AI-Powered Career Path Recommendations for Employee Growth: A Comprehensive Scholarly Review
Dr. Siny Philip Dr. Sreeja K
What the paper says
The rapid advancement of Artificial Intelligence (AI) has significantly transformed human resource management (HRM), particularly in the domain of employee career development. AI-powered career path recommendation systems leverage machine learning, data analytics, and predictive modeling to guide employees toward optimal career trajectories aligned with both individual aspirations and organizational objectives. This article presents a comprehensive scholarly examination of AI-driven career path recommendations for employee growth. It explores theoretical foundations, system architectures, algorithms, benefits, challenges, ethical considerations, and future research directions. Drawing on real-world organizational practices and peer-reviewed literature, the study highlights how AI enhances personalized learning, internal mobility, workforce agility, and talent retention. Tables, conceptual diagrams, and analytical frameworks are included to support academic understanding. The article contributes to the growing body of literature on intelligent HR systems and offers practical implications for researchers, practitioners, and policymakers.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.