@Article{cmc.2026.084792,
AUTHOR = {Diana Maria Popa, Simona-Vasilica Oprea, Adela Bâra},
TITLE = {Generative AI and the Evolution of Skill Requirements in Job Postings across Labor Markets},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27593},
ISSN = {1546-2226},
ABSTRACT = {This paper investigates how generative-artificial intelligence (AI) is influencing job requirements, skill compositions and sectoral dynamics across global labor markets. It examines the evolving frequency and framing of AI-related competencies in job postings, exploring whether generative-AI functions primarily as an augmentative or substitutive component in the workplace. A large-scale, multi-source corpus of over 150,000 English-language job postings (2018–2025) is compiled from twelve open-access datasets and one public API. The analytical framework integrates lexical skill extraction, semantic framing, topic modeling and time-series forecasting. Skill mentions are categorized into five dimensions: AI_Data, Routine, Soft_Meta, Domain_Specific and Leadership, while cross-sectoral analyses and correlation matrices quantify interdependencies between competencies. Sentence-transformer embeddings and cosine similarity are used to compute a Framing Index, distinguishing augmentation- vs. automation-oriented discourse. Results reveal a statistically significant increase in AI-related skill mentions after 2021 (t = −4.70, <i>p</i> = 0.016), alongside a decline in routine-task skills such as data entry and manual coding. Forecasts indicate potential continued growth in AI_Data and Soft_Meta skills through 2025, signaling a structural convergence toward hybrid human-AI expertise as a new foundation of employability. However, the results do not imply direct evidence of workforce transformation beyond what job-posting data can support. Investigating job postings, our research contributes a replicable, data-driven methodology for mapping the diffusion of AI-related skills across industries and time.},
DOI = {10.32604/cmc.2026.084792}
}