Digitization in teacher education—quality enhancement, status quo, and professionalization approaches

This special issue brings together a diverse range of research that reflects the multifaceted landscape within the realm of digitization in teacher education. The contributions in this special issue range from underscoring how machine learning is not just an analytical tool but a beacon for identifying systemic challenges, i.e., dropouts and its impacts, and informing policy decisions within teacher education. They extend to contributions that bring to light the critical evaluation tools now at our disposal, which diagnose pressing issues in teacher education—such as the stagnation in digital competencies among (pre-service) teachers and their underestimation of digital media’s potential. The findings of the present contributions also highlight a crucial point: the need to reshape curricula to embed digital competencies as fundamental pillars of teacher education. Moreover, they reveal the effectiveness of innovative strategies such as reverse mentoring, collaborative peer learning, and leveraging educator role models as catalysts for change. Together, this special issue acts as a compass, directing the course of teacher education to a tech-savvy and responsive future. © 2024, Die Herausgeber und Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.