Design and Development of the Graphology-based Career Analysis and Prediction System (G-CAPS) for Engineering Students

The decisions regarding a prospective career choice and the paths leading to it are life-changing actions for any student. Engineering students particularly have plenty of career opportunities to choose from after their graduation or even during campus placements. The wide gamut of opportunities may sometimes cause engineering students to end up in career paths that do not match their aptitudes, skills, and personality traits. Developing efficient career prediction and guidance systems exclusively for engineering students is a pressing priority. However, there is a scarcity of research studies on automated career prediction systems for engineering education settings. Against this backdrop, we propose a novel solution rooted in artificial intelligence titled Graphology-based Career Analysis and Prediction System (G-CAPS). Advanced graphology tools are employed to connect handwriting features with the personality traits of individual students. The Holland theory of vocational interests is adopted in G-CAPS to characterize and model individual career interests. Existing literature indicates that no such graphology-based prediction system was developed based on vocational personality traits. The G-CAPS model can be trained and tested using the handwriting samples collected from engineering students and working professionals with engineering degrees. Distinct handwriting features are captured and processed utilizing an array of Convolutional Neural networks (CNN). The system architecture development of the model and its working process is particularised in the paper. It is anticipated that the G-CAPS model can soundly address the career path selection issues of engineering students and graduates looking for a job. The innovative prediction system can be scaled to assist engineering students and graduates across the globe in selecting potential career paths most suitable for their specific character traits.