AI-Driven Teaching and Learning in Vocational Education: Current Trends and Future Direetions

Abstract

Numerous studies have examined the role of Artificial Intelligence (AI) in education. However, research focusing on AI implementation in vocational education, particularly in teaching and learning processes, remains limited. This study maps research trends in AI-driven vocational teaching and learning through a bibliometric review, addressing: (1) Publication growth and geographical distribution, (2) Collaborative authorship patterns, (3) Keyword cooccurrence, and (4) Emerging thematic trends. A systematic literature review was condueted using the Scopus database, covering publications from 2001 to 2025. Data were colleeted on March 5, 2025, and analyzed using VOSviewer and Biblioshiny. The findings show that research in this area began in 2001 and grew rapidly, reaching 51 publications by the end of 2024. China dominated both publication output and citation impaet, while Krirk University emerged as the most produetive institution. Collaboration networks were largely driven by researchers from China and Malaysia, with key authors such as Zhang Z, Shen T-C, Chou C-M, Wang D, Li J, and Wu J playing central roles. The most influential keywords included Artificial Intelligence, Vocational Education, Student, and Teaching. Future research opportunities remain open in automation-based learning design, AI-supported learning management systems and instruetional media, and evaluation praetices through automated assessment, feedback generation, and AI-based test item development. These findings highlight AI's strategic role as adaptive pedagogical support in fostering studentcentered and self-regulated learning in vocational education, while AI-driven automation may also reduce teachers' workload. Future studies are encouraged to adopt mixed-method and cross-country approaches to advance AI integration in vocational contexts.