Abstract
The accelerating pace of technological disruption has fundamentally reshaped the competency requirements of the modern workforce, compelling organisations to priorities employee upskilling and reskilling as strategic imperatives. Artificial Intelligence (AI)-powered learning platforms have emerged as transformative tools in this endeavour, offering unprecedented levels of personalisation, scalability, and analytical depth that conventional Learning Management Systems (LMS) cannot replicate. This systematic review synthesizes empirical and theoretical literature published between 2018 and 2026, drawn from Scopus, Web of Science, IEEE Xplore, and Springer databases, encompassing 47 peer-reviewed studies selected through a PRISMA-aligned methodology. The review examines the conceptual architecture of AI-powered learning platforms, including Machine Learning (ML), Natural Language Processing (NLP), learning analytics, and adaptive learning systems. Applications across organizational contexts are critically analyzed, including personalized learning pathways, automated skill gap analysis, real-time feedback mechanisms, and the transformation of corporate training. The review further evaluates documented benefits such as improved workforce productivity, enhanced employee engagement, and augmented organizational agility, while critically interrogating challenges related to algorithmic bias, data privacy, the digital divide, and barriers to institutional implementation. A comparative analysis of traditional LMS and AI-driven platforms is presented, followed by an examination of emerging trends, including generative AI tutors, AI copilots, and continuous learning ecosystems. The findings reveal that while AI-powered platforms demonstrate significant efficacy in accelerating competency acquisition, critical research gaps persist regarding long-term learning transfer, equitable access, and ethical governance frameworks. Future research directions and practical organizational implications are discussed.
