An AI-Based Personalized Learning Framework for Corporate Employee Development: An Integrative Literature Synthesis - Dzaky Mubarok; Khoe Yao Tung; Budi Wibawanta (Jurnal Ekuisci, 2026)
Background: Accelerating digital transformation, encompassing widespread business process automation and the adoption of artificial intelligence, has widened the competency gap between current workforce capabilities and future organizational demands. This condition positions the corporate Learning and Development (L&D) function as a strategic pillar for sustaining competitive advantage and simultaneously heightens the urgency of integrating AI into corporate learning systems. Purpose: Aims. This study synthesized, through an integrative approach, empirical and conceptual literature on AI-based personalized learning frameworks for corporate employee development to produce a coherent conceptual framework. Method: A Systematic Literature Review (SLR) design was employed, utilizing qualitative meta-synthesis guided by the PRISMA 2020 protocol. Research questions were formulated using the SPIDER framework. Systematic searches were conducted across five major academic databases, Scopus, Web of Science, ERIC, IEEE Xplore, and Google Scholar, covering publications from 2020 to 2025. Results: From 1,847 initially identified articles, 1,203 unique records remained after deduplication. Title and abstract screening yielded 312 articles, and full-text screening produced a final synthesis corpus of 47 articles. Findings confirm that AI-driven personalized learning systems have a significant capacity to address workforce competency gaps arising from digital transformation. Conclusion: This study produced a comprehensive AI-based personalized learning framework by integrating perspectives from educational technology, human resource management, and artificial intelligence. Implementation. Organizations are advised to adopt this framework as a strategic response to the imperatives of reskilling and upskilling in the digital transformation era.