Designing a precision career-guidance model based on student psychological profiling in higher education.

In recent years, the importance of precision career guidance in higher education has become increasingly evident. As the job market evolves rapidly, students face the challenge of aligning their educational paths with career opportunities that match their skills and interests (). Not only does effective career guidance enhance student satisfaction and success, but it also contributes to the efficient allocation of educational resources and the development of a skilled workforce (). Moreover, understanding the psychological profiles of students can provide deeper insights into their career preferences and potential, enabling more personalized and effective guidance (). Therefore, developing a precision career-guidance model that incorporates psychological profiling is not only necessary but also timely, as it addresses the growing demand for tailored educational and career planning solutions (). Early efforts to automate career guidance systems focused on creating structured frameworks that could simulate human decision-making processes. These systems aimed to provide logical and consistent career advice by encoding domain knowledge into structured formats (). However, the rigidity of these frameworks often led to challenges in adapting to the nuanced and dynamic nature of individual student profiles (). The lack of flexibility and the need for extensive manual input limited the scalability and personalization of these systems (). As a result, while these initial approaches laid the groundwork for automated career guidance, their limitations necessitated the exploration of more adaptive methods. In response to the limitations of early frameworks, the introduction of algorithms capable of learning from data marked a significant shift in career guidance models. These approaches utilized large datasets to identify patterns and correlations between student characteristics and career outcomes (). By employing techniques such as decision trees and clustering, these models offered improved adaptability and predictive accuracy. The ability to learn from data allowed these models to provide more personalized recommendations based on historical trends and individual profiles (). However, despite their enhanced performance, these models often struggled with the interpretability of results and required substantial amounts of labeled data for training, which could be a barrier in diverse educational settings ().