Issah Baako
Abstract The integration of Artificial Intelligence (AI) in Technical and Vocational Education and Training (TVET) has gained significant traction over the past decade. AI-driven technologies are transforming skills acquisition, workforce development, and educational methodologies. Despite this increasing interest, there remains a limited comprehensive bibliometric analysis mapping the research landscape. This study aims to analyze trends, scholarly contributions, and emerging themes in AI applications within TVET, providing a structured understanding of its evolution and identifying research gaps. A bibliometric approach was utilized to analyze AI-related research in TVET from 2015 to 2024. Data was sourced from Dimensions.ai, ensuring coverage of peer-reviewed journal articles and conference proceedings. The study employed the bibliometric tool VOSviewer for citation network analysis, keyword co-occurrence mapping, and authorship collaboration patterns. Descriptive statistics and trend analysis were applied to assess publication growth, influential authors, institutions, and countries. The findings indicate a steady increase in AI-TVET research, with a notable surge post-2019 due to advancements in adaptive learning systems, machine learning applications, and intelligent tutoring systems. Keyword analysis revealed dominant themes, including personalized learning, automation in vocational training, and AI-driven competency assessments. However, gaps remain in research addressing AI ethics, accessibility, and effectiveness in skill-based education. This study highlights the transformative role of AI in TVET, emphasizing the need for interdisciplinary collaboration and policy alignment. Future research should explore AI's long-term impact on vocational skills and employability. The insights from this bibliometric analysis serve as a foundation for guiding policymakers, educators, and researchers toward more strategic AI integration in TVET.