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

Shodmonov Davronjon AbduvaliyevichSamarkand Branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi

Zenodo (CERN European Organization for Nuclear Research)repository2026

ABI

Annotatsiya

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.

Mavzular

Intelligent Tutoring Systems and Adaptive Learning AI-based Problem Solving and Planning Online Learning and Analytics

Identifikatorlar

DOI: 10.5281/zenodo.20320233

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