Veränderungen der Arbeitswelt durch Künstliche Intelligenz
Anwendungsmöglichkeiten und Auswirkungen des Einsatzes künstlicher Intelligenz auf den Arbeitsmarkt werden breit diskutiert. Welche Folgen für Beschäftigung, Löhne und Qualifikationsanforderungen sind zu erwarten? Wie verändern sich Berufe und Tätigkeiten? Birgt die Nutzung automatisierter Entscheidungssysteme (z.B. für die Personalauswahl) ein Diskriminierungsrisiko?
Dieses Themendossier stellt Literatur zum Stand der internationalen Forschung zusammen.
Im Themendossier Digitale Arbeitswelt – Chancen und Herausforderungen für Beschäftigte und Arbeitsmarkt finden Sie weitere Informationen zum digitalen Wandel der Arbeitswelt mit speziellem Fokus auf Deutschland.
Im Filter „Autorenschaft“ können Sie auf IAB-(Mit-)Autorenschaft eingrenzen.
-
Literaturhinweis
Algorithm Aversion in Prosocial Tasks: Evidence from AI-Based Performance Evaluation (2026)
Zitatform
Abel, Martin, Raghad Dawi, Tyler Lenk & Aidan Singer (2026): Algorithm Aversion in Prosocial Tasks: Evidence from AI-Based Performance Evaluation. (IZA discussion paper / IZA Network @ LISER 18678), Bonn: IZA Network @ LISER, 41 S.
Abstract
"How do workers respond when artificial intelligence replaces human judgment in evaluating prosocial work? Partnering with a non-profit addressing food insecurity, we recruit 1,491 U.S. volunteers to write fundraising messages and cross-randomize evaluation by humans versus AI and the presence of performance pay. AI evaluation reduces effort by 11–14 percent among volunteers with low commitment to the cause, while having no effect on those strongly aligned with the mission. Performance pay fails to mitigate these adverse effects. Workers perceive AI as less effective at identifying quality, which appears to be the primary mechanism, and as less fair and transparent than human evaluation. Introducing an AI algorithm that explicitly applies human evaluation criteria does not mitigate these negative effects, suggesting that resistance to AI evaluation reflects deeper skepticism about machines' capacity for subjective judgment." (Author's abstract, IAB-Doku) ((en))
-
Literaturhinweis
Building Pro-Worker Artificial Intelligence (2026)
Zitatform
Acemoglu, Daron, David Autor & Simon Johnson (2026): Building Pro-Worker Artificial Intelligence. (NBER working paper / National Bureau of Economic Research 34854), Cambridge, Mass, 48 S.
Abstract
"This paper defines pro-worker technologies, including Artificial Intelligence, as technologies that make human skills and expertise more valuable by expanding worker capabilities. Our conceptual framework distinguishes among five categories of technological change: labor-augmenting, capital-augmenting, automating, expertise-leveling, and new task-creating. Only the last category is unambiguously pro-worker, generating demand for novel human expertise rather than commodifying it. We illustrate these distinctions through hypothetical and real-world examples spanning aviation maintenance, electrical services, custodial work, education, patent examination, and gig delivery. While AI's capacity to automate work is substantial, we argue that its potential to serve as a collaborator, by extending human judgment, enabling new tasks, and accelerating skill acquisition, is equally transformative and currently underexploited. We identify market failures, including misaligned firm and developer incentives, path dependence, and a pervasive pro-automation ideology, that may lead to underinvestment in pro-worker AI. We consider nine policy directions that would change incentives, including targeted investments in health care and education, tax code reform, antitrust enforcement, and intellectual property protections for worker expertise." (Author's abstract, IAB-Doku) ((en))
-
Literaturhinweis
Who uses Generative AI? Patterns and inequalities across the EU: Employment and labour markets (2026)
Zitatform
Adăscăliței, Dragoș (2026): Who uses Generative AI? Patterns and inequalities across the EU. Employment and labour markets. (Eurofound working paper), Dublin, 19 S.
Abstract
"This paper describes generative AI use patterns across EU27 Member States in 2025, analysing crossnational variation and socio-demographic inequalities based on Eurostat aggregate data. Overall use of generative AI reaches 32.7% at the EU27 level, ranging from 17.8% in Romania to 48.4% in Denmark. Country patterns do not follow clear geographic clustering, with high and low adopters distributed across all European regions. Private use systematically exceeds professional use, whilst educational use remains concentrated among young populations. Educational attainment emerges as a strong predictor of AI use, with high-educated individuals using generative AI at more than double the rate of low-educated individuals. Age is also a strong predictor, with 63.8% of those aged 16-24 having used generative AI in the past three months compared to just 6.5% of those aged 65 and above. The gender gaps in AI use are moderate but widen with education. In terms of broad occupational groups, the analysis demonstrates that uptake is heavily skewed towards ICT professions. Labour force status also matters decisively, with students (72.0%) far exceeding employed (36.4%), unemployed (28.3%), and retired/inactive populations (12.9%) in technology usage. These patterns reveal stratified diffusion of generative AI use with implications for labour market inequalities across the EU." (Author's abstract, IAB-Doku) ((en))
-
Literaturhinweis
Automation, Learning, and Career Dynamics (2026)
Zitatform
Afrouzi, Hassan, Andres Blanco, Andrés Drenik & Erik Hurst (2026): Automation, Learning, and Career Dynamics. (NBER working paper / National Bureau of Economic Research 35157), Cambridge, Mass, 58 S.
Abstract
"We study how an automating technology affects career dynamics, human capital, and welfare in an economy where workers acquire skill through the tasks they perform. In a continuous-time general equilibrium model, learning-by-doing is determined jointly with the share of tasks automated, the frontier of tasks managers maintain, and the worker-to-manager career transition. Economies with high learning capacity admit pairs of stationary equilibria strictly ranked by the aggregate learning rate. Cheaper technology has opposite effects across the two: in the high-learning equilibrium, it raises welfare through the learning channel itself; in the low-learning equilibrium, it tips the economy into a human-capital trap. The planner's first-best combines a tax on automation profits with a subsidy on frontier-maintenance expenditures at a common rate." (Author's abstract, IAB-Doku) ((en))
-
Literaturhinweis
Enhancing Worker Productivity Without Automating Tasks: A Different Approach to AI and the Task-Based Model (2026)
Zitatform
Agrawal, Ajay K., John McHale & Alexander Oettl (2026): Enhancing Worker Productivity Without Automating Tasks: A Different Approach to AI and the Task-Based Model. (NBER working paper / National Bureau of Economic Research 34781), Cambridge, Mass, 44 S.
Abstract
"The task-based approach has become the dominant framework for studying the labor-market effects of artificial intelligence (AI), typically emphasizing the replacement of human workers by machines. Motivated by growing empirical evidence that contemporary AI is more often used as a tool that augments workers, this paper develops two related task-based models in which AI enhances worker productivity without automating tasks. Abstracting from capital, we develop a pair of related task-based models that examine how technological progress in AI that provides new tools to augment workers affects aggregate productivity and wage inequality. Both models emphasize the role of human capital in intermediating the effects of AI-related technological shocks. In the first model, AI use requires specialized expertise, and technological progress expands the set of tasks for which such expertise is effective. We show that a larger supply of AI expertise amplifies the productivity gains from improvements in AI technology while attenuating its adverse effects on wage inequality. The second model focuses on non-AI skills, allowing AI tools to alter the set of tasks that workers can perform given their skills. In equilibrium, workers allocate across tasks in response to wages, generating an endogenous distribution of skills across the task space. A central result is that aggregate productivity and wage inequality depend on different global properties of this equilibrium distribution: productivity is particularly sensitive to thinly staffed tasks that create bottlenecks, while wage inequality is driven by the concentration of workers in a narrow set of tasks. As a result, improvements in AI tools can induce non-monotonic co-movement between productivity and inequality. By linking these mechanisms to multidimensional human capital---including AI expertise and higher-order non-AI skills---the paper highlights the role of education and training policies in shaping the economic consequences of AI-driven technological change." (Author's abstract, IAB-Doku) ((en))
-
Literaturhinweis
Generative Artificial Intelligence and Gender: A Systematic Scoping Review from a Sociotechnical Perspective (2026)
Zitatform
Aguilar, Antonio (2026): Generative Artificial Intelligence and Gender: A Systematic Scoping Review from a Sociotechnical Perspective. (SocArXiv papers), 50 S. DOI:10.31235/osf.io/a9y2c_v1
Abstract
"Generative artificial intelligence has been rapidly incorporated into educational,professional, communicative, healthcare, and institutional contexts, creatingopportunities but also new risks in terms of gender equality. This study presents asystematic scoping review of generative artificial intelligence and gender from asociotechnical perspective. Following a PRISMA-based procedure, 1,372 bibliographicrecords were identified and, after the screening and eligibility process, 108 studiespublished between 2022 and 2026 were included. The results were organized into sixdimensions: visual and representational biases; language, ChatGPT, and genderperformativity; digital violence, deepfakes, and psychosocial harm; gaps in use, trust,anxiety, and well-being; education and sociocritical literacy; and governance, health, andwork. The discussion shows that these dimensions are articulated through cross-cuttingtensions: visibility versus equality, technical neutrality versus sociocultural reproduction,automated efficiency versus evaluative bias, technological innovation versuspsychosocial harm, access versus critical appropriation, individual anxiety versusstructural vulnerability, and instrumental use versus sociocritical literacy. The reviewshows that generative AI does not operate as a neutral tool, but rather as a sociotechnicalinfrastructure capable of reproducing, amplifying, or reconfiguring inequalities throughimages, texts, interactions, automated decisions, and emerging forms of epistemicauthority. The article concludes that the analysis of generative AI and gender requires theintegration of technical, social, psychosocial, educational, and normative dimensions, aswell as responses based on critical literacy, human oversight, gender-sensitive regulation,and intersectional approaches." (Author's abstract, IAB-Doku) ((en))
-
Literaturhinweis
Do AI Expectations Reduce Unemployment in the United States? Evidence from an AI Attention Index (2026)
Akyildirim, Erdinc; Gozgor, Giray; Bekci, Suzan;Zitatform
Akyildirim, Erdinc, Suzan Bekci & Giray Gozgor (2026): Do AI Expectations Reduce Unemployment in the United States? Evidence from an AI Attention Index. (CESifo working paper 12653), München, 13 S.
Abstract
"This paper constructs an AI Attention Index from LexisNexis news coverage and embeds it within an augmented Phillips curve framework. It then examines the relationship between AI attention and unemployment in the United States using monthly data from January 2000 to December 2025. We find that greater AI attention is associated with lower unemployment. Nonlinear estimates reveal a U-shaped relationship, indicating diminishing marginal effects within the observed data range. The relationship weakens after the onset of COVID-19, with both linear and nonlinear effects reduced. These findings indicate that labour market effects of AI-related expectations are sensitive to macroeconomic regime shifts." (Author's abstract, IAB-Doku) ((en))
-
Literaturhinweis
The heterogeneous effects of artificial intelligence on labor markets: A calibrated simulation of skills, tasks, and wages (2026)
Zitatform
Al khatib, Abdullah Mohammad Ghazi & Bayan Mohamad Alshaib (2026): The heterogeneous effects of artificial intelligence on labor markets: A calibrated simulation of skills, tasks, and wages. In: International Review of Economics and Finance, Jg. 108. DOI:10.1016/j.iref.2026.105219
Abstract
"Artificial Intelligence (AI) is reconfiguring labor markets with profound, heterogeneous effects on skills and wages. While existing models offer qualitative insights, they often lack the quantitative rigor for testable predictions. This paper develops a novel simulation model extending the standard task-based framework from a one-dimensional continuum to a two-dimensional space (cognitive complexity and codifiability) with a dynamic AI capability frontier. Calibrated to match empirical stylized facts, such as the high-skill wage premium, our model generates three key findings. First, AI's advance consistently widens the wage gap between high- and low-skill workers. Second, the rate of inequality growth slows when AI becomes a stronger substitute for human labor, as broad displacement compresses wage growth across the skill spectrum. Third, counterfactual policy simulations reveal a strict efficiency-equity trade-off: interventions managing AI's adoption pace (e.g., an "AI tax") effectively mitigate inequality but dampen aggregate output, while training subsidies and labor supply expansions prove less effective against structural automation. Our work provides a methodologically transparent tool for analyzing the future of work." (Author's abstract, IAB-Doku, © 2026 The Authors. Published by Elsevier Inc.) ((en))
-
Literaturhinweis
AI adoption, productivity and employment: Evidence from European firms (2026)
Aldasoro, Iñaki; Gambacorta, Leonardo; Pal, Rozalia; Wolski, Marcin; Weiß, Christoph; Revoltella, Debora;Zitatform
Aldasoro, Iñaki, Leonardo Gambacorta, Rozalia Pal, Debora Revoltella, Christoph Weiß & Marcin Wolski (2026): AI adoption, productivity and employment: Evidence from European firms. (Economics - working papers / European Investment Bank 2026/02), Luxembourg, 32 S. DOI:10.2867/1772538
Abstract
"This paper provides new evidence on how the adoption of artificial intelligence (AI) affects productivity and employment in Europe. Using matched EIBIS-ORBIS data on more than 12,000 non-financial firms in the European Union (EU) and United States (US), we instrument the adoption of AI by EU firms by assigning the adoption rates of US peers to isolate exogenous technological exposure. Our results show that AI adoption increases the level of labor productivity by 4%. Productivity gains are due to capital deepening, as we find no adverse effects on firm-level employment. This suggests that AI increases worker output rather than replacing labor in the short run, though longer-term effects remain uncertain. However, productivity benefits of AI adoption are unevenly distributed and concentrate in medium and large firms. Moreover, AI-adopting firms are more innovative and their workers earn higher wages. Our analysis also highlights the critical role of complementary investments in software and data or workforce training to fully unlock the productivity gains of AI adoption." (Author's abstract, IAB-Doku) ((en))
Ähnliche Treffer
auch erschienen als: BIS Working Papers, 1325 -
Literaturhinweis
Avoiding unemployment in a post-growth economy (2026)
Aldred, Jonathan;Zitatform
Aldred, Jonathan (2026): Avoiding unemployment in a post-growth economy. In: Ecological economics, Jg. 244. DOI:10.1016/j.ecolecon.2026.108974
Abstract
"There is increasing concern about rising unemployment in early industrialised countries, arising from low economic growth or the emergence of new labour-displacing technologies, notably AI. Post-growth economics has explored a number of strategies to prevent unemployment, such as working time reductions. This paper contributes to this discussion, focusing on two strategies. The first strategy involves a shift in the composition of output towards labour-intensive services. It is argued that Baumol's cost disease may be less problematic for this strategy than is commonly believed. The second strategy involves re-orienting production towards more labour-intensive processes (taking the composition of output as fixed). This strategy has received less attention, partly because it appears to involve rising costs of production. Drawing on insights from post-Keynesian and institutional economics, this paper argues that under some circumstances more labour-intensive production processes can be adopted without increasing unit costs." (Author's abstract, IAB-Doku, © 2026 The Author. Published by Elsevier B.V.) ((en))
-
Literaturhinweis
The effect of AI on labour demand: A critical assessment of 'Power and Progress' by Acemoglu and Johnson (2026)
Aldred, Jonathan;Zitatform
Aldred, Jonathan (2026): The effect of AI on labour demand: A critical assessment of 'Power and Progress' by Acemoglu and Johnson. In: Structural Change and Economic Dynamics, Jg. 78, S. 188-196. DOI:10.1016/j.strueco.2026.03.008
Abstract
"Task-based models of production have led to a theoretical reappraisal of the effect of new technology on labour demand. This paper critically assesses the policy implications of this research agenda, with particular reference to Acemoglu and Johnson’s recent book, Power and Progress. While Acemoglu and Johnson take welcome steps away from previous orthodoxy, their analysis has several flaws which affect the policy lessons to be drawn, including: (i) the explanation for anti-labour bias is unclear; (ii) worker-friendly technologies are not clearly characterised in theory, and hard to identify in practice; (iii) macroeconomic policy orthodoxy is largely unquestioned. More generally, much of Power and Progress remains unhelpfully constrained by theoretical commitments to mainstream economics." (Author's abstract, IAB-Doku, © 2026 The Author. Published by Elsevier B.V.) ((en))
-
Literaturhinweis
Task-Specific Technical Change and Comparative Advantage (2026)
Zitatform
Althoff, Lukas & Hugo Reichardt (2026): Task-Specific Technical Change and Comparative Advantage. (CESifo working paper 12403), München, 85 S.
Abstract
"Artificial intelligence is changing which tasks workers do and how they do them. Predicting its labor market consequences requires understanding how technical change affects workers' productivity across tasks, how workers adapt by changing occupations and acquiring new skills, and how wages adjust in general equilibrium. We introduce a dynamic task-based model in which workers accumulate multidimensional skills that shape their comparative advantage and, in turn, their occupational choices. We then develop an estimation strategy that recovers (i) the mapping from skills to task-specific productivity, (ii) the law of motion for skill accumulation, and (iii) the determinants of occupational choice. We use the quantified model to study generative AI's impact via augmentation, automation, and a third and new channel — simplification — which captures how technologies change the skills needed to perform tasks. Our key finding is that AI substantially reduces wage inequality while raising average wages by 21 percent. AI's equalizing effect is fully driven by simplification, enabling workers across skill levels to compete for the same jobs. We show that the model's predictions line up with recent labor market data." (Author's abstract, IAB-Doku) ((en))
-
Literaturhinweis
Generative AI and the Changing Demand for Skills (2026)
Andreescu, Marie-Renée; Maurin, Éric;Zitatform
Andreescu, Marie-Renée & Éric Maurin (2026): Generative AI and the Changing Demand for Skills. (Paris-Jourdan Science Economiques. Working paper 2026-27), Paris, 48 S.
Abstract
"Using data from the French Public Employment Service linked to social security records, we examine the shifts in the supply of and demand for information technology (IT) skills that followed the recent emergence of generative artificial intelligence (GenAI). We show that the large-scale deployment of GenAI tools between late 2022 and late 2023 was followed by a sharp decline in vacancies for IT-specialist jobs and, more broadly, for jobs requiring IT skills. While relative wages remained largely unchanged, jobs requiring IT skills also experienced a substantial increase in applicants per vacancy and a marked decline in hiring. These findings are consistent with a model in which GenAI and natural-language coding boost the productivity not only of expert programmers but also of ordinary software users, particularly the least skilled. Tensions in the labor market are all the more acute because, as the required skill levels decline, various IT-exposed jobs become accessible to a broader pool of candidates." (Author's abstract, IAB-Doku) ((en))
-
Literaturhinweis
A potential boost from AI in ageing societies: Early insights (2026)
Zitatform
André, Christophe & Matthias Schief (2026): A potential boost from AI in ageing societies. Early insights. (OECD Economics Department working papers 1870), Paris, 49 S. DOI:10.1787/be002e40-en
Abstract
"Demographic headwinds are set to weaken economic growth in OECD countries over the coming decades. At the same time, artificial intelligence (AI) provides opportunities for productivity gains, potentially alleviating labour shortages and boosting economic growth. However, little is known about how exposure to AI varies over the life cycle and what this may imply for AI deployment in ageing societies. This paper shows, using OECD Programme for the International Assessment of Adult Competencies (PIAAC) data, that workers’ overall exposure to AI (automation and augmentation) exhibits an inverted U-shaped pattern across age groups, albeit less pronounced when controlling for education, occupation and country. Exposure to automation is higher in younger age groups and declines rapidly with age, as experience tends to complement AI. Nevertheless, as a general-purpose technology, AI is bound to be disruptive. Reaping its benefits will require labour market reallocation, reskilling and upskilling, and business dynamism and innovation, which may all be weaker in ageing societies." (Author's abstract, IAB-Doku) ((en))
-
Literaturhinweis
Digital Workplace Technologies and the Inclusion of Disabled Workers: A Realist Review and Synthesis of Evidence and Workers' Perspectives (2026)
Zitatform
Ansari, Bahareh, Qurat Ul Ain & Juliet Hassard (2026): Digital Workplace Technologies and the Inclusion of Disabled Workers: A Realist Review and Synthesis of Evidence and Workers' Perspectives. In: New Technology, Work and Employment, S. 1-14. DOI:10.1111/ntwe.70042
Abstract
"While digital technologies have transformed modern workplaces, research examining their impact on disabled workers' inclusion has focused on assistive technologies, overlooking the underlying social structures and organisational practices. This realist review, informed by focus group insights from disabled workers, examines mainstream workplace technologies and the contexts in which they enable or constrain inclusion. From an initial pool of 317 records, 29 studies were analysed, focusing on working-age adults with disabilities or neurodiversity and worker-related outcomes. Focus group participants validated identified patterns and highlighted critical contextual factors. Using Context-Intervention-Mechanism-Outcome (CIMO) configurations, the analysis examined four contextual domains: technological design, individual circumstances organisational practices and legal and policy environment. The synthesis reveals that technologies produce divergent outcomes depending on how contexts activate mechanisms at psychosocial-, organisational- and societal-levels, demonstrating both enabling and exclusionary patterns. The findings offer insights for organisations seeking to leverage digital technologies while ensuring equitable and inclusive employment opportunities." (Author's abstract, IAB-Doku) ((en))
-
Literaturhinweis
The end of work feels near. How do people perceive the impact of digital technologies and automation? (2026)
Zitatform
Arntz, Melanie, Sebastian Blesse & Philipp Doerrenberg (2026): The end of work feels near. How do people perceive the impact of digital technologies and automation? In: Labour Economics, Jg. 102, 2026-05-11. DOI:10.1016/j.labeco.2026.102897
Abstract
"Anxieties about technological change in the context of the labor market are a recurring historical phenomenon. Using customized survey data collected in 2019 in the US and Germany, prior to the recent wave of generative AI applications, we study how respondents perceive the impact of the digital (automation) technologies available at the time of the survey on the labor market. We document that a majority views digital technologies and automation as a major threat to overall employment and as a cause of rising inequality, while a quarter is concerned about their own labor market prospects. Providing scientific information on the likely labor market implications of digital technologies in a randomized experiment reduces these concerns. Yet, treatment responses depend on prior beliefs about the future of work, resulting in heterogeneous and opposing treatment effects on policy demand." (Author's abstract, IAB-Doku, © 2026 Elsevier) ((en))
-
Literaturhinweis
KI als Jobkiller für Berufseinsteiger? Entwarnung mit Vorbehalt (2026)
Zitatform
Arntz, Melanie, Michele Dinelli & Oliver Schlenker (2026): KI als Jobkiller für Berufseinsteiger? Entwarnung mit Vorbehalt. In: IAB-Forum H. 13.10.2026. DOI:10.48720.IAB.FOO.20261013.01
Abstract
"Derzeit wird vielfach befürchtet, dass Künstliche Intelligenz den Einstieg in Beschäftigung erschweren oder gar zu einem Kahlschlag am Arbeitsmarkt führen könne. Neue Beschäftigtendaten für Deutschland zeichnen hier ein weniger dramatisches Bild: Ein allgemeiner Beschäftigungseinbruch in KI-exponierten Berufen bleibt aus. Unterm Strich haben sich diese sogar günstiger entwickelt als die weniger exponierten Berufe. Bei jungen Beschäftigten zeigt sich indes ein im Vergleich zum negativen Effekt der schwachen Konjunktur zwar geringer, aber jedoch zunehmend negativer Zusammenhang." (Autorenreferat, IAB-Doku)
-
Literaturhinweis
Fachliche Expertise plus KI wird der Schlüssel zur Karriere: Einleitung zum Karriereguide - Die Welt (2026)
Zitatform
Arntz, Melanie (2026): Fachliche Expertise plus KI wird der Schlüssel zur Karriere. Einleitung zum Karriereguide - Die Welt. In: Die Welt H. 221.
Abstract
"Prof. Dr. Melanie Arntz vom Institut für Arbeitsmarkt- und Berufsforschung erklärt, warum fachliches Wissen kombiniert mit KI-Kompetenzen zur Schlüsselqualifikation wird." (Autorenreferat, IAB-Doku)
-
Literaturhinweis
Artificial intelligence exposure and occupational wages: Evidence from the United States (2026)
Zitatform
Atalay, Ozan (2026): Artificial intelligence exposure and occupational wages: Evidence from the United States. In: Journal of Economic Studies, S. 1-15. DOI:10.1108/jes-03-2026-0281
Abstract
"Purpose: This paper investigates the relationship between occupational exposure to artificial intelligence (AI) and wage structures in the United States. While much of the literature focuses on the displacement effects of AI, less attention has been given to its implications for wage outcomes across occupations. Design/methodology/approach: The study uses occupation-level data for 671 occupations, combining wage information with an AI exposure index that captures the extent to which occupations are affected by AI technologies. Cross-sectional regression models with robust standard errors are employed, controlling for employment size and occupational characteristics. Quantile regression is also used to examine variation across the wage distribution. Findings: The results indicate a positive and statistically significant association between AI exposure and wages. Occupations with higher exposure tend to have higher wage levels. This pattern is consistent across model specifications and across the wage distribution. The findings are broadly consistent when using an instrumental variable approach. The association is stronger in occupations with higher cognitive skill intensity. Research limitations/implications: This study is based on occupation-level data rather than individual-level observations, which limits the ability to capture within-occupation wage heterogeneity. In addition, the AI exposure index reflects potential exposure rather than actual adoption at the firm level. Future research could extend this analysis using firm-level or longitudinal data. Practical implications: The findings suggest that occupations with higher exposure to artificial intelligence tend to exhibit higher wages, highlighting the importance of skill upgrading and targeted workforce policies. Policymakers and organizations should focus on enhancing digital skills and supporting workforce transition to maximize the benefits of AI. Social implications: The results indicate that artificial intelligence may contribute to wage differences across occupations by enhancing productivity in certain roles. This highlights the need to ensure equal access to skills and training opportunities so that the benefits of AI are distributed more evenly across the labor market. Originality/value: This study contributes by providing occupation-level evidence on the relationship between AI exposure and wages, shifting attention from employment effects to wage structures. It also highlights that this relationship is partly explained by occupational skill composition, while a residual association remains." (Author's abstract, IAB-Doku, © Emerald Group) ((en))
-
Literaturhinweis
Engineering Education at the Intersection of Knowledge, Work, and Power: Generative AI and Epistemic Authority (2026)
Aydeniz, Mehmet;Zitatform
Aydeniz, Mehmet (2026): Engineering Education at the Intersection of Knowledge, Work, and Power: Generative AI and Epistemic Authority. In: Sociology Compass, Jg. 20, H. 8. DOI:10.1111/soc4.70230
Abstract
"Generative artificial intelligence (GenAI) is rapidly reshaping engineering education, yet prevailing debates frame its impact narrowly as either pedagogical innovation or epistemic threat. In this article, we argue that such framings misdiagnose the problem by treating GenAI as a tool rather than as a sociotechnical reorganization of epistemic authority, agency, and professional formation. Drawing on sociological theory, philosophy of science, labor economics, and recent empirical studies, we show that GenAI lowers epistemic floors by expanding access to competent performance while leaving epistemic ceilings, those associated with judgment, authority, and problem framing, largely intact. This asymmetry produces new forms of stratification, redistributes responsibility without redistributing control, and stabilizes plausibility-based forms of knowledge in place of warrant-based engineering reasoning. We advance three central contributions. First, we conceptualize a shift from warrant to plausibility in engineering knowledge, in which fluent, algorithmically generated outputs increasingly circulate as legitimate despite bypassing core practices of verification, critique, and accountability. Second, we extend the concept of the AI wall beyond productivity to describe a ceiling of epistemic development, where further learning and judgment are not institutionally rewarded without deeper expertise, authority, or complementary organizational resources. Third, we theorize how engineering education participates in stabilizing these dynamics through what we term the social campus, the institutional space in which curricular norms, assessment regimes, professional signaling, and peer comparison converge to normalize AI-mediated epistemic authority. Together, these dynamics position engineering education at a critical juncture. We argue for a reorientation toward epistemic stewardship, in which engineering programs deliberately cultivate the capacities needed to interrogate, govern, and responsibly deploy AI systems rather than merely operate within them. The question we want to highlight is not whether engineering education will adapt to AI, but whether it will do so as a steward of epistemic responsibility or as an accelerator of epistemic stratification." (Author's abstract, IAB-Doku) ((en))