A design science research methodology was adopted for this thesis. The process began with a comprehensive literature review to investigate current trends in workforce reskilling, skill assessment models, and recommendation systems. Multiple existing models were analyzed and compared based on the key features, methods and technologies they offered. Based on these findings, essential components were selected to guide the design of the proposed model. The conceptual system was structured into multiple layers, including an interface layer, a data layer, and a recommendation logic layer. These components were designed to work together to collect and analyze employee skill data, compare it with the skill requirements of target roles, and suggest appropriate courses. Expert interviews were conducted with HR professionals and technical and business experts in knowledge graphs, taxonomies and NLP to validate and refine the initial model. Feedback from these interviews informed the iteration of the initial version of the model.

The final outcome is a conceptual proposal that aims to demonstrate how employees in a company can identify the skills required for internal job roles and receive personalized suggestions for relevant upskilling.