Digital Twins in Education Applications, Challenges, and Future Directions

Digital Twins (DTs) are transforming education by creating immersive, data-driven learning environments that integrate theoretical knowledge and practical application. In the current educational landscape, traditional teaching methods often struggle to engage diverse learners, adapt to individual needs, and provide hands-on experiences. While existing literature highlights DT potential in specific domains like engineering, comprehensive analyses of recent advancements and cross-disciplinary applications remain scarce. This systematic literature review addresses this gap by analyzing 25 peer-reviewed studies, identifying the current applications and implementation approaches of DTs in educational settings, their distribution across different educational disciplines, and emerging trends in educational DT development. Our analysis reveals four dominant trends: (1) AI-driven cognitive modeling for personalized learning, (2) immersive technology integration, (3) gamification mechanisms, and (4) cross-domain convergence. Researchers are leveraging these trends to address critical challenges, such as enhancing student engagement, enabling adaptive learning, and optimizing campus operations. However, critical gaps persist, including limited scalability in non-technical fields, insufficient longitudinal validation, and heterogeneous data integration challenges. Future research should prioritize empirical validation, scalability across diverse educational contexts, and ethical frameworks to ensure inclusive and sustainable adoption of DTs in education.