Design and Development of an AI-Assisted Personalized Case Teaching Model Based on Fuzzy Delphi and ISM Approaches

Abstract

This study design and developed an AI-Assisted Personalized Case Teaching Model (AI-PCT) that integrates the Fuzzy Delphi Method (FDM) and Interpretive Structural Modeling (ISM) to explore how artificial intelligence (AI) can enhance case-based learning in higher education. A panel of 15 experts identified and validated 22 key elements across five domains: pedagogical support, assessment and feedback, technical functionality, teacher and institutional control, and user experience. The ISM analysis revealed a five-level hierarchical structure, illustrating the progression from technological foundations to learner experience outcomes. The findings highlight that AI integration must balance personalization, authenticity, and ethical oversight, ensuring that AI serves as a cognitive partner rather than a substitute for educators. The AI-PCT model contributes both a validated theoretical framework and a practical pathway for embedding AI into business and management education. This model bridges the gap between theory and practice and facilitates the transition to student-centered classrooms.