Evidence (368 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
10085 claims
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Productivity
8974 claims
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Governance
8062 claims
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Human-AI Collaboration
7749 claims
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Org Design
5057 claims
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Innovation
4896 claims
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Labor Markets
4088 claims
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Skills & Training
3372 claims
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Inequality
2377 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 | 882 | 244 | 117 | 1097 | 2424 |
| Governance & Regulation | 1010 | 469 | 229 | 135 | 1875 |
| Organizational Efficiency | 977 | 235 | 149 | 90 | 1462 |
| Technology Adoption Rate | 781 | 299 | 143 | 128 | 1362 |
| Research Productivity | 506 | 155 | 74 | 363 | 1110 |
| Output Quality | 555 | 219 | 71 | 70 | 915 |
| Decision Quality | 395 | 200 | 95 | 54 | 751 |
| Firm Productivity | 523 | 67 | 101 | 27 | 724 |
| AI Safety & Ethics | 262 | 309 | 75 | 36 | 688 |
| Market Structure | 195 | 201 | 135 | 30 | 566 |
| Task Allocation | 248 | 77 | 96 | 38 | 464 |
| Innovation Output | 300 | 34 | 55 | 20 | 411 |
| Skill Acquisition | 207 | 75 | 65 | 21 | 368 |
| Employment Level | 138 | 67 | 119 | 24 | 350 |
| Fiscal & Macroeconomic | 156 | 80 | 53 | 33 | 329 |
| Task Completion Time | 211 | 38 | 13 | 16 | 280 |
| Firm Revenue | 183 | 52 | 29 | 5 | 270 |
| Consumer Welfare | 131 | 77 | 48 | 13 | 269 |
| Inequality Measures | 50 | 141 | 54 | 9 | 254 |
| Worker Satisfaction | 104 | 85 | 25 | 13 | 227 |
| Error Rate | 87 | 112 | 11 | 5 | 215 |
| Automation Exposure | 69 | 69 | 37 | 20 | 198 |
| Wages & Compensation | 102 | 49 | 31 | 11 | 193 |
| Team Performance | 115 | 30 | 30 | 11 | 187 |
| Regulatory Compliance | 88 | 74 | 17 | 7 | 186 |
| Training Effectiveness | 109 | 22 | 14 | 21 | 168 |
| Developer Productivity | 116 | 21 | 15 | 8 | 161 |
| Job Displacement | 12 | 92 | 26 | 1 | 131 |
| Hiring & Recruitment | 57 | 12 | 9 | 5 | 83 |
| Skill Obsolescence | 6 | 59 | 10 | 2 | 77 |
| Social Protection | 43 | 17 | 8 | 2 | 70 |
| Creative Output | 35 | 21 | 9 | 4 | 70 |
| Labor Share of Income | 18 | 23 | 17 | 1 | 59 |
| Worker Turnover | 15 | 16 | — | 4 | 35 |
| Industry | — | — | — | 1 | 1 |
The study reveals an 'AI Competency Paradox'—AI raises technical skills while increasing demand for meta-competencies that established frameworks fail to assess.
Synthesis of empirical findings reported in the paper linking measured increases in technical skills with unmet assessment needs for meta-competencies.
Some skills generalize broadly across tasks and models, whereas others become specialized to role-specific workflows and lose effectiveness under transfer.
Analyses reported in the paper showing heterogeneous transfer behavior across the 22 procedural skills in the AFTER benchmark, with some skills showing broad cross-task and cross-model generalization and others showing role-specific specialization and reduced transfer performance.
Policy-related AI development, rather than national AI development alone, may be more relevant for observed adult participation in education and training.
Comparative interpretation of the (null) contemporaneous association for total AI Vibrancy Score and the positive lagged association for AI-related Policy and Government activity in the panel regressions (2017–2024, 18 European countries).
Generative AI (GenAI) is reshaping higher education by changing learning practices, academic writing, knowledge access, assessment preparation, research support, and student engagement.
Narrative literature review / synthesis (review article). No primary empirical sample reported — claim drawn from cited literature and conceptual synthesis.
The prominence of machine learning, Internet of Things (IoT), and cybersecurity varies depending on organisational context and role requirements within the wind sector.
Paper reports variation across data sources and organisational contexts based on interviews, surveys, and job-posting patterns; no subgroup sample sizes or statistical tests reported in summary.
The same practice input carries opposite signs depending on whether the environment screens for it.
Synthesis of empirical patterns: in unscreened CF environment AI-style practice predicts smaller rating gains (for non-affiliated users) while in screened ICPC environment it predicts higher non-AI-aided scores.
In open Codeforces contests a stronger AI-style signature predicts smaller rating gains for users with no ICPC/IOI affiliation, but not for those who qualified for the AI-prohibited contests.
Comparative empirical analysis of CF contest rating gains by users' affiliation (ICPC/IOI qualification status) and individual AI-style signature strength; methods likely regression/heterogeneity analysis—sample sizes not reported in abstract.
Collective practices that emerge in response (from shared prompt strategies to jailbreaking techniques) represent vernacular knowledge formations that, while often exhibiting magical thinking, contain resources for 'revolutionary prompting' and the transformation of individual prompt anxiety into collective political critique.
Qualitative/interpretive claim based on observed user practices and collective responses to LLM behaviour; no systematic survey or sample sizes reported in the abstract.
Overall, STARA technologies can both enhance skill development, thriving and career opportunities and concurrently produce identity threats, pressures, and contextual complexities that shape long-term career trajectories—requiring integrated organisational and labour-market perspectives to design supportive approaches.
Editorial synthesis and summary of contributions in the special issue; draws on multiple cited empirical and conceptual studies included in the issue and prior literature.
AI usage at work can simultaneously enhance employees' thriving and induce identity threat; employees’ learning and performance goal orientations drive career growth in this context (Yuan et al., 2026, in this special issue).
Reported empirical finding from a paper in the special issue (Yuan et al., 2026) cited in the editorial.
While new high-skill roles emerge from AI adoption, their limited accessibility constrains workforce transition.
Literature synthesis indicating emergence of high-skill roles alongside barriers to access (skills, education, hiring practices) reported in reviewed studies.
Key mechanisms of AI's impact on employment structure were identified: automation of routine processes, formation of new professional profiles, and changes in requirements for employees' competencies.
Qualitative analysis of statistical data, industry reviews, and regulatory legal documents described in the paper (no experimental or survey sample size reported).
AI is changing informal cultural practices like professional mentoring that are key to helping professionals settle in their positions, stay engaged with their work, and grow their careers.
Participant reports from the 24 interviews indicating changes to informal practices such as mentoring, onboarding, and informal feedback.
High-information AI improves short-run (immediate) performance without reducing post-AI outcomes on average in the experiments, but effects are heterogeneous across participants.
Experimental condition with high-information AI in the controlled logical reasoning task showing improved short-run performance and no average reduction in post-AI outcomes; heterogeneity in effects reported (sample size not provided in abstract).
Metis can be subdivided into 'constitutive metis' (knowledge destroyed by the act of formalization) and 'operational metis' (system-specific familiarity that automation can progressively absorb).
Conceptual taxonomy proposed by the authors; definitions and distinctions are theoretical and illustrated via argumentation and prior literature rather than quantified empirical measurement.
Accounting for heterogeneity in AI literacy (agents' ability to identify and adapt to inaccurate AI outputs) can produce skill polarization in the long-run steady state.
Analytical/theoretical steady-state distribution analysis of agent skill dynamics with heterogeneous AI literacy parameters; paper reports conditions under which polarization emerges (theoretical, no empirical sample).
Program outcomes are moderated by a person's prior occupational skill set, their area of work, and features of the local economy.
Heterogeneity analyses across subgroups defined by prior occupational skill composition, industry/area of work, and local labor-market conditions in the WIOA administrative data (2017-2023) show variation in outcomes.
Generative AI-powered tools like ChatGPT are reshaping market skill demands while also offering new forms of on-demand learning support to meet those demands.
Framed in paper as background/motivation; asserted from prior literature and the paper's motivating claims rather than reported as a quantified result in this study.
The architect's role is shifting, but the human remains central.
Authors' discussion and interpretive analysis about the role of humans in agentic AI-driven design processes.
AI is rapidly transforming the nature of work, the demand for skills, and the professional roles of Information Technology (IT) practitioners.
Stated as a synthesis result from a narrative review of recent empirical studies, conference findings, and industry reports (review paper).
Outcomes of AI deployment in labor-market settings depend on complementary organizational practices, workers’ access to skills, and the regulatory environment.
Synthesis-derived moderator/ mechanism claim from qualitative analysis of the 19 included studies identifying organizational practices, skill access, and regulation as contextual moderators.
This work establishes a foundation for understanding how generative AI systems not only augment cognitive performance but also reshape self-perception and perceived expertise.
Paper's stated contribution presenting theory and conceptual groundwork; no empirical validation provided in the abstract.
Result 3: When AI productivity depends less on worker expertise, workers can permanently diverge in skill: experienced workers realize their full potential while less experienced workers deskill to zero.
Analytical result from the dynamic model showing path-dependent divergence in skill levels under particular parameterizations (lower dependence of AI on worker expertise).
Participants were clustered into three profiles -- resilient, overcontrolled, and undercontrolled -- based on the Big-Five personality traits and ARC typology.
Paper reports clustering analysis on participants using Big-Five trait measures and ARC typology; clustering result described as three profiles. Total sample reported as N=267.
A useful working model is 'AI-mediated metacognitive decoupling': LLM use widens the gap among produced output, underlying understanding, calibration accuracy, and self-assessed ability.
Conceptual synthesis and theoretical proposal grounded in reviewed empirical findings from multiple literatures (human–AI interaction, learning research, model evaluation); presented as the paper's working model rather than as a single empirical estimate.
Implementation of human-replacing technologies leads to significant transformations in skill demand: it reduces reliance on low-skilled labour while increasing demand for qualified engineers, system operators and specialists in digital technologies.
Sector-specific analysis and review of international labour-market studies cited in the article documenting skill-biased effects of automation and digitalization; qualitative assessment for Ukraine's mining and metallurgical sector under workforce shortage conditions.
The framework implies threshold effects in training and capability acquisition: when the teaching horizon lies below the prerequisite depth of the target, additional instruction cannot produce successful completion of teaching; once that depth is reached, completion becomes feasible.
Model-derived threshold result described in the abstract (mathematical analysis of prerequisite depth vs. teaching horizon).
The value of information depends on whether downstream users can absorb and act on it: a signal conveys meaning only to a learner with the structural capacity to decode it (an explanation that clarifies a concept for one user may be indistinguishable from noise to another who lacks the relevant prerequisites).
Conceptual argument motivating the model; theoretical reasoning described in the paper's intro/abstract.
The net educational value of AI-generated feedback depends on alignment with pedagogical goals, quality evaluation, integration with human teaching, and governance to manage equity, privacy, and incentives.
Synthesis statement from the meeting report produced by 50 interdisciplinary scholars; conceptual judgment rather than empirical proof.
Technical use falls steeply with age.
Survey responses from the June 2026 sample (n=1,999) reporting use of AI for technical tasks by respondent age; authors summarize an age gradient (no numerical slope provided in excerpt).
Workforce skill gaps constrain AI adoption and implementation in organizations.
Identified repeatedly across the reviewed literature and industry reports in the narrative review (no quantified sample size provided).
Educational and digital-skill divides (limited access to computer-science curricula, low mobile-internet penetration in rural areas, inconsistent electricity) constrain upward mobility into AI-complementary occupations.
Paper synthesizes cross-country infrastructure and education access evidence from cited organizations; no numeric access rates included in the text.
There are critical gaps in data literacy among SME personnel.
Reported as a recurring theme in the reviewed literature (2020–2025) in the narrative review; no numeric prevalence or sample sizes provided in the excerpt.
An unguarded AI helper left high-school students about 17% worse on an unaided exam than peers with no tool at all.
Described as the 'strongest causal evidence' in the paper; empirical study of high-school students measuring unaided exam performance. (Study design details and sample size not provided in the excerpt.)
Used poorly, AI replaces the cognitive work that learning requires and leaves an illusion of learning: a confident sense of mastery that collapses on the unaided task.
Authors' conceptual claim supported in the paper by reference to causal evidence (see following empirical claims); no sample size given in the excerpt.
Lagged AI-related R&D activity is negatively associated with subsequent participation in education and training (one-year lag).
Lagged (one-year) structural panel models (two-way fixed effects) on 18 European countries, 2017–2024. One-year lag coefficients reported as −1.2310 (ages 18-74), −0.9392 (ages 45-54), and −0.8911 (ages 50-74).
Average lifelong-learning participation declines with age: 20.09% among adults aged 18-74, 14.82% among those aged 45-54, and 9.34% among those aged 50-74.
Descriptive statistics computed from the study panel of 18 European countries (2017–2024).
Automation AI is associated with a greater likelihood of not pursuing postgraduate studies and with higher rates of field-switching after graduation.
Chapter 3: individual-level analyses of post-graduation decisions (postgraduate enrollment and field-switching) using U.S. data 2010–2022 and IV with lagged CS research intensity.
In those European countries, demand for Social skills declines in AI-exposed occupations.
Chapter 2: same 75 million job postings dataset, multilingual skill extraction, and IV approach with lagged CS research intensity to identify effects on skill demand between 2018–2023.
Only 14% of organizational leaders report proficiency in designing effective human-machine interactions.
Reported descriptive statistic from the same Deloitte 2026 Global Human Capital Trends survey of over 3,000 business leaders across 15 countries.
Africa faces an operational deficit across three axes — skilled people, compute and electrical power, and investment — each measured against current figures.
Paper presents capacity measurements/figures on human capital, compute/electrical infrastructure, and investment to characterize deficits (cross-sectional capacity assessment).
Africa cannot yet operate frontier models.
Paper claims operational incapacity, measured against current figures for skills, compute/power, and investment (descriptive / capacity assessment).
GenAI raises concerns including passive dependence, weakened critical thinking, uncertain authorship, academic integrity breaches, algorithmic bias, unequal access, and employability gaps.
Synthesis of concerns reported in prior studies and discussions in the literature (review article); no new empirical data provided.
Learning specialized simulator input languages can cost domain scientists hours to days.
Stated motivating claim in the paper (no experimental sample size or formal measurement reported in abstract).
Interviews provide expanded analysis on existing skill gaps and lifelong learning needs among wind-energy professionals.
Qualitative interview data are reported to highlight skill gaps and lifelong learning needs; specific counts of interviewees not provided in the summary.
The informal post-degree apprenticeship system that historically completed graduate formation no longer reliably exists.
Claim based on the paper's systematic review of eighteen peer-reviewed studies and current labor-market analyses (as described in the abstract).
GenAI usage significantly decreased domain-relevant skills.
Experiment with 82 participants reported in the paper; authors report a statistically significant reduction in measures of domain-relevant skills for the GenAI condition.
AI adoption presents challenges related to skill gaps.
Thematic findings from peer-reviewed literature and secondary data (qualitative review). No sample size reported.
Simultaneously, there is a structural shortage of qualified personnel and a gap between the education system and the needs of the economy in Uzbekistan.
Synthesis of statistical data, industry reviews, and regulatory/legal document analysis presented in the paper (no primary survey/sample size reported).
Other changes are more nuanced and put the typical career growth opportunities, like receiving feedback from professional networks and promoting leadership and mentorship, at risk.
Qualitative reports from interview participants (n=24) expressing concerns that AI-driven changes may reduce feedback, leadership development, and mentoring opportunities.