Abstract
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.
Date of publication
Spring 5-1-2025
Document Type
Dissertation
Language
english
Persistent identifier
http://hdl.handle.net/10950/4846
Committee members
Kim Nimon, Sangok Yoo, Thomas Reio
Degree
Ph.D. in Human Research Development
Recommended Citation
Legate, Amanda, "ENHANCING RESEARCH AND PRACTICE IN HRD WITH AI-DRIVEN DATA EXTRACTION" (2025). Human Resource Development Theses and Dissertations. Paper 72.
http://hdl.handle.net/10950/4846