This study addresses the pressing challenges in higher education—rising tuition costs, unsustainable student debt, and the growing disconnect between theoretical instruction and practical through the integration of artificial intelligence (AI) and digital twin technology within a Design Science Research (DSR) framework. By leveraging AI-driven agents and the concept of a “student digital twin,” the proposed system creates a dynamic, personalized learning environment. Central to the system is a comprehensive digital twin for each student that captures real-time data on learning styles, performance, and academic objectives. Despite challenges such as data privacy, technical complexity, and algorithmic bias, the iterative DSR approach offers a robust methodology for refining and scaling the system. Future research directions include empirical validation, the development of privacy-preserving mechanisms, and the integration of complementary technologies to foster a more accessible, responsive, and dynamic higher education landscape.