A CASE-BASED REASONING FRAMEWORK WITH SOFT SIMILARITY AND SUBMODULAR OPTIMIZATION FOR PERSONALIZED CAREER GUIDANCE (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2025)
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.The proposed approach models each graduate as a structured case comprising academic profiles, temporal learning trajectories, contextual attributes, observed outcomes, and contributing actions. A hybrid similarity function is employed to retrieve relevant cases from heterogeneous educational data, while a soft-retrieve aggregation mechanism ensures robustness under partial observability and noisy inputs. To bridge the gap between relevance estimation and actionable guidance, the reuse phase is formulated as a constrained submodular optimization problem, enabling the selection of compact and feasible action sets with theoretical approximation guarantees.Theoretical analysis establishes stability of the soft-retrieve mechanism, probabilistic consistency of outcome inference, and near-optimality of the reuse strategy. Experimental evaluation on real institutional data collected through a higher education management information system demonstrates that the proposed framework consistently outperforms classical CBR and baseline machine learning approaches in outcome prediction. Moreover, submodular reuse optimization significantly improves action recommendation quality by increasing competency coverage and reducing redundancy, particularly in low-cardinality settings that are most relevant for practical advising.The results indicate that combining case-based reasoning with submodular optimization offers a robust, explainable, and deployable solution for personalized career guidance in higher education.
Keywords: Case-Based Reasoning; Conversational CBR; Career Guidance; Hybrid Similarity; Submodular Optimization; Educational Data Mining; Explainable AI