This study explores the role of Generative Artificial Intelligence (GenAI) in skill development across education, training and creative sectors. This study aims to understand how GenAI supports the acquisition of key competencies such as critical thinking, creativity, problem-solving and career readiness and to assess research trends and thematic patterns in this emerging field.
This research uses a combination of bibliometric analysis and systematic literature review using 79 peer-reviewed articles retrieved from the Scopus database. The bibliometric analysis identifies publication trends, leading contributors and collaboration networks, while thematic content analysis uncovers key domains where GenAI is applied in skill development.
This study identifies 11 major thematic areas in which GenAI is influencing skill acquisition, including STEM education, communication, creative arts and personalized learning. This paper reveals growing interdisciplinary interest and highlights the potential of GenAI to transform learning. However, this study also uncovers critical gaps such as ethical concerns, limited application in underrepresented contexts and the need for longitudinal evidence on skill retention.
The review is limited to articles indexed in the Scopus database and published in English, which may exclude relevant contributions from other databases or languages. Additionally, the fast-evolving nature of GenAI may mean recent developments are not fully captured within the current data set.
This study provides evidence-based insights for educators, policymakers and training professionals seeking to integrate GenAI into skill development initiatives. This study emphasizes the importance of ethical deployment, equitable access and culturally sensitive applications to ensure effective and inclusive learning outcomes.
To the best of the authors’ knowledge, this study is among the first to use a systematic literature review combined with bibliometric analysis to examine how GenAI promotes skill acquisition across educational and professional contexts. Most previous research has focused narrowly on domain-specific applications or outcomes. By synthesizing findings from diverse contexts, this study highlights cross-disciplinary trends and identifies emerging areas for research. This comprehensive perspective can better inform educators, researchers and policymakers in developing equitable, forward-looking and effective AI-supported learning practices.
