A model for enhancing career selection systems: a case of Ugandan secondary school students

Abstract

Career selection in developing countries is often guided by traditional methods such as teacher- led sessions and examination results yet these approaches fail to account for the complex and multi-dimensional factors influencing career choices. As a result, many students make decisions based on guesswork, peer pressure or parental influence, leading to career mismatches and wasted potential. Although ICT-based career selection tools have been of help, they remain generic and overlook Uganda’s socio-economic and cultural realities. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have strengthened predictive capabilities in career selection systems; however, these technologies rely on limited variables and are largely designed for developed contexts. This study therefore aimed to develop a model that enhances the performance of the existing Career Selection Systems for secondary school students in Uganda. Guided by Social Cognitive Career Theory (SCCT) and Holland’s Vocational theory, the model integrates socio-behavioural and contextual variables to enhance the variables of the existing career selection systems to reflect the realities of Uganda. The study adopted pragmatism research paradigm and employed Design Science Research strategy with both quantitative and qualitative data collected from secondary school and university students and career guidance stakeholders. Data were analysed using SPSS, NVivo, and PLS- SEM. Eight key SCCT variables were identified for the model: Contextual Influences, Contextual Affordances, Learning Experiences, Career Goals, Outcome Expectations, Self- Efficacy Expectations, Personality, and Career Decision-Making. The model demonstrated good statistical fit, with 3.2% of variance in enhancing career selection. Additional qualitative insights highlighted five additional factors for model improvement. Model evaluation showed strong support, with expert walkthroughs yielding a 79.3% satisfaction rate and prototype testing with 51 users recording an 80% satisfaction rate. These results demonstrate that the model is both robust and adaptable, making it suitable for integration into technology-supported career selection systems, including AI- and ML-based platforms in Uganda and similar contexts. Future work should include a longitudinal study to assess the long-term impact of the model on students’ career trajectories from school to employment, alongside the development of a mobile version of the prototype to enhance accessibility and usability. Additionally, integrating the model into existing career selection systems, particularly AI- and ML-based platforms, is recommended to improve system performance and support wider adoption.