Evidence (4175 claims)
Adoption
8570 claims
Productivity
7631 claims
Governance
6869 claims
Human-AI Collaboration
6491 claims
Org Design
4175 claims
Innovation
4114 claims
Labor Markets
3566 claims
Skills & Training
2966 claims
Inequality
2066 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 758 | 199 | 100 | 900 | 2007 |
| Governance & Regulation | 826 | 400 | 191 | 122 | 1563 |
| Organizational Efficiency | 777 | 193 | 124 | 84 | 1189 |
| Technology Adoption Rate | 635 | 233 | 124 | 97 | 1098 |
| Research Productivity | 422 | 128 | 57 | 336 | 954 |
| Output Quality | 476 | 179 | 59 | 47 | 761 |
| Decision Quality | 328 | 177 | 81 | 47 | 640 |
| Firm Productivity | 435 | 57 | 88 | 20 | 606 |
| AI Safety & Ethics | 218 | 277 | 65 | 33 | 599 |
| Market Structure | 180 | 170 | 123 | 24 | 502 |
| Task Allocation | 213 | 64 | 72 | 33 | 387 |
| Skill Acquisition | 170 | 61 | 61 | 17 | 309 |
| Innovation Output | 203 | 27 | 43 | 18 | 292 |
| Employment Level | 105 | 54 | 107 | 13 | 281 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 117 | 63 | 42 | 11 | 233 |
| Firm Revenue | 153 | 48 | 26 | 3 | 230 |
| Task Completion Time | 173 | 31 | 8 | 12 | 225 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Worker Satisfaction | 89 | 65 | 22 | 12 | 188 |
| Error Rate | 69 | 92 | 10 | 2 | 173 |
| Regulatory Compliance | 77 | 69 | 14 | 5 | 165 |
| Automation Exposure | 56 | 56 | 26 | 13 | 154 |
| Training Effectiveness | 94 | 21 | 13 | 19 | 149 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Team Performance | 86 | 17 | 27 | 10 | 141 |
| Developer Productivity | 95 | 17 | 14 | 6 | 133 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 52 | 7 | 8 | 3 | 70 |
| Creative Output | 31 | 18 | 8 | 3 | 61 |
| Skill Obsolescence | 5 | 46 | 6 | 1 | 58 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 19 | 17 | — | 53 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Org Design
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AI significantly enhances supply chain resilience (SCR) in manufacturing firms.
Empirical analysis of A-share listed manufacturing companies (2011–2023) using a multi-period difference-in-differences (DID) model; authors report the finding and state it remains after robustness checks.
From synthesis of results, we suggest three practices that focus on preserving agency in software engineering for coding, learning, and mentorship, especially as AI grows increasingly autonomous.
Authors' prescriptive recommendations derived from the paper's qualitative synthesis; presented as proposed practices rather than empirically tested interventions.
Seniors leverage pre-AI foundational instincts to steer modern tools and possess valuable perspectives for mentoring juniors in their early AI-encouraged career development.
Qualitative accounts from senior participants in the Delphi/ACTA process and blind reviews showing seniors reference pre-AI practices and see mentoring value.
Juniors enter as AI‑natives, seniors adapted mid‑career.
Authors' synthesis from a three-phase mixed-methods study: ACTA combined with a Delphi process (5 seniors), an AI-assisted debugging task (10 juniors), and blind reviews of junior prompt histories by 5 additional seniors.
Technology-driven recruitment has emerged as a strategic imperative for organizations seeking competitive advantage in talent acquisition.
Argumentative/interpretive claim in the paper's introduction and discussion, supported by survey findings (N=150) indicating perceived strategic importance.
The paper proposes the Technology-Enabled Recruitment Optimization Framework (TEROF), a structured implementation model designed to guide organizations through the phased adoption of recruitment technology.
Paper synthesizes its empirical findings into a named framework (TEROF) described in the discussion/conclusions; based on combined survey (N=150) and case-study analysis (4 organizations).
Video interview platforms improved recruiter productivity by 41%.
Reported quantitative finding from the study's survey (N=150) and corroborating case study observations.
AI-powered resume screening reduced initial shortlisting time by 64%.
Reported quantitative result in the paper derived from the survey of HR professionals (N=150) and illustrated in case studies.
Integrated technology-driven recruitment produced a 52% reduction in cost-per-hire relative to traditional methods.
Reported quantitative finding from the study's survey (N=150) and supporting case studies (4 organizations).
Adoption of integrated recruitment technology yielded a 45% improvement in candidate quality as measured by first-year performance ratings.
Reported quantitative result from the survey (N=150) and case study evidence using first-year performance ratings as the quality metric.
Organizations adopting integrated technology-driven recruitment platforms experienced an average reduction in time-to-hire of 38%.
Reported quantitative finding based on the paper's mixed-methods analysis (survey of 150 HR professionals and corroborating qualitative case studies of 4 organizations).
Strategic adoption of AI can significantly improve project outcomes and operational performance in the construction industry.
Synthesis of case study findings indicating improved scheduling, risk management, resource allocation, reduced delays and costs, and improved productivity; support is based on the analysed cases rather than a large-scale representative sample.
Artificial Neural Networks (ANN) and predictive modelling support data-driven decision-making in construction.
Paper highlights the use of ANN and predictive modelling in case studies and their role in supporting data-driven decision-making; the summary does not provide quantitative performance metrics for these models.
Quantitative results demonstrate notable improvements in productivity and time efficiency across the analysed cases.
Summary reports quantitative analyses across the case studies showing improvements in productivity and time efficiency; no explicit sample size, statistical significance values, or effect magnitudes provided in the summary.
These enhancements lead to measurable reductions in project delays, operational costs, and safety risks.
Authors state quantitative measurements from analysed cases indicate reductions in delays, costs, and safety risks attributable to AI-driven tools. The summary does not provide numeric magnitudes or sample counts.
AI-driven tools enhance project scheduling, risk management, and resource allocation.
Reported findings across multiple case studies (qualitative and quantitative analyses) where AI applications were applied to scheduling, risk management, and resource allocation tasks. Specific number of cases or statistical tests not provided in the summary.
By framing disclosure as epistemic infrastructure, this work outlines a conceptual roadmap for future empirical and design research on Human–AI collaboration.
High-level, forward-looking claim about the paper's contribution to research agenda (conceptual argument). No empirical validation in the abstract.
We contribute a research instrument that operationalizes these configurations in a collaborative chat setting and articulate testable design conjectures.
Paper contribution: a research instrument and set of conjectures described by the authors (design/methodological artifact). The abstract does not report empirical deployment or sample size.
We introduce an AI Disclosure Design Space that conceptualizes disclosure as an epistemic coordination mechanism.
Paper contribution: conceptual artifact (design space) introduced by the authors; this is a descriptive/foundational claim about the paper's contents.
What matters in practice is the design of disclosure: how systems reveal, signal, or conceal AI assistance within collaboration.
Central theoretical argument of the paper (conceptual/design claim); no empirical validation reported in the abstract.
The approach provides a practical path toward more transparent, controllable, and accountable AI use without requiring new model architectures.
Authors' asserted benefit of the proposed interaction-layer framework; no empirical demonstration that transparency, control, or accountability are achieved or that no architectural changes are required in practice.
The framework enables auditable reasoning traces and supports alignment with emerging governance standards, including the EU AI Act and ISO/IEC 42001.
Stated compliance/alignment claim linking the proposed interaction-layer approach to existing regulatory standards; no compliance testing or audit examples reported.
This reframes the question from whether the model can think to whether the human-AI system can reason.
Conceptual reframing stated in the paper; no empirical evidence required as it is a change of perspective.
We introduce 'The Architect's Pen' as a practical method where the human uses the model as an external medium for structured reflection by embedding phases of articulation, critique, and revision into human-AI interaction.
Method description / practical proposal included in the paper; no experimental evaluation, user study, or quantitative validation reported.
This perspective emphasizes collaborative intelligence, combining human judgment and contextual understanding with machine speed, memory, and associative capacity.
Theoretical claim about complementary strengths of humans and models within the proposed framework; presented without empirical tests.
Building on recent work on 'System-2' learning, reflective reasoning can be relocated to the interaction layer and framed as a cognitive protocol that can be structured, measured, and governed using existing systems.
Conceptual extension of prior literature ('System-2' learning) into an interaction-layer protocol; no empirical protocol testing or measurement evidence provided.
Reasoning should be treated as a relational process distributed between human and model rather than an internal capability of either.
Methodological proposal / theoretical framing presented by the authors; no empirical validation reported.
Large language models have advanced rapidly, from pattern recognition to emerging forms of reasoning.
Stated as an observational claim in the paper's introduction; no empirical evaluation or dataset provided.
This approach aligns with emerging compliance expectations, including the EU AI Act and ISO/IEC 42001, by making reasoning processes traceable under real conditions of use.
Claim of regulatory alignment made by the authors; presented as interpretive/legal/standards-relevant argument rather than supported by empirical analysis or legal review data in this excerpt.
Stabilising interaction makes uncertainty and drift visible before enforcement is applied, enabling more precise capability governance.
Normative/operational claim in the paper about the anticipated effect of the proposed interventions; no empirical test or measurement reported in this excerpt.
Together, these layers form a missing operational substrate for governance by increasing signal-to-noise at the point of use.
Argumentative claim from the paper proposing that the combined interventions improve the information available at the decision point; no empirical validation or sample size provided here.
This paper is the first in a five-paper research series on stabilising human-AI reasoning that proposes a two-layer approach: Parts II–IV introduce human-side mechanisms (uncertainty cues, conflict surfacing, auditable reasoning traces) and Part V develops a model-side Epistemic Control Loop (ECL) that detects instability and modulates generation.
Descriptive claim about the structure and scope of the paper series as stated by the authors; internal to the publication (no external dataset).
Large language models are increasingly integrated into decision-making in areas such as healthcare, law, finance, engineering, and government.
Statement in paper describing observed/adoptive trend; no empirical dataset, sample size, or quantitative analysis reported in the text.
These cooperation mechanisms become more effective under evolutionary pressures to maximize individual payoffs.
Authors report results from experiments or simulations applying evolutionary-pressure dynamics (selection for payoff-maximizing agents) and observing increased effectiveness of mechanisms; no numeric results or sample sizes in excerpt.
Contracting and mediation are most effective in achieving cooperative outcomes between capable LLM models.
Empirical results from the authors' experiments across four social dilemmas comparing mechanism performance; specifics (which models, quantitative cooperation rates) are not included in the excerpt.
Continuous learning and diversity of ideas are essential if AI is to play a meaningful role in original scientific discovery.
Normative/conditional claim supported by conceptual reasoning in the article; no empirical evidence or measured sample provided.
AI is likely to fundamentally reshape scientific publication.
Author's argument and discussion of implications for publishing and evaluation; no reported empirical study.
There is a gradual path from AI as a research tool to AI as a scientific collaborator.
Narrative/theoretical progression outlined in the article; conceptual roadmap rather than empirical demonstration.
AI for Science is especially important because it may transform not only the efficiency of research, but also the structure of scientific collaboration, discovery, publishing, and evaluation.
Argumentative/theoretical analysis in the article; forward-looking claim without reported empirical data or experimental sample.
The most important significance of the AI revolution, especially the rise of large language models, lies not simply in automation, but in a fundamental change in how complex information and human know-how are carried, replicated, and shared.
Conceptual argument presented in the article (theoretical/essayistic reasoning); no empirical sample or quantitative study reported.
A hybrid AI-human sprint planning framework should assign algorithmic tools to estimation and backlog formatting while mandating human deliberation for risk assessment and ambiguity resolution.
Theoretical framework proposed by the authors, motivated by the experimental findings (trade-offs observed between efficiency and risk capture/rework) and qualitative analysis.
Human-only planning excels at adaptability.
Controlled experiment comparing human-only, AI-only, and hybrid models with qualitative indicators of planning robustness and adaptability showing superior adaptability for human-only planning.
AI-only planning minimizes time and cost.
Controlled, three-condition experiment (AI-only, human-only, hybrid) conducted on a live client deliverable at a mid-sized digital agency; quantitative metrics included time and cost measures (reported alongside estimation accuracy, rework rates, and scope change recovery time).
CoCoGen+ outperforms baselines in efficiency.
Comparative experiments reported in the paper showing CoCoGen+ versus baseline methods on efficiency metrics; the abstract does not report numeric effect sizes or sample sizes.
Experiments on varying learning tasks validate the feasibility of CoCoGen+.
Simulation/experimental evaluation on multiple learning tasks reported in the paper; abstract does not state dataset sizes, number of tasks, or other experimental details.
To promote long-term collaboration, CoCoGen+ integrates a payoff-redistribution-based incentive mechanism to compensate organizations for their contributions and competition-caused utility degradation.
Mechanism design described in the paper (proposed incentive mechanism); presented theoretically and incorporated into experiments.
We provide a tractable equilibrium characterization of the game and derive implementable synthetic-data generation strategies that maximize social welfare.
Analytical equilibrium characterization and derived strategies reported in the methods/analysis sections of the paper; theoretical derivations rather than randomized trial data.
We introduce CoCoGen+, a coopetition-compatible data generation and incentivization framework that jointly models non-IID data and inter-organizational competition while endogenizing GenAI-based synthetic data generation as a strategic decision.
Design and formal description of the CoCoGen+ framework within the paper (theoretical contribution); no sample size applicable.
Finance applications of AI strengthen risk assessment and process efficiency.
Abstract statement summarizing literature findings across reviewed finance-related studies.
Logistics applications of AI improve forecasting and supply chain resilience.
Reported thematic finding in the abstract based on synthesis of the included studies.