AI-enabled learning systems for employee training and skill development in organizations

Abstract

The accelerating pace of digital transformation has compelled organizations to fundamentally reconceptualize their approaches to employee training and workforce development. Artificial intelligence (AI)-enabled learning systems have emerged as a pivotal intervention, offering unprecedented capacity for personalization, real-time feedback, and data-driven skill gap remediation. This article presents a systematic review of empirical and conceptual literature published between 2021 and 2025, indexed in Scopus and Web of Science, examining the applications, outcomes, and challenges associated with AI-driven corporate learning. A structured database search and thematic synthesis identified 24 primary studies for inclusion, supplemented by theoretical literature drawn from Human Capital Theory, Experiential Learning Theory, the Technology Acceptance Model, and Organizational Learning Theory. Key findings indicate that AI-enabled Learning Management Systems (LMS), adaptive training platforms, and intelligent tutoring systems significantly enhance knowledge acquisition speed, learner engagement, and measurable competency development. However, the review also surfaces persistent challenges, including algorithmic bias, data privacy vulnerabilities, employee resistance, and a critical scarcity of longitudinal evaluation studies. A conceptual framework linking AI technologies to learning personalization, employee skill development, and organizational performance is proposed. The article contributes to the growing interdisciplinary conversation at the intersection of human resource development (HRD) and artificial intelligence, offering actionable implications for HR professionals, organizational strategists, and future researchers.

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