AI-Based Digital Twins of Students: A New Paradigm for Competency-Oriented Learning Transformation

Universities face growing pressure to deliver personalized learning that prepares students with adaptable, future-ready competencies. Traditional static curricula are often unable to meet these demands. This paper introduces a novel framework based on AI-enhanced digital twins of students (DTS) as dynamic virtual representations integrating academic performance, competency attainment, learning preferences, career objectives, and engagement patterns. The DTS framework employs artificial intelligence algorithms, semantic ontologies spanning educational and career domains, and real-time feedback mechanisms for personalized learning pathway orchestration. To demonstrate the framework’s potential, a simulation study was conducted using synthetic student data. Results compared DTS-guided adaptive pathways with traditional static approaches and showed improvements in competency attainment, engagement, learning efficiency, and reduced dropout risk.