A CASE-BASED REASONING FRAMEWORK WITH SOFT SIMILARITY AND SUBMODULAR OPTIMIZATION FOR PERSONALIZED CAREER GUIDANCE

The increasing availability of longitudinal academic data in higher education creates new opportunities for personalized and evidence-based career guidance. However, many existing AI-driven guidance systems rely on black-box predictive models and provide limited interpretability or actionable recommendations. This paper proposes a principled Case-Based Reasoning (CBR) framework for personalized career guidance that leverages institutional academic data and historical student trajectories.

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