Aligning STEM education with labour market demands through knowledge graphs and neural networks

The educational system in modern society is required to provide a fast reaction to real-life challenges. Also, the growing role of IT skills in labour market requires to provide changes in educational systems related to enhancing the role of IT. This study aims to provide the development of a system that considers real-life requirements of labour market and defines competencies it requires (including IT skills) and provides the possibility to take them into account during the development of the educational programs. In the paper, the system that provides a correspondence between the demand on competencies provided by employers and delivering that request to Methodists that developing educational programs is described. The main actors in the proposed systems are Employers, Job Seekers, and Ministry specialists (Methodists). The proposed concept may be implemented using simple basic tools such as MS Excel and as well by specialized tools such as CIT Polyhedron and Python. A graph-based system using CIT Polyhedron has been developed. In this case, each entity is represented in form of nodes. Proper links have been provided between entities (nodes). Characteristics and descriptions of each specific entity were added in form of semantic and numeric nodes’ metadata. An example of CIT Polyhedron’s specific tools (rank) usage is described. The integration of graph neural networks and knowledge graphs demonstrates potential for dynamic curriculum adaptation, with recent implementations achieving up to 94% accuracy in aligning educational outcomes with workforce requirements. This approach addresses the projected 22% growth in STEM occupations while tackling persistent underrepresentation challenges in STEM fields.