A Systematic Review of Competency-Based Recruitment Frameworks: Integrating Micro-Credentialing, Skill Taxonomies, and AI-Driven Talent Matching

The increasing shift toward skills-based hiring in dynamic labor markets has underscored the relevance of competency-based recruitment frameworks. This review systematically explores the integration of micro-credentialing, skill taxonomies, and artificial intelligence (AI)-driven talent matching in modern recruitment ecosystems. Drawing on interdisciplinary insights, the study evaluates how micro-credentials offer verifiable evidence of competencies and how structured skill taxonomies enable precise role-person alignment. The paper further analyzes how AI-powered tools optimize candidate-job fit through predictive analytics, natural language processing, and machine learning algorithms. Emphasis is placed on the standardization of assessment protocols, digital portfolios, and adaptive recruitment strategies that prioritize capabilities over traditional qualifications. Findings indicate that the confluence of digital micro-credentials, AI-enhanced screening, and robust skills frameworks provides scalable solutions to address inefficiencies in traditional hiring processes. The review concludes by identifying research gaps and recommending actionable frameworks for education institutions, employers, and platform developers to collaboratively support a skills-first future of work.