Bibliometric and Systematic Review of AI-Assisted Adaptive Learning Applications in Vocational Education (2018-2023) - Harleni Harleni; M. Giatman; Nurhasan Syah; Ganefri Ganefri; Nizwardi Jalinus; Ridwan Ridwan; Krismadinata Krismadinata (STAI Hubbulwathan Duri, 2025)
The integration of Artificial Intelligence (AI) into adaptive learning systems has gained traction in vocational education due to its potential to personalize instruction and enhance competency-based learning. However, research on this intersection remains fragmented, particularly in the context of vocational education and training (TVET). This study conducts a Systematic Literature Review (SLR) using the PRISMA protocol, combined with bibliometric analysis using VOSviewer, to map research trends on AI-assisted adaptive learning in vocational education from 2018 to 2023. Data were sourced from Scopus, Semantic Scholar, and Google Scholar, resulting in 41 eligible articles. The findings reveal a sharp increase in publications after 2020, reflecting growing interest in AI-driven innovations, particularly during the COVID-19 pandemic. Bibliometric mapping identified three dominant thematic clusters: AI-enabled personalization, competency-based vocational education, and pedagogical innovation. Geographically, most research originates from technologically advanced countries such as the United States, India, and the United Kingdom. The study highlights the strategic role of AI-assisted adaptive learning in supporting individualized pathways and skills alignment in vocational education. It also identifies gaps in longitudinal evaluation, pedagogical integration, and research representation from developing regions. These insights provide practical implications for policymakers, educators, and curriculum developers aiming to modernize vocational training systems through AI.