Trainee-Related Determinants of Incorporation of Computer-Aided Production Techniques in Training of Mechanical Engineering Technicians in TVET Institutions within Uasin Gishu County, Kenya

Abstract

Although Technical and Vocational Education and Training (TVET) system in Kenya aims to produce technically competent mechanical engineering technicians, many institutions in counties struggle to integrate computer-aided production techniques in training. These challenges reduce the employability of TVET graduates, making Kenya less competitive in sectors reliant on digital manufacturing. Given that Uasin Gishu County is an emerging industrial hub, the future technicians trained there require advanced skills. Consequently, this study sought to identify the specific trainee-related determinants influencing the incorporation of computer-aided production (CAP) techniques in mechanical technician training, analyzing factors such as trainee exposure and training, perception and attitude, desire to learn, learning preferences, personal ambitions, and self-determination. This study was anchored on William Theodore’s human capital theory and adopted a descriptive research design. The research was conducted across accredited mechanical engineering programs in TVET institutions within Uasin Gishu County. The target population consisted of 128 trainees from seven institutions, with participants selected through random sampling. Data collection utilized a structured questionnaire that underwent pilot testing to establish validity through expert review and to ensure reliability. Data was analyzed using descriptive statistics and presented in tables and charts. Findings showed that trainee-related factors significantly influenced the adoption of computer-aided techniques. Majority of trainees reported receiving relevant training (n=35 agreed; n=34 strongly agreed) and learning guided by modern technology (n=50 agreed; n=30 strongly agreed). A majority perceived these techniques as essential for their careers (n=42 agreed; n=57 strongly agreed) and expressed strong motivation to learn (n=43 agreed; n=60 strongly agreed). Many linked their ambitions to mastering these skills (n=48 agreed; n=37 strongly agreed) and demonstrated self-determination (n=53 agreed; n=52 strongly agreed). However, respondents cited key constraints including inadequate staff (n=40 agreed; n=44 strongly agreed), obsolete equipment (n=46 agreed; n=56 strongly agreed), and exam-oriented curricula (n=26 agreed; n=69 strongly agreed). The study concludes that trainee-related factors are strong enablers of digital adoption, but institutional and structural challenges remain the critical bottlenecks in achieving comprehensive digital competence among mechanical technician trainees in Kenya. The study recommends revising the TVET curriculum to include compulsory computer-aided production modules, upgrading institutional digital infrastructure, enhancing trainers’ continuous professional development, and strengthening industry partnerships for internships and mentorship to improve trainees’ practical exposure and employability.