digital skills/training, and analytics/data-driven decision-making.
Synthesis of components extracted from included studies where reported associations between specific digital transformation elements and productivity outcomes were noted across multiple studies.
GenAI models enable personalization (tailored care pathways and risk predictions) by integrating multimodal data (notes, imaging, labs).
Technical capability demonstrated in model development literature and small-scale studies using multimodal inputs; the paper notes limited real-world longitudinal evidence of clinical outcome improvements from such personalization.
GenAI CDS can extend access to expertise in low-resource settings by supporting non-specialists or overburdened clinicians.
The paper cites the potential based on the capability of decision-support systems and early pilot evaluations; empirical real-world evidence and large-scale trials in low-resource settings are limited or not cited.
GenAI CDS can save clinician time (faster charting, literature summarization, guideline retrieval), potentially increasing capacity and access.
Reported process findings from early studies and human-AI interaction evaluations (qualitative and quantitative) and retrospective workflow analyses; specific sample sizes and effect magnitudes are not provided in the paper.