ENHANCING RESEARCH AND PRACTICE IN HRD WITH AI-DRIVEN DATA EXTRACTION

This multi-paper dissertation investigates the use of artificial intelligence (AI) to enhance data extraction from academic literature in Human Resource Development (HRD). A pre-registered living systematic review (LSR) mapped emerging tools and gaps in AI-supported extraction beyond clinical research. Findings informed a Delphi study engaging global HRD experts to identify context-specific challenges, ethical concerns, and priorities for responsible AI adoption. Drawing on insights across studies, this work introduces a conceptual model grounded in the Job Demands–Resources (JD-R) framework. The model emphasizes that AI benefits are not automatic but depend on researcher competencies, institutional conditions, and governance infrastructure. To encourage empirical progress, detailed variable-level guidance is provided to support testing and refinement of the model across research contexts. By combining evidence synthesis, expert input, and theoretical development, this dissertation offers a foundation for building adaptive, AI-supported workflows that advance both the rigor and translational impact of HRD research.