Optimizing Vocational Human Resource Development through AI and Adaptive Learning Integration: A Systematic Literature Review (2019-2025) - Chusna, Chufadatul; Hadi, Syamsul; Sugandi, Machmud (FKIP Universitas Wisnuwardhana, 2026)
This study aims to analyze the integration of Artificial Intelligence (AI) and adaptive learning in vocational education and its implications for vocational human resource development. A Systematic Literature Review (SLR) approach was conducted following PRISMA guidelines, covering peer-reviewed articles published between 2019 and 2025 from Scopus, DOAJ, SpringerLink, and Google Scholar. A total of 10 selected studies were analyzed using thematic synthesis to identify patterns, models, and research gaps. The findings indicate that AI-driven adaptive learning enhances competency development through data-driven personalization, learning analytics, predictive mechanisms, and immersive technologies. A cross-study synthesis reveals that AI improves learning efficiency, skill relevance, and lifelong learning capacity; however, it also exposes a critical imbalance between technical skill development and the limited integration of soft skills. Furthermore, the relationship between adaptive learning systems and workforce outcomes is non-linear and influenced by pedagogical design, institutional readiness, and industry alignment. This study proposes an integrated AI–adaptive learning framework that connects technological, pedagogical, and workforce perspectives, contributing to more adaptive, human-centered, and industry-relevant vocational education systems.