Privacy-Preserving and Explainable Hybrid Learning Framework for Career Guidance Using Psychometric Indicators: A Synthetic-Data-Driven Study

Abstract

Data-driven modeling has been a key factor in developing career guidance systems by matching students' attributes with changing occupational categories. However, centralized machine learning approaches have raised concerns about privacy of the user's data, fairness of the results and interpretability of the models. In this study, a hybrid framework for privacy-preserving and explainable learning is presented. The proposed Privacy-Preserving and Explainable Hybrid Learning Framework (PX-CGF) integrates both psychometric indicators and skill-based profiles as well as interpretable reasoning techniques through federated learning. To evaluate this new framework, a synthetic dataset was created with the psychometric traits, academic indicators and occupational fit categories. Evaluation experiments showed that PX-CGF resulted in greater accuracy compared to baseline models, as well as increased fairness and interpretability. Several tables were created to illustrate the dataset used, comparisons of the models evaluated, and the metrics used to measure these evaluations, which demonstrate the professional structure of the proposed framework.

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